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Energies, Volume 12, Issue 20 (October-2 2019) – 217 articles

Cover Story (view full-size image): Precise determination of electric field potentials around earthing systems can help us to understand the earthing system’s design safety and economy. With the help of complex optimisation methods, it is possible to search for the most efficient earthing system designs within specific constraints. More on the conducted study of touch voltage optimisation is presented in an article On Minimisation of Earthing System Touch Voltages. View this paper.
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25 pages, 5614 KiB  
Article
Numerical Investigation of the Aerodynamic Characteristics and Attitude Stability of a Bio-Inspired Corrugated Airfoil for MAV or UAV Applications
by Hui Tang, Yulong Lei, Xingzhong Li and Yao Fu
Energies 2019, 12(20), 4021; https://doi.org/10.3390/en12204021 - 22 Oct 2019
Cited by 11 | Viewed by 4765
Abstract
In this study, two-dimensional (2D) and three-dimensional (3D) numerical calculations were conducted to investigate the aerodynamic characteristics, especially the unsteady aerodynamic characteristics and attitude stability of a bio-inspired corrugated airfoil compared with a smooth-surfaced airfoil (NACA2408 airfoil) at the chord Reynolds number of [...] Read more.
In this study, two-dimensional (2D) and three-dimensional (3D) numerical calculations were conducted to investigate the aerodynamic characteristics, especially the unsteady aerodynamic characteristics and attitude stability of a bio-inspired corrugated airfoil compared with a smooth-surfaced airfoil (NACA2408 airfoil) at the chord Reynolds number of 4000 to explore the potential applications of non-traditional, corrugated dragonfly airfoils for micro air vehicles (MAVs) or micro-sized unmanned aerial vehicles (UAVs) designs. Two problem settings were applied to our numerical calculations. First, the airfoil was fixed at a constant angle of attack to analyze the aerodynamic characteristics and the hydrodynamic moment. Second, the angle of attack of airfoils was passively changed by the fluid force to analyze the attitude stability. The current numerical solver for the flow field around an unsteady rotating airfoil was validated against the published numerical data. It was confirmed that the corrugated airfoil performs (in terms of the lift-to-drag ratio) much better than the profiled NACA2408 airfoil at low Reynolds number R e = 4000 in low angle of attack range of 0 6 , and performs as well at the angle of attack of 6 or more. At these low angles of attack, the corrugated airfoil experiences an increase in the pressure drag and decrease in shear drag due to recirculation zones inside the cavities formed by the pleats. Furthermore, the increase in the lift for the corrugated airfoil is due to the negative pressure produced at the valleys. It was found that the lift and drag in the 2D numerical calculation are strong fluctuating at a high angle of attacks. However, in 3D simulation, especially for a 3D corrugated airfoil with unevenness in the spanwise direction, smaller fluctuations and the smaller average value in the lift and drag were obtained than the results in 2D calculations. It was found that a 3D wing with irregularities in the spanwise direction could promote three-dimensional flow and can suppress lift fluctuations even at high angles of attack. For the attitude stability, the corrugated airfoil is statically more unstable near the angle of attack of 0 , has a narrower static stable range of the angle of attack, and has a larger amplitude of fluctuations of the angle of attack compared with the profiled NACA2408 airfoil. Based on the Routh–Hurwitz stability criterion, it was confirmed that the control systems of the angle of attack passively changed by the fluid force for both two airfoils are unstable systems. Full article
(This article belongs to the Special Issue Modelling of Aerospace Vehicle Dynamics)
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Figure 1

Figure 1
<p>Profile of corrugated airfoil.</p>
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<p>Profile of NACA2408 airfoil.</p>
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<p>Computational domain and boundary conditions: (<b>a</b>) two dimensional; and (<b>b</b>) three dimensional.</p>
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<p>Computational grid: (<b>a</b>) corrugated airfoil; and (<b>b</b>) around leading edge.</p>
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<p>Parameters for the airfoil movements in the case of validation.</p>
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<p>Computational domain and boundary condition of the validation case.</p>
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<p>Periodic variation of the thrust coefficient of a heaving and pitching NACA0010 airfoil.</p>
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<p>Periodic variation of the thrust coefficient of a heaving and pitching NACA0010 airfoil.</p>
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<p>Mean streamlines around a corrugated airfoil at angle of attack of <math display="inline"><semantics> <msup> <mn>2</mn> <mo>∘</mo> </msup> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>R</mi> <mi>e</mi> <mo>=</mo> <mn>4000</mn> </mrow> </semantics></math>.</p>
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<p>Mean pressure distribution of a corrugated airfoil at angle of attack of <math display="inline"><semantics> <msup> <mn>2</mn> <mo>∘</mo> </msup> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>R</mi> <mi>e</mi> <mo>=</mo> <mn>4000</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 11
<p>The mean lift and drag coefficient of the two dimensional corrugated airfoil and the profiled NACA airfoil at <span class="html-italic">R<sub>e</sub></span> = 4000 with varying angle of attack: (<b>a</b>) lift coefficient; and (<b>b</b>) drag coefficient.</p>
Full article ">Figure 12
<p>Instantaneous velocity field around NACA2408 airfoil at angle of attack of <math display="inline"><semantics> <msup> <mn>8</mn> <mo>∘</mo> </msup> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>R</mi> <mi>e</mi> <mo>=</mo> <mn>4000</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 13
<p>Instantaneous vorticity contours around NACA2408 airfoil at angle of attack of <math display="inline"><semantics> <msup> <mn>8</mn> <mo>∘</mo> </msup> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>R</mi> <mi>e</mi> <mo>=</mo> <mn>4000</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 14
<p>Time history of lift and drag coefficient of 2D and 3D calculations for the corrugated airfoil at <span class="html-italic">R<sub>e</sub></span> = 4000 with angle of attack of 8°: (<b>a</b>) lift coefficient; and (<b>b</b>) drag coefficient.</p>
Full article ">Figure 15
<p>Instantaneous velocity field on the upper surface of the corrugated airfoil at angle of attack of 8°, <span class="html-italic">R<sub>e</sub></span> = 4000: (<b>a</b>) top view; and (<b>b</b>) front view.</p>
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<p>The averaged lift and drag coefficient of 2D and 3D calculations for the corrugated airfoil and NACA2408 airfoil at <span class="html-italic">R<sub>e</sub></span> = 4000 with varying angle of attack: (<b>a</b>) lift coefficient; and (<b>b</b>) drag coefficient.</p>
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<p>The mean lift-to-drag ratio of the 3D calculation for the corrugated airfoil and the 3D profiled NACA2408 airfoil at <math display="inline"><semantics> <mrow> <mi>R</mi> <mi>e</mi> <mo>=</mo> <mn>4000</mn> </mrow> </semantics></math> with varying angle of attack.</p>
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<p>A three-dimensional corrugated wing shape.</p>
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<p>Instantaneous velocity field on the upper surface of the three dimensional corrugated wing at angle of attack of <math display="inline"><semantics> <msup> <mn>2</mn> <mo>∘</mo> </msup> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>R</mi> <mi>e</mi> <mo>=</mo> <mn>4000</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 20
<p>Time history of lift coefficient of 3D calculations for the 2D and 3D corrugated airfoil at <span class="html-italic">R<sub>e</sub></span> = 2000 and 4000 with angle of attack of 8°: (<b>a</b>) <span class="html-italic">R<sub>e</sub></span> = 2000; and (<b>b</b>) <span class="html-italic">R<sub>e</sub></span> = 4000.</p>
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<p>The mean static moment coefficient of the corrugated airfoil and the profiled NACA2408 airfoil at <math display="inline"><semantics> <mrow> <mi>R</mi> <mi>e</mi> <mo>=</mo> <mn>4000</mn> </mrow> </semantics></math> with varying angle of attack.</p>
Full article ">Figure 22
<p>Time evolution of angle of attack and pitching relative moment coefficient of fluid force for the corrugated airfoil and NACA2408 airfoil at <math display="inline"><semantics> <mrow> <mi>R</mi> <mi>e</mi> <mo>=</mo> <mn>4000</mn> </mrow> </semantics></math>, with initial angle of attack of <math display="inline"><semantics> <msup> <mn>0</mn> <mo>∘</mo> </msup> </semantics></math> (<math display="inline"><semantics> <mrow> <msub> <mi>r</mi> <mrow> <mi>ρ</mi> <mi>C</mi> </mrow> </msub> <mo>=</mo> <mn>3.0</mn> <mo>×</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>3</mn> </mrow> </msup> </mrow> </semantics></math>).</p>
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<p>Attitude and rotation speed of an airfoil.</p>
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<p>Geometric relationship between velocity and angle of attitude and attack.</p>
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27 pages, 6801 KiB  
Article
A Process for Defining Prototype Building Models: Courthouse Case Study for U.S. Commercial Energy
by Mini Malhotra, Piljae Im and Joshua New
Energies 2019, 12(20), 4020; https://doi.org/10.3390/en12204020 - 22 Oct 2019
Cited by 2 | Viewed by 5509
Abstract
Buildings currently consume 36% of the world’s energy and contribute nearly 40% of CO2 emissions. Many countries desire to generate virtual models of their nation’s buildings in order to coordinate research activities and inform market opportunities for a more sustainable built environment. [...] Read more.
Buildings currently consume 36% of the world’s energy and contribute nearly 40% of CO2 emissions. Many countries desire to generate virtual models of their nation’s buildings in order to coordinate research activities and inform market opportunities for a more sustainable built environment. The United States Department of Energy uses a suite of Commercial Prototype Building Models, which currently includes 16 building types and covers 80% of US commercial floorspace. Efforts are underway to expand this suite by developing prototype models for additional building types. In this paper, we outline a systematic approach to defining the building, collecting relevant information and creating a flexible model while doing so in the pragmatic context of a courthouse building. Informed by building design guides, databases, documented projects and inputs from courthouse design experts, we define a small, 69,324 ft2 (6440 m2), four-courtroom, low-rise courthouse as the prototype to represent an average-size courthouse in the US. We present building characteristics relevant for energy model development and provide the rationale for their selection. These details combined with climate- and construction-vintage-specific requirements for the building envelope and systems from building standards will be used for developing the courthouse model for the Commercial Prototype Building Models suite. The comprehensive information presented will also guide model modification to capture the dynamics of smaller or larger courthouses more accurately for building or system size-specific research. Full article
Show Figures

Figure 1

Figure 1
<p>Workflow of prototype model development (context: Courthouse building).</p>
Full article ">Figure 2
<p>Courthouses in the US commercial building stock [<a href="#B41-energies-12-04020" class="html-bibr">41</a>]. Courthouses account for 0.5% of US commercial floorspace (<b>a</b>); 0.6% of fuel consumption in US commercial buildings (<b>b</b>); are dissimilar to most other buildings in terms of average floor area but comparable in terms of average fuel consumption intensity (<b>c</b>) and have a narrower range of variation of fuel consumption intensity compared to most other building types (<b>d</b>).</p>
Full article ">Figure 3
<p>The US court system.</p>
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<p>Building efficiency ratio in federal and state courthouses [<a href="#B36-energies-12-04020" class="html-bibr">36</a>,<a href="#B37-energies-12-04020" class="html-bibr">37</a>,<a href="#B38-energies-12-04020" class="html-bibr">38</a>].</p>
Full article ">Figure 5
<p>Percentage frequency of (<b>a</b>) number of courtrooms and (<b>b</b>) the ratio of gross square feet to number of courtrooms in federal and state courthouses [<a href="#B36-energies-12-04020" class="html-bibr">36</a>,<a href="#B37-energies-12-04020" class="html-bibr">37</a>,<a href="#B38-energies-12-04020" class="html-bibr">38</a>].</p>
Full article ">Figure 5 Cont.
<p>Percentage frequency of (<b>a</b>) number of courtrooms and (<b>b</b>) the ratio of gross square feet to number of courtrooms in federal and state courthouses [<a href="#B36-energies-12-04020" class="html-bibr">36</a>,<a href="#B37-energies-12-04020" class="html-bibr">37</a>,<a href="#B38-energies-12-04020" class="html-bibr">38</a>].</p>
Full article ">Figure 6
<p>Three-part circulation system in a courthouse shown in section (<b>a</b>) and court-floor plan (<b>b</b>) [<a href="#B25-energies-12-04020" class="html-bibr">25</a>].</p>
Full article ">Figure 7
<p>Typical stacking scheme (<b>a</b>) and court-floor blocking scheme (<b>b</b>) in a courthouse [<a href="#B24-energies-12-04020" class="html-bibr">24</a>].</p>
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<p>Example floor plan for courtroom floors of low-rise, mid-rise and high-rise federal courthouses [<a href="#B34-energies-12-04020" class="html-bibr">34</a>].</p>
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<p>Example space area distribution in low-rise, mid-rise and high-rise federal courthouses [<a href="#B34-energies-12-04020" class="html-bibr">34</a>].</p>
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<p>Courthouse construction characteristics [<a href="#B41-energies-12-04020" class="html-bibr">41</a>]. ‘Brick, stone or stucco’ on stud-walls is the predominant wall construction (<b>a</b>) and ‘plastic, rubber or synthetic sheeting’ and ‘built-up roofing’ are the common roof construction (<b>b</b>).</p>
Full article ">Figure 11
<p>Hours of operation in (<b>a</b>) federal courthouses [<a href="#B40-energies-12-04020" class="html-bibr">40</a>] and (<b>b</b>) sample courthouses in the 2012 Commercial Building Energy Consumption Survey (CBECS) data [<a href="#B41-energies-12-04020" class="html-bibr">41</a>].</p>
Full article ">Figure 12
<p>Courthouse floor area in (<b>a</b>) the 2012 CBECS data [<a href="#B41-energies-12-04020" class="html-bibr">41</a>] and (<b>b</b>) the General Services Administration (GSA) Portfolio Data [<a href="#B39-energies-12-04020" class="html-bibr">39</a>].</p>
Full article ">Figure 13
<p>Building shape statistics for (<b>a</b>) courthouses in the 2012 CBECS data [<a href="#B41-energies-12-04020" class="html-bibr">41</a>] and (<b>b</b>) ‘Public Order and Safety’ buildings in the 1992 CBECS data [<a href="#B48-energies-12-04020" class="html-bibr">48</a>].</p>
Full article ">Figure 14
<p>Number of floors in courthouses [<a href="#B41-energies-12-04020" class="html-bibr">41</a>] (<b>a</b>), and number of elevators versus building square footage and number of floors (<b>b</b>).</p>
Full article ">Figure 14 Cont.
<p>Number of floors in courthouses [<a href="#B41-energies-12-04020" class="html-bibr">41</a>] (<b>a</b>), and number of elevators versus building square footage and number of floors (<b>b</b>).</p>
Full article ">Figure 15
<p>Percent exterior glass in courthouses [<a href="#B41-energies-12-04020" class="html-bibr">41</a>].</p>
Full article ">Figure 16
<p>Heating ventilation and air conditioning (HVAC) system characteristics in courthouses [<a href="#B41-energies-12-04020" class="html-bibr">41</a>] by heating source (<b>a</b>); type of heating equipment (<b>b</b>); type of cooling system (<b>c</b>); and type of ventilation system (<b>d</b>).</p>
Full article ">Figure 17
<p>Prototype courthouse layout of basement (<b>bottom</b>), first floor (<b>middle</b>) and second floor (<b>top</b>).</p>
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<p>Prototype courthouse space area distribution.</p>
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<p>Prototype courthouse space-specific occupancy schedules.</p>
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<p>Prototype courthouse building massing and window placement.</p>
Full article ">
40 pages, 13345 KiB  
Article
New Interface for Assessing Wellbore Stability at Critical Mud Pressures and Various Failure Criteria: Including Stress Trajectories and Deviatoric Stress Distributions
by Jihoon Wang and Ruud Weijermars
Energies 2019, 12(20), 4019; https://doi.org/10.3390/en12204019 - 22 Oct 2019
Cited by 8 | Viewed by 3553
Abstract
This study presents a new interface for wellbore stability analysis, which visualizes and quantifies the stress condition around a wellbore at shear and tensile failure. In the first part of this study, the Mohr–Coulomb, Mogi–Coulomb, modified Lade and Drucker–Prager shear failure criteria, and [...] Read more.
This study presents a new interface for wellbore stability analysis, which visualizes and quantifies the stress condition around a wellbore at shear and tensile failure. In the first part of this study, the Mohr–Coulomb, Mogi–Coulomb, modified Lade and Drucker–Prager shear failure criteria, and a tensile failure criterion, are applied to compare the differences in the critical wellbore pressure for three basin types with Andersonian stress states. Using traditional wellbore stability window plots, the Mohr–Coulomb criterion consistently gives the narrowest safe mud weight window, while the Drucker–Prager criterion yields the widest window. In the second part of this study, a new type of plot is introduced where the safe drilling window specifies the local magnitude and trajectories of the principal deviatoric stresses for the shear and tensile wellbore failure bounds, as determined by dimensionless variables, the Frac number ( F ) and the Bi-axial Stress scalar ( χ ), in combination with failure criteria. The influence of both stress and fracture cages increases with the magnitude of the F values, but reduces with depth. The extensional basin case is more prone to potential wellbore instability induced by circumferential fracture propagation, because fracture cages persists at greater depths than for the compressional and strike-slip basin cases. Full article
(This article belongs to the Special Issue Petroleum Geomechanics)
Show Figures

Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>(<b>a</b>) Failure surfaces in <math display="inline"><semantics> <mi>π</mi> </semantics></math>-plane calculated by the Drucker–Prager and Mohr–Coulomb failure criteria. The Drucker–Prager constants were determined with the assumptions that the failure surface circumscribes (green), middle circumscribes (yellow) or inscribes (blue) the failure surface of the Mohr–Coulomb (red) criterion. Internal friction angle is 30°. (<b>b</b>) Failure surfaces in <math display="inline"><semantics> <mrow> <mi>π</mi> </mrow> </semantics></math>-plane calculated by the Drucker–Prager (circumscribing case, green), modified Lade (yellow), Mogi–Coulomb (blue), and Mohr–Coulomb (red) failure criteria. The dashed black circle represents the stress state where the maximum and intermediate principal stresses are identical. Internal friction angle is 30°.</p>
Full article ">Figure 2
<p>Reverse faulting regime - compressional basin. Critical wellbore pressure at lower- (colored dashed), upper-bound shear (colored solid), and tensile (TF; black solid) failures under the reverse faulting regime with the vertical, maximum and minimum horizontal stress gradients of 22.6, 29.4, and 24.9 kPa/m, respectively. The pore pressure gradient is 10.2 kPa/m (black dashed). The wellbore pressures were calculated by the Mohr–Coulomb (MC; red), Mogi–Coulomb (MG; blue), modified Lade (ML; yellow), and Drucker–Prager (DP; green) failure criteria. The vertical in-situ stress is depicted with the dash-dotted black line. The magnified frame indicates that the calculated lower critical wellbore pressures exceed the pore pressure at certain depths. Input parameters are given in <a href="#energies-12-04019-t002" class="html-table">Table 2</a> and <a href="#energies-12-04019-t003" class="html-table">Table 3</a>.</p>
Full article ">Figure 3
<p>Reverse faulting regime—compressional basin. Locally induced principal stresses at a moment of (<b>a</b>) lower-bound and (<b>b</b>) upper-bound shear failure under the reverse faulting regime with the vertical, maximum and minimum horizontal stresses of 22.6, 29.4, and 24.9 kPa/m, respectively. The black solid lines represent the axial stress (<math display="inline"><semantics> <mrow> <mo> </mo> <msub> <mi>σ</mi> <mi>z</mi> </msub> <mo stretchy="false">)</mo> </mrow> </semantics></math>. The tangential (<math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mi>θ</mi> </msub> <mo> </mo> <mo> </mo> </mrow> </semantics></math>; solid) and radial (<math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mi>r</mi> </msub> </mrow> </semantics></math>; dashed) stresses are calculated by the Mohr–Coulomb (MC; red), Mogi–Coulomb (MG; blue), modified Lade (ML; yellow), and Drucker–Prager (DP; green) failure criteria.</p>
Full article ">Figure 4
<p>Strike-slip regime—strike-slip basin. Critical wellbore pressure at lower- (colored dashed), upper-bound shear (colored solid), and tensile (TF; black solid) failures under the strike-slip regime with the vertical, maximum and minimum horizontal stress gradients of 22.6, 24.9, and 20.4 kPa/m, respectively. The pore pressure gradient is 10.2 kPa/m (black dashed). The wellbore pressures were calculated by the Mohr–Coulomb (MC; red), Mogi–Coulomb (MG; blue), modified Lade (ML; yellow), and Drucker–Prager (DP; green) failure criteria. The minimum horizontal in-situ stress is depicted with the dash-dotted black line. The magnified frame indicates that the calculated lower critical wellbore pressures exceed the pore pressure at certain depths.</p>
Full article ">Figure 5
<p>Strike-slip regime—strike-slip basin. Locally induced principal stresses at a moment of (<b>a</b>) lower-bound and (<b>b</b>) upper-bound shear failure under the strike-slip regime with the vertical, maximum and minimum horizontal stresses of 22.6, 24.9, and 20.4 kPa/m, respectively. The black solid lines represent the axial stress (<math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mi>z</mi> </msub> <mo stretchy="false">)</mo> </mrow> </semantics></math>. The tangential (<math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mi>θ</mi> </msub> </mrow> </semantics></math>; solid) and radial (<math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mi>r</mi> </msub> </mrow> </semantics></math>; dashed) stresses are calculated by the Mohr–Coulomb (MC; red), Mogi–Coulomb (MG; blue), modified Lade (ML; yellow), and Drucker–Prager (DP; green) failure criteria.</p>
Full article ">Figure 5 Cont.
<p>Strike-slip regime—strike-slip basin. Locally induced principal stresses at a moment of (<b>a</b>) lower-bound and (<b>b</b>) upper-bound shear failure under the strike-slip regime with the vertical, maximum and minimum horizontal stresses of 22.6, 24.9, and 20.4 kPa/m, respectively. The black solid lines represent the axial stress (<math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mi>z</mi> </msub> <mo stretchy="false">)</mo> </mrow> </semantics></math>. The tangential (<math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mi>θ</mi> </msub> </mrow> </semantics></math>; solid) and radial (<math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mi>r</mi> </msub> </mrow> </semantics></math>; dashed) stresses are calculated by the Mohr–Coulomb (MC; red), Mogi–Coulomb (MG; blue), modified Lade (ML; yellow), and Drucker–Prager (DP; green) failure criteria.</p>
Full article ">Figure 6
<p>Normal faulting regime—extensional basin. Critical wellbore pressure at lower- (colored dashed), upper-bound shear (colored solid), and tensile (TF; black solid) failures under the normal faulting case with the vertical, maximum and minimum horizontal stress gradients of 22.6, 20.4, and 15.8 kPa/m, respectively. The pore pressure gradient is 10.2 kPa/m (black dashed). The wellbore pressures were calculated by the Mohr–Coulomb (MC; red), Mogi–Coulomb (MG; blue), modified Lade (ML; yellow), and Drucker–Prager (DP; green) failure criteria. The minimum horizontal in-situ stress is depicted with the dash-dotted black line. The magnified frame (<b>a</b>) indicates that the calculated lower critical wellbore pressures exceed the pore pressure at certain depths. The magnified frame (<b>b</b>) shows the alteration of the upper critical wellbore pressure gradient calculated by the Mohr–Coulomb (red) and Mogi–Coulomb (blue) criteria.</p>
Full article ">Figure 7
<p>Normal faulting regime. Locally induced principal stresses at a moment of (<b>a</b>) lower-bound and (<b>b</b>) upper-bound shear failure under the normal faulting regime with the vertical, maximum and minimum horizontal stresses of 22.6, 20.4, and 15.8 kPa/m, respectively. The black solid lines represent the axial stress (<math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mi>z</mi> </msub> <mo stretchy="false">)</mo> </mrow> </semantics></math>. The tangential (<math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mi>θ</mi> </msub> </mrow> </semantics></math>; solid) and radial (<math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mi>r</mi> </msub> </mrow> </semantics></math>; dashed) stresses are calculated by the Mohr–Coulomb (MC; red), Mogi–Coulomb (MG; blue), modified Lade (ML; yellow), and Drucker–Prager (DP; green) failure criteria. The magnified frame in (b) indicate reverse of the principal stress order. (<b>c</b>) Critical wellbore pressure difference at upper-bound shear failure. Difference between the Mohr–Coulomb (MC) and Mogi–Coulomb (MG), and the Mogi–Coulomb and modified Lade (ML) are shown in red and yellow curves, respectively. At 1147 m, the red curve reaches 0 and the yellow curve shows the maximum value.</p>
Full article ">Figure 7 Cont.
<p>Normal faulting regime. Locally induced principal stresses at a moment of (<b>a</b>) lower-bound and (<b>b</b>) upper-bound shear failure under the normal faulting regime with the vertical, maximum and minimum horizontal stresses of 22.6, 20.4, and 15.8 kPa/m, respectively. The black solid lines represent the axial stress (<math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mi>z</mi> </msub> <mo stretchy="false">)</mo> </mrow> </semantics></math>. The tangential (<math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mi>θ</mi> </msub> </mrow> </semantics></math>; solid) and radial (<math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mi>r</mi> </msub> </mrow> </semantics></math>; dashed) stresses are calculated by the Mohr–Coulomb (MC; red), Mogi–Coulomb (MG; blue), modified Lade (ML; yellow), and Drucker–Prager (DP; green) failure criteria. The magnified frame in (b) indicate reverse of the principal stress order. (<b>c</b>) Critical wellbore pressure difference at upper-bound shear failure. Difference between the Mohr–Coulomb (MC) and Mogi–Coulomb (MG), and the Mogi–Coulomb and modified Lade (ML) are shown in red and yellow curves, respectively. At 1147 m, the red curve reaches 0 and the yellow curve shows the maximum value.</p>
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<p>Frac number (<math display="inline"><semantics> <mrow> <mi>F</mi> <mo stretchy="false">)</mo> </mrow> </semantics></math> versus depth at failure. <math display="inline"><semantics> <mi>F</mi> </semantics></math> values at lower-bound (colored dashed) and upper-bound shear failure (colored solid) calculated by the Mohr–Coulomb (MC; red), Mogi–Coulomb (MG; blue), modified Lade (ML; yellow), and Drucker–Prager (DP; green) criteria, for the following cases: (<b>a</b>) Reverse faulting (<math display="inline"><semantics> <mrow> <msub> <mi>χ</mi> <mn>3</mn> </msub> <mo>=</mo> <mn>0.2</mn> <mo stretchy="false">)</mo> <mo>,</mo> </mrow> </semantics></math> (<b>b</b>) strike-slip (<math display="inline"><semantics> <mrow> <msub> <mi>χ</mi> <mn>2</mn> </msub> <mo>=</mo> <mn>1</mn> <mo stretchy="false">)</mo> <mo>,</mo> <mo> </mo> <mo> </mo> </mrow> </semantics></math> and (<b>c</b>) normal faulting (<math display="inline"><semantics> <mrow> <msub> <mi>χ</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>5</mn> <mo stretchy="false">)</mo> <mo>.</mo> </mrow> </semantics></math> F values at tensile failure (TF; assuming zero tensile strength) are depicted with the black solid line.</p>
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<p>Frac number (<math display="inline"><semantics> <mrow> <mi>F</mi> <mo stretchy="false">)</mo> </mrow> </semantics></math> versus depth at failure. <math display="inline"><semantics> <mi>F</mi> </semantics></math> values at lower-bound (colored dashed) and upper-bound shear failure (colored solid) calculated by the Mohr–Coulomb (MC; red), Mogi–Coulomb (MG; blue), modified Lade (ML; yellow), and Drucker–Prager (DP; green) criteria, for the following cases: (<b>a</b>) Reverse faulting (<math display="inline"><semantics> <mrow> <msub> <mi>χ</mi> <mn>3</mn> </msub> <mo>=</mo> <mn>0.2</mn> <mo stretchy="false">)</mo> <mo>,</mo> </mrow> </semantics></math> (<b>b</b>) strike-slip (<math display="inline"><semantics> <mrow> <msub> <mi>χ</mi> <mn>2</mn> </msub> <mo>=</mo> <mn>1</mn> <mo stretchy="false">)</mo> <mo>,</mo> <mo> </mo> <mo> </mo> </mrow> </semantics></math> and (<b>c</b>) normal faulting (<math display="inline"><semantics> <mrow> <msub> <mi>χ</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>5</mn> <mo stretchy="false">)</mo> <mo>.</mo> </mrow> </semantics></math> F values at tensile failure (TF; assuming zero tensile strength) are depicted with the black solid line.</p>
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<p>Dimensionless deviatoric stress magnitudes and trajectories for the compressional basin (reverse faulting) case at upper-bound (left-hand side) and lower-bound shear failure (right-hand side). For the zero tensile strength, tensile failure will occur at <math display="inline"><semantics> <mrow> <mi>F</mi> <mo> </mo> <mo> </mo> <mo> </mo> </mrow> </semantics></math>= 6.6. The color indices above the magnitude plots indicate the local stress intensity is normalized by the larger (compressional) far-field stress (see <a href="#secBdot6-energies-12-04019" class="html-sec">Appendix B.6</a>). The triangles and dashed lines on the magnitude plots of the maximum principal stress at shear failure and of the minimum principal stress at tensile failure show the expected locations of failure. The blue and green curves, and the red dots in the stress trajectory plots represent the maximum and minimum principal stress trajectories, and the neutral points, respectively. The tables beneath the <math display="inline"><semantics> <mi>F</mi> </semantics></math> curves show the depths for each <math display="inline"><semantics> <mi>F</mi> </semantics></math> value calculated by the Mohr–Coulomb (MC), Mogi–Coulomb (MG), modified Lade (ML), and Drucker–Prager (DP) failure criteria.</p>
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<p>Dimensionless deviatoric stress magnitudes and trajectories for the strike-slip basin case at upper-bound (left-hand side) and lower-bound shear failure (right-hand side). For the zero tensile strength, tensile failure will occur at <math display="inline"><semantics> <mrow> <mi>F</mi> <mo> </mo> <mo> </mo> <mo> </mo> <mo> </mo> </mrow> </semantics></math>= 7.0. The color indices above the magnitude plots indicate the local stress intensity is normalized by the larger (compressional) far-field stress (see <a href="#secBdot4-energies-12-04019" class="html-sec">Appendix B.4</a>). The triangles and dashed lines on the magnitude plots of the maximum principal stress at shear failure and of the minimum principal stress at tensile failure show the expected locations of failure. The blue and green curves, and the red dots in the stress trajectory plots represent the maximum and minimum principal stress trajectories, and the neutral points, respectively. The tables beneath the <math display="inline"><semantics> <mi>F</mi> </semantics></math> curves show the depths for each <math display="inline"><semantics> <mi>F</mi> </semantics></math> value calculated by the Mohr–Coulomb (MC), Mogi–Coulomb (MG), modified Lade (ML), and Drucker–Prager (DP) failure criteria.</p>
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<p>Dimensionless deviatoric stress magnitudes and trajectories for the extensional basin (normal faulting) case at upper-bound (left-hand side) and lower-bound shear failure (right-hand side). For the zero tensile strength, tensile failure will occur at <math display="inline"><semantics> <mrow> <mi>F</mi> <mo> </mo> <mo> </mo> <mo> </mo> <mo> </mo> </mrow> </semantics></math>= 9.0. The color indices above the magnitude plots indicate the local stress intensity is normalized by the intermediate (compressional) far-field stress (see <a href="#secBdot5-energies-12-04019" class="html-sec">Appendix B.5</a>). The triangles and dashed lines on the magnitude plots of the maximum principal stress at shear failure and of the minimum principal stress at tensile failure show the expected locations of failure. The blue and green curves, and the red dots in the stress trajectory plots represent the maximum and minimum principal stress trajectories, and the neutral points, respectively. The tables beneath the <math display="inline"><semantics> <mi>F</mi> </semantics></math> curves show the depths for each <math display="inline"><semantics> <mi>F</mi> </semantics></math> value calculated by the Mohr–Coulomb (MC), Mogi–Coulomb (MG), modified Lade (ML), and Drucker–Prager (DP) failure criteria.</p>
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<p>Comparison of the critical wellbore pressure calculated by the Mogi–Coulomb failure criterion. The dashed lines show the critical wellbore pressure with (<b>a</b>) the cohesion of 13.8 MPa and (<b>b</b>) the internal friction angle of 15°. The solid lines indicate the base case (the cohesion of 6.9 MPa and the internal friction angle of 40°). The colors represent the Andersonian cases, i.e., reverse faulting (RF; compressional basin; red), strike-slip (SS; green) and normal faulting (NF; extensional basin; blue). The arrows below the plots indicate the safe windows at 3048 m.</p>
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<p>Critical wellbore pressure at lower- (colored dashed), upper-bound shear (colored solid) and tensile (TF; black solid) failures under the normal faulting case with the vertical, maximum, and minimum horizontal stress gradients of 22.6, 20.4, and 15.8 kPa/m, respectively. The internal friction angle is 15°. The pore pressure gradient is 10.2 kPa/m (black dashed). The wellbore pressures were calculated by the Mohr–Coulomb (MC; red), Mogi–Coulomb (MG; blue), modified Lade (ML; yellow), and Drucker–Prager (DP; green) failure criteria. The minimum horizontal in-situ stress is depicted with the dash-dotted black line. The magnified frame indicates that the lower and upper critical wellbore pressures cross at certain depths, according to the MC (red circle), MG (blue circle), and ML criteria (yellow circle).</p>
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<p>Comparison of the stress trajectories with the numerical simulation results when maximum and minimum total principal stresses normal to the wellbore are 80 MPa and 40 MPa, respectively [<a href="#B2-energies-12-04019" class="html-bibr">2</a>]. Two initial cracks are placed at the wellbore wall with the length of 0.64 (right fracture) and 1.28 times (left fracture) the wellbore radius. The dimensionless variables <span class="html-italic">χ</span> and <span class="html-italic">F</span> are –0.5 and –1.66, respectively. The results are (<b>a</b>) at time 0.0004 and (<b>b</b>) 0.6031 second. (<b>c</b>) The numerical fracture propagation result and the analytical stress trajectory contours are overlaid and match closely.</p>
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<p>Comparison of the stress trajectories with the numerical simulation results when maximum and minimum total principal stresses normal to the wellbore are 80 MPa and 40 MPa, respectively [<a href="#B2-energies-12-04019" class="html-bibr">2</a>]. Two initial cracks are placed at the wellbore wall with the length of 0.64 (right fracture) and 1.28 times (left fracture) the wellbore radius. The dimensionless variables <span class="html-italic">χ</span> and <span class="html-italic">F</span> are –0.5 and –1.66, respectively. The results are (<b>a</b>) at time 0.0004 and (<b>b</b>) 0.6031 second. (<b>c</b>) The numerical fracture propagation result and the analytical stress trajectory contours are overlaid and match closely.</p>
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<p>Casings of the wellbore segments placed between (<b>a</b>) the critical wellbore pressures and (<b>b</b>) the critical <math display="inline"><semantics> <mi>F</mi> </semantics></math> values. (<b>c</b>) Gently curved caving shapes (spalling) from shallow formation. Cavings bounded by principal stress trajectories. (<b>d</b>) Angular caving shapes (shear failure). Cavings bounded by slip lines between two principal stresses.</p>
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28 pages, 5203 KiB  
Article
Energy-Saving Strategies and their Energy Analysis and Exergy Analysis for In Situ Thermal Remediation System of Polluted-Soil
by Tian-Tian Li, Yun-Ze Li, Zhuang-Zhuang Zhai, En-Hui Li and Tong Li
Energies 2019, 12(20), 4018; https://doi.org/10.3390/en12204018 - 22 Oct 2019
Cited by 6 | Viewed by 3312
Abstract
The environmental safety of soil has become a severe problem in China with the boost of industrialization. Polluted-soil thermal remediation is a kind of suitable remediation technology for large-scale heavily contaminated industrial soil, with the advantages of being usable in off-grid areas and [...] Read more.
The environmental safety of soil has become a severe problem in China with the boost of industrialization. Polluted-soil thermal remediation is a kind of suitable remediation technology for large-scale heavily contaminated industrial soil, with the advantages of being usable in off-grid areas and with a high fuel to energy conversion rate. Research on energy-saving strategies is beneficial for resource utilization. Focused on energy saving and efficiency promotion of polluted-soil in situ thermal remediation system, this paper presents three energy-saving strategies: Variable-condition mode (VCM), heat-returning mode (HRM) and air-preheating mode (APM). The energy analysis based on the first law of thermodynamics and exergy analysis based on the second law of thermodynamics are completed. By comparing the results, the most effective part of the energy-saving strategy for variable-condition mode is that high savings in the amount of natural gas (NG) used can be achieved, from 0.1124 to 0.0299 kg·s−1 in the first stage. Energy-saving strategies for heat-returning mode and air-preheating mode have higher utilization ratios than the basic method (BM) for the reason they make full use of waste heat. As a whole, a combination of energy-saving strategies can improve the fuel savings and energy efficiency at the same time. Full article
(This article belongs to the Special Issue Enhancement of Industrial Energy Efficiency and Sustainability)
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<p>System diagram of polluted-soil thermal remediation system: (<b>a</b>) Structure diagram of polluted-soil thermal remediation system including burner, pipe, well and soil; (<b>b</b>) flowchart of air distribution in polluted-soil thermal remediation system.</p>
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<p>Structure diagram of polluted-soil thermal remediation system using the energy-saving strategy for variable-condition mode (VCM).</p>
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<p>System diagram of polluted-soil thermal remediation system using the energy-saving strategy for heat-returning mode: (<b>a</b>) Structure diagram of polluted-soil thermal remediation system using the energy-saving strategy for heat-returning mode; (<b>b</b>) flowchart of air distribution in polluted-soil thermal remediation system using the energy-saving strategy for heat-returning mode.</p>
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<p>System diagram of polluted-soil thermal remediation system using the energy-saving strategy for air-preheating mode: (<b>a</b>) Structure diagram of polluted-soil thermal remediation system using the energy-saving strategy for air-preheating mode; (<b>b</b>) flowchart of air distribution in polluted-soil thermal remediation system using the energy-saving strategy for air-preheating mode.</p>
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<p>The locations of energy loss of components of polluted-soil thermal remediation system.</p>
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<p>The locations of exergy loss of components of polluted-soil thermal remediation system.</p>
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<p>The flowchart of the parameters calculation in energy analysis.</p>
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<p>Energy utilization ratio and exergy utilization ratio comparisons of variable-condition mode (VCM) and basic method (BM): (<b>a</b>) Energy utilization ratio comparisons of variable-condition mode (VCM) and basic method (BM); (<b>b</b>) Exergy utilization ratio comparisons of variable-condition mode (VCM) and basic method (BM).</p>
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<p>Flow diagrams of forced convection in stage <math display="inline"><semantics> <mi mathvariant="normal">I</mi> </semantics></math> of of BM and VCM: (<b>a</b>) Energy flow diagram of forced convection in stage <math display="inline"><semantics> <mi mathvariant="normal">I</mi> </semantics></math> of BM; (<b>b</b>) Energy flow diagram of forced convection in stage <math display="inline"><semantics> <mi mathvariant="normal">I</mi> </semantics></math> of VCM; (<b>c</b>) Exergy flow diagram of forced convection in stage <math display="inline"><semantics> <mi mathvariant="normal">I</mi> </semantics></math> of BM; (<b>d</b>) Exergy flow diagram of forced convection in stage <math display="inline"><semantics> <mi mathvariant="normal">I</mi> </semantics></math> of VCM.</p>
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<p>Energy utilization ratio and exergy utilization ratio comparisons of Case 3.1, Case 3.2 and Case 3.3 of heat-returning mode and basic method (BM): (<b>a</b>) Energy utilization ratio comparisons of Case 3.1, Case 3.2 and Case 3.3 of heat-returning mode and basic method (BM); (<b>b</b>) Exergy utilization ratio comparisons of Case 3.1, Case 3.2 and Case 3.3 of heat-returning mode and basic method (BM).</p>
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<p>Flow diagrams of forced convection in stage <math display="inline"><semantics> <mi mathvariant="normal">I</mi> </semantics></math> of BM and Case 3.3 of heat-returning mode: (<b>a</b>) Energy flow diagram of forced convection in stage <math display="inline"><semantics> <mi mathvariant="normal">I</mi> </semantics></math> of BM; (<b>b</b>) Energy flow diagram of forced convection in stage <math display="inline"><semantics> <mi mathvariant="normal">I</mi> </semantics></math> of Case 3.3 of heat-returning mode; (<b>c</b>) Exergy flow diagram of forced convection in stage <math display="inline"><semantics> <mi mathvariant="normal">I</mi> </semantics></math> of BM; (<b>d</b>) Exergy flow diagram of forced convection in stage <math display="inline"><semantics> <mi mathvariant="normal">I</mi> </semantics></math> of Case 3.3 of heat-returning mode.</p>
Full article ">Figure 11 Cont.
<p>Flow diagrams of forced convection in stage <math display="inline"><semantics> <mi mathvariant="normal">I</mi> </semantics></math> of BM and Case 3.3 of heat-returning mode: (<b>a</b>) Energy flow diagram of forced convection in stage <math display="inline"><semantics> <mi mathvariant="normal">I</mi> </semantics></math> of BM; (<b>b</b>) Energy flow diagram of forced convection in stage <math display="inline"><semantics> <mi mathvariant="normal">I</mi> </semantics></math> of Case 3.3 of heat-returning mode; (<b>c</b>) Exergy flow diagram of forced convection in stage <math display="inline"><semantics> <mi mathvariant="normal">I</mi> </semantics></math> of BM; (<b>d</b>) Exergy flow diagram of forced convection in stage <math display="inline"><semantics> <mi mathvariant="normal">I</mi> </semantics></math> of Case 3.3 of heat-returning mode.</p>
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<p>Energy utilization ratio and exergy utilization ratio comparisons of Case 4.1, Case 4.2 and Case 4.3 of heat-returning mode and basic method (BM): (<b>a</b>) Energy utilization ratio comparisons of Case 4.1, Case 4.2 and Case 4.3 of heat-returning mode and basic method (BM); (<b>b</b>) Exergy utilization ratio comparisons of Case 4.1, Case 4.2 and Case 4.3 of heat-returning mode and basic method (BM).</p>
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<p>Flow diagrams of forced convection in stage <math display="inline"><semantics> <mi mathvariant="normal">I</mi> </semantics></math> of BM and Case 4.3 of air-preheating mode: (<b>a</b>) Energy flow diagram of forced convection in stage <math display="inline"><semantics> <mi mathvariant="normal">I</mi> </semantics></math> of BM; (<b>b</b>) Energy flow diagram of forced convection in stage <math display="inline"><semantics> <mi mathvariant="normal">I</mi> </semantics></math> of Case 4.3 of air-preheating mode; (<b>c</b>) Exergy flow diagram of forced convection in stage <math display="inline"><semantics> <mi mathvariant="normal">I</mi> </semantics></math> of BM; (<b>d</b>) Exergy flow diagram of forced convection in stage <math display="inline"><semantics> <mi mathvariant="normal">I</mi> </semantics></math> of Case 4.3 of air-preheating mode.</p>
Full article ">Figure 13 Cont.
<p>Flow diagrams of forced convection in stage <math display="inline"><semantics> <mi mathvariant="normal">I</mi> </semantics></math> of BM and Case 4.3 of air-preheating mode: (<b>a</b>) Energy flow diagram of forced convection in stage <math display="inline"><semantics> <mi mathvariant="normal">I</mi> </semantics></math> of BM; (<b>b</b>) Energy flow diagram of forced convection in stage <math display="inline"><semantics> <mi mathvariant="normal">I</mi> </semantics></math> of Case 4.3 of air-preheating mode; (<b>c</b>) Exergy flow diagram of forced convection in stage <math display="inline"><semantics> <mi mathvariant="normal">I</mi> </semantics></math> of BM; (<b>d</b>) Exergy flow diagram of forced convection in stage <math display="inline"><semantics> <mi mathvariant="normal">I</mi> </semantics></math> of Case 4.3 of air-preheating mode.</p>
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<p>Energy utilization ratio and exergy utilization ratio comparisons of Case 3.4, Case 4.4 and variable-condition mode (VCM): (<b>a</b>) Energy utilization ratio comparisons of Case 3.4, Case 4.4 and variable-condition mode (VCM); (<b>b</b>) Exergy utilization ratio comparisons of Case 3.4, Case 4.4 and variable-condition mode (VCM).</p>
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<p>The heat transfer process: (<b>a</b>) The ideal heat transfer and (<b>b</b>) The actual heat transfer.</p>
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22 pages, 4139 KiB  
Article
A Novel Fault Diagnosis Method for Power Transformer Based on Dissolved Gas Analysis Using Hypersphere Multiclass Support Vector Machine and Improved D–S Evidence Theory
by Haikun Shang, Junyan Xu, Zitao Zheng, Bing Qi and Liwei Zhang
Energies 2019, 12(20), 4017; https://doi.org/10.3390/en12204017 - 22 Oct 2019
Cited by 27 | Viewed by 3622
Abstract
Power transformers are important equipment in power systems and their reliability directly concerns the safety of power networks. Dissolved gas analysis (DGA) has shown great potential for detecting the incipient fault of oil-filled power transformers. In order to solve the misdiagnosis problems of [...] Read more.
Power transformers are important equipment in power systems and their reliability directly concerns the safety of power networks. Dissolved gas analysis (DGA) has shown great potential for detecting the incipient fault of oil-filled power transformers. In order to solve the misdiagnosis problems of traditional fault diagnosis approaches, a novel fault diagnosis method based on hypersphere multiclass support vector machine (HMSVM) and Dempster–Shafer (D–S) Evidence Theory (DET) is proposed. Firstly, proper gas dissolved in oil is selected as the fault characteristic of power transformers. Secondly, HMSVM is employed to diagnose transformer fault with selected characteristics. Then, particle swarm optimization (PSO) is utilized for parameter optimization. Finally, DET is introduced to fuse three different fault diagnosis methods together, including HMSVM, hybrid immune algorithm (HIA), and kernel extreme learning machine (KELM). To avoid the high conflict between different evidences, in this paper, a weight coefficient is introduced for the correction of fusion results. Results indicate that the fault diagnosis based on HMSVM has the highest probability to identify transformer faults among three artificial intelligent approaches. In addition, the improved D–S evidence theory (IDET) combines the advantages of each diagnosis method and promotes fault diagnosis accuracy. Full article
(This article belongs to the Special Issue Power Transformer Condition Assessment)
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<p>Hypersphere support vector machine (HSSVM) schematic diagram.</p>
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<p>Hypersphere multiclass support vector machine (HMSVM) classification model.</p>
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<p>Boundaries of HMSVM vary with different parameters.</p>
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<p>Influence of <span class="html-italic">C</span> on the radius of HMSVM.</p>
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<p>Influence of σ on the radius of HMSVM.</p>
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<p>Flow chart of particle swarm optimization (PSO)-HMSVM.</p>
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<p>Flow chart of transformer fault diagnosis based on PSO-HMSVM.</p>
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<p>Fitness curve of parameters in HMSVM.</p>
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<p>Diagnosis results of three intelligent algorithms.</p>
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<p>The fault diagnosis model of power transformer based on improved evidence theory.</p>
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<p>Diagnostic results with different algorithms.</p>
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25 pages, 4698 KiB  
Article
Cluster Analysis of Distribution Grids in Baden-Württemberg
by Tobias Rösch and Peter Treffinger
Energies 2019, 12(20), 4016; https://doi.org/10.3390/en12204016 - 22 Oct 2019
Cited by 6 | Viewed by 2654
Abstract
With the growing share of renewable energies in the electricity supply, transmission and distribution grids have to be adapted. A profound understanding of the structural characteristics of distribution grids is essential to define suitable strategies for grid expansion. Many countries have a large [...] Read more.
With the growing share of renewable energies in the electricity supply, transmission and distribution grids have to be adapted. A profound understanding of the structural characteristics of distribution grids is essential to define suitable strategies for grid expansion. Many countries have a large number of distribution system operators (DSOs) whose standards vary widely, which contributes to coordination problems during peak load hours. This study contributes to targeted distribution grid development by classifying DSOs according to their remuneration requirement. To examine the amendment potential, structural and grid development data from 109 distribution grids in South-Western Germany, are collected, referring to publications of the respective DSOs. The resulting data base is assessed statistically to identify clusters of DSOs according to the fit of demographic requirements and grid-construction status and thus identify development needs to enable a broader use of regenerative energy resources. Three alternative algorithms are explored to manage this task. The study finds the novel Gauss-Newton algorithm optimal to analyse the fit of grid conditions to regional requirements and successfully identifies grids with remuneration needs. It is superior to the so far used K-Means algorithm. The method developed here is transferable to other areas for grid analysis and targeted, cost-efficient development. Full article
(This article belongs to the Section F: Electrical Engineering)
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<p>Different fitting functions with exemplary residuals obtained by the Gauss-Newton-Method.</p>
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<p>Population density map of Baden-Württemberg [<a href="#B22-energies-12-04016" class="html-bibr">22</a>].</p>
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<p>Plot of W<sub>na</sub> versus D<sub>po</sub> with linear regression line.</p>
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<p>Plot of l<sub>c</sub> versus n<sub>gc</sub> with regression line.</p>
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<p>Block diagram of empirical method.</p>
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<p>Clustering obtained by means of k-means algorithm applying the attributes <math display="inline"><semantics> <mrow> <mfrac> <mrow> <msub> <mi mathvariant="normal">W</mi> <mrow> <mi>PV</mi> </mrow> </msub> </mrow> <mrow> <msub> <mi mathvariant="normal">n</mi> <mrow> <mi>gc</mi> </mrow> </msub> </mrow> </mfrac> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="normal">D</mi> <mrow> <mi>po</mi> </mrow> </msub> </mrow> </semantics></math>.</p>
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<p>Clustering obtained with the k-means algorithm considering the attributes l<sub>c</sub> and n<sub>gc</sub>.</p>
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<p>Clustering obtained with the DBSCAN algorithm considering the attributes <math display="inline"><semantics> <mrow> <msub> <mrow> <mrow> <mo>(</mo> <mrow> <mfrac> <mrow> <msub> <mi mathvariant="normal">W</mi> <mrow> <mi>PV</mi> </mrow> </msub> </mrow> <mrow> <msub> <mi mathvariant="normal">n</mi> <mrow> <mi>gc</mi> </mrow> </msub> </mrow> </mfrac> </mrow> <mo>)</mo> </mrow> </mrow> <mi mathvariant="normal">N</mi> </msub> </mrow> </semantics></math> and D<sub>PO,N</sub> (n<sub>min</sub> = 3 and ε = 0.07).</p>
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<p>Clustering obtained with the DBSCAN algorithm for attributes l<sub>c,N</sub> and N<sub>gc,N</sub>, n<sub>min</sub> = 5 and ε = 0.0075).</p>
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<p>Plot of <math display="inline"><semantics> <mrow> <mfrac> <mrow> <msub> <mi>W</mi> <mrow> <mi>P</mi> <mi>V</mi> </mrow> </msub> </mrow> <mrow> <msub> <mi>n</mi> <mrow> <mi>g</mi> <mi>c</mi> </mrow> </msub> </mrow> </mfrac> </mrow> </semantics></math> versus D<sub>po</sub>.</p>
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<p>Plot of <math display="inline"><semantics> <mrow> <msub> <mstyle mathvariant="bold" mathsize="normal"> <mi>l</mi> </mstyle> <mstyle mathvariant="bold" mathsize="normal"> <mi>c</mi> </mstyle> </msub> </mrow> </semantics></math> versus <b>n</b><sub>gc</sub>. with Gauss Newton Algorithm.</p>
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<p>Reliability test of Gauss Newton Algorithm to identify misfits of W<sub>pv</sub>/n<sub>gc -</sub> W<sub>po</sub> and of l<sub>c</sub>-n<sub>gc</sub>.</p>
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<p>Reliability test of Gauss Newton Algorithm to identify misfits of W<sub>pv</sub>/n<sub>gc -</sub> W<sub>po</sub> and of l<sub>c</sub>-n<sub>gc</sub>.</p>
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<p>Reliability test of Gauss Newton Algorithm to identify misfits of W<sub>pv</sub>/n<sub>gc -</sub> W<sub>po</sub> and of l<sub>c</sub>-n<sub>gc</sub>.</p>
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32 pages, 10681 KiB  
Article
Dynamic Modeling and Preliminary Performance Analysis of a New Solar Thermal Reverse Osmosis Desalination Process
by Clément Lacroix, Maxime Perier-Muzet and Driss Stitou
Energies 2019, 12(20), 4015; https://doi.org/10.3390/en12204015 - 22 Oct 2019
Cited by 15 | Viewed by 3691
Abstract
Reverse osmosis (RO) is a desalination technique that is commonly preferred because of its low energy consumption. In this paper, an innovative, thermally powered RO desalination process is presented. This new thermo-hydraulic process uses solar thermal energy in order to realize the pressurization [...] Read more.
Reverse osmosis (RO) is a desalination technique that is commonly preferred because of its low energy consumption. In this paper, an innovative, thermally powered RO desalination process is presented. This new thermo-hydraulic process uses solar thermal energy in order to realize the pressurization of the saltwater beyond its osmotic pressure to allow its desalination. This pressurization is enabled thanks to a piston or a membrane set in motion in a reservoir by a working fluid that follows a thermodynamic cycle similar to an Organic Rankine Cycle. In this cycle, the evaporator is heated by low-grade heat, such as the one delivered by flat-plate solar collectors, while the condenser is cooled by the saltwater to be treated. Such an installation, designed for small-scale (1 to 10 m3·day−1) brackish water desalination, should enable an average daily production of 500 L of drinkable water per m² of solar collectors with a specific thermal energy consumption of about 6 kWhth·m−3. A dynamic modeling of the whole process has been developed in order to study its dynamic cyclic operating behavior under variable solar thermal power, to optimize its design, and to maximize its performances. This paper presents the preliminary performance results of such a solar-driven desalination process. Full article
(This article belongs to the Section A2: Solar Energy and Photovoltaic Systems)
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<p>Global diagram of the thermo-hydraulic desalination process.</p>
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<p>Flow diagram of the thermo-hydraulic desalination process during a half-cycle: (<b>a</b>) alpha phase, (<b>b</b>) beta phase, (<b>c</b>) end of half-cycle. Schematic representation of the evolution of the pressure and volume in the transfer tanks, and the mass flow rate of the permeate over one complete cycle.</p>
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<p>Flow diagram of the thermo-hydraulic desalination process during the liquid refilling phase of the evaporator by the second cylinder, which is put in motion by the high-pressure vapor supplied by the evaporator during the beta phases of the cycle.</p>
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<p>Schematic of evaporator modeling.</p>
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<p>Schematic of condenser modeling.</p>
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<p>Schematic of transfer tanks modeling during: (<b>a</b>) pressurization, (<b>b</b>) working fluid expansion, and (<b>c</b>) water filling.</p>
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<p>Modeling of hydraulic cylinders: (<b>a</b>) main cylinder, (<b>b</b>) refilling cylinder.</p>
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<p>Schematic of the reverse osmosis (RO) membrane modeling.</p>
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<p>Schematic concentration profile in the half-feed channel in the case of a low (in red line) or a high feed-flow rate (dot line).</p>
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<p>Picture (<b>a</b>) and schematic diagram (<b>b</b>) of the experimental setup.</p>
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<p>Applied pressure profiles starting at 7, 10, or 12 bars, consisting in a plateau followed by a decreasing evolution.</p>
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<p>Comparison of experimental data with the simulated results obtained by the dynamic model for different starting pressures: (<b>a</b>) 7 bars, (<b>b</b>) 10 bars, and (<b>c</b>) 12 bars.</p>
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<p>Graph of feed pressure evolution applied to the spiral wound module.</p>
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<p>Longitudinal evolution of the local permeate flow and membrane saturation ratio when applied pressure is decreasing.</p>
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<p>Simulated temperature and pressure evolutions of the vapor contained in the tanks over several cycles, considering a constant thermal power at the evaporator.</p>
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<p>Permeate-simulated flow rate over time, considering a constant thermal power at the evaporator.</p>
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<p>Stroke evolutions of the main and refilling actuators over time, considering a constant thermal power at the evaporator.</p>
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<p>Simulated fresh-water production and quality over a sunny day.</p>
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<p>Simulated evaporator pressure over a sunny day.</p>
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<p>Simulated evaporator temperature over a sunny day.</p>
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<p>Simulated fresh-water production and quality during a disturbed and cloudy day.</p>
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<p>Simulated evaporator pressure during the disturbed day.</p>
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<p>Simulated evaporator temperature over the disturbed day.</p>
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11 pages, 3724 KiB  
Article
Effects of the Aspect Ratio of a Rectangular Thermosyphon on Its Thermal Performance
by Chia-Wang Yu, C. S. Huang, C. T. Tzeng and Chi-Ming Lai
Energies 2019, 12(20), 4014; https://doi.org/10.3390/en12204014 - 22 Oct 2019
Cited by 2 | Viewed by 2358
Abstract
The natural convection behaviors of rectangular thermosyphons with different aspect ratios were experimentally analyzed in this study. The experimental model consisted of a loop body, a heating section, a cooling section, and adiabatic sections. The heating and cooling sections were located in the [...] Read more.
The natural convection behaviors of rectangular thermosyphons with different aspect ratios were experimentally analyzed in this study. The experimental model consisted of a loop body, a heating section, a cooling section, and adiabatic sections. The heating and cooling sections were located in the vertical portions of the rectangular loop. The length of the vertical cooling section and the lengths of the upper and lower adiabatic sections were fixed at 300 mm and 200 mm, respectively. The inner diameter of the loop was fixed at 11 mm, and the cooling end temperature was 30 °C. The relevant parameters and their ranges were as follows: The aspect ratios were 6, 4.5, and 3.5 (with potential differences of 41, 27, and 18, respectively, between the cold and hot ends), and the input thermal power ranged from 30 to 60 W (with a heat flux of 600 to 3800 W/m2). The results show that it is feasible to obtain solar heat gain by installing a rectangular thermosyphon inside the metal curtain wall and that increasing the height of the opaque part of the metal curtain wall can increase the aspect ratio of the rectangular thermosyphon installed inside the wall and thus improve the heat transfer efficiency. Full article
(This article belongs to the Special Issue Building Thermal Envelope)
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<p>Scenario and test cell illustration.</p>
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<p>Experimental test cell and geometric parameters.</p>
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<p>Steady-state temperature distribution of the wall of the loop with different heating powers.</p>
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<p>Distribution of the maximum wall temperature.</p>
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<p>Relationship between the Reynolds number (<span class="html-italic">Re</span>) and the modified Rayleigh number (<span class="html-italic">Ra</span>*).</p>
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<p>Relationship between the average Nusselt number (<span class="html-italic">Nu</span>) and the modified Rayleigh number (<span class="html-italic">Ra</span>*) of the heating section.</p>
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<p>Relationship between the thermal resistance of the working fluid flow (<math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mrow> <mi>f</mi> <mi>l</mi> <mi>o</mi> <mi>w</mi> </mrow> </msub> </mrow> </semantics></math>) and <span class="html-italic">Ra</span>*.</p>
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20 pages, 5254 KiB  
Article
CO2 Adsorption–Desorption Kinetics from the Plane Sheet of Hard Coal and Associated Shrinkage of the Material
by Norbert Skoczylas, Anna Pajdak and Mariusz Młynarczuk
Energies 2019, 12(20), 4013; https://doi.org/10.3390/en12204013 - 22 Oct 2019
Cited by 6 | Viewed by 2445
Abstract
The paper presents the results of studies on sorption and CO2 desorptions from coals from two Polish mines that differed in petrographic and structural properties. The tests were carried out on spherical and plane sheet samples. On the basis of the sorption [...] Read more.
The paper presents the results of studies on sorption and CO2 desorptions from coals from two Polish mines that differed in petrographic and structural properties. The tests were carried out on spherical and plane sheet samples. On the basis of the sorption tests, the effective diffusion coefficient was calculated on the plane sheet samples based on a proper model. Similar tests were performed on the spherical samples. Mathematical model results for plane sheet samples were compared with the most frequently chosen model for spherical samples. The kinetics of CO2 desorption from plane sheet samples were compared with the kinetics of sample shrinkage. In both samples, the shrinkage was about 0.35%. The size change kinetics and CO2 desorption kinetics significantly differed between the samples. In both samples, the determined shrinkage kinetics was clearly faster than CO2 kinetics. Full article
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Figure 1
<p>The half time of CO<sub>2</sub> desorption from coal samples De = 10<sup>−9</sup> [cm<sup>2</sup>/s] of various equivalent radii in the range of: (<b>a</b>) 0.007–0.15 cm; (<b>b</b>) 0.007–1.5 cm.</p>
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<p>Coal sample: (<b>a</b>) after grinding to 0.2–0.25 mm grains; (<b>b</b>) plane sheet.</p>
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<p>Registration of changes in the plane sheet sample geometry during desorption: (<b>a</b>) measuring laser head; (<b>b</b>) side view of the sample; top view of the sample.</p>
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<p>Taking into account changes in the size of coal samples during desorption in the time between the step change in pressure, with the moment of recording the sample geometry: (<b>a</b>) coal from Sobieski mine coal; (<b>b</b>) coal from Budryk mine coal.</p>
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<p>CO<sub>2</sub> adsorption isotherms (273 K) of coal materials.</p>
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<p>Pore size distribution of the coals based on method low-pressure gas adsorption (LPA) CO<sub>2</sub> (HK model).</p>
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<p>Pore size distribution of coals based on method LPA N<sub>2</sub> (BJH model).</p>
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<p>Methane desorption from spherical grains and plane sheet samples.</p>
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<p>Sorption isotherm for Sobieski mine coal—granular sample.</p>
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<p>Sorption isotherm for Budryk mine coal—granular sample.</p>
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<p>CO<sub>2</sub> saturation kinetics for Sobieski mine coal—granular sample.</p>
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<p>CO<sub>2</sub> saturation kinetics for Budryk mine coal—granular sample.</p>
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<p>CO<sub>2</sub> saturation kinetics for Sobieski mine coal—plane sheet sample.</p>
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<p>CO<sub>2</sub> saturation kinetics for Budryk mine coal—plane sheet sample.</p>
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<p>Kinetics of shrinkage of Sobieski mine coal juxtaposed with CO<sub>2</sub> desorption kinetics—plane sheet sample.</p>
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<p>Kinetics of shrinkage of Budryk mine coal juxtaposed with CO<sub>2</sub> desorption kinetics—plane sheet sample.</p>
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<p>Particular types of diffusion in the coal pore system [<a href="#B50-energies-12-04013" class="html-bibr">50</a>].</p>
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19 pages, 8814 KiB  
Article
A New Method of Determination of the Angle of Attack on Rotating Wind Turbine Blades
by Wei Zhong, Wen Zhong Shen, Tong Guang Wang and Wei Jun Zhu
Energies 2019, 12(20), 4012; https://doi.org/10.3390/en12204012 - 22 Oct 2019
Cited by 8 | Viewed by 3091
Abstract
The angle of attack (AoA) is the key parameter when extracting the aerodynamic polar from the rotating blade sections of a wind turbine. However, the determination of AoA is not straightforward using computational fluid dynamics (CFD) or measurement. Since the incoming streamlines are [...] Read more.
The angle of attack (AoA) is the key parameter when extracting the aerodynamic polar from the rotating blade sections of a wind turbine. However, the determination of AoA is not straightforward using computational fluid dynamics (CFD) or measurement. Since the incoming streamlines are bent because of the complex inductions of the rotor, discrepancies exist between various existing determination methods, especially in the tip region. In the present study, flow characteristics in the region near wind turbine blades are analyzed in detail using CFD results of flows past the NREL UAE Phase VI rotor. It is found that the local flow determining AOA changes rapidly in the vicinity of the blade. Based on this finding, the concepts of effective AoA as well as nominal AoA are introduced, leading to a new method of AOA determination. The new method has 5 steps: (1) Find the distributed vortices on the blade surface; (2) select two monitoring points per cross-section close to the aerodynamic center on both pressure and suction sides with an equal distance from the rotor plane; (3) subtract the blade self-induction from the velocity at each monitoring point; (4) average the velocity of the two monitoring points obtained in Step 3; (5) determine the AoA using the velocity obtained in Step 4. Since the monitoring points for the first time can be set very close to the aerodynamic center, leading to an excellent estimation of AoA. The aerodynamic polar extracted through determination of the effective AoA exhibits a consistent regularity for both the mid-board and tip sections, which has never been obtained by the existing determination methods. Full article
(This article belongs to the Section A3: Wind, Wave and Tidal Energy)
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<p>Different kinds of inductions in the flattened section of a certain radial location: (<b>a</b>) Bent streamlines because of disc-induction; (<b>b</b>) self-induction; (<b>c</b>) downwash because of 3D-induction.</p>
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<p>Illustration of the concentrated vortex of an airfoil.</p>
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<p>Illustration of the distributed vortices on an airfoil: (<b>a</b>) vortices; (<b>b</b>) viscous flow on a small segment.</p>
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<p>Contours of dimensionless-induced velocity: (<b>a</b>) Induced by the airfoil entity, <math display="inline"><semantics> <mrow> <mrow> <mrow> <mrow> <mo>|</mo> <mrow> <msub> <msup> <mover accent="true"> <mi>v</mi> <mo stretchy="false">→</mo> </mover> <mo>′</mo> </msup> <mrow> <mi>a</mi> <mi>i</mi> <mi>r</mi> <mi>f</mi> <mi>o</mi> <mi>i</mi> <mi>l</mi> </mrow> </msub> </mrow> <mo>|</mo> </mrow> </mrow> <mo>/</mo> <mrow> <msub> <mi>V</mi> <mo>∞</mo> </msub> </mrow> </mrow> </mrow> </semantics></math>; (<b>b</b>) induced by the distributed vortices, <math display="inline"><semantics> <mrow> <mrow> <mrow> <mrow> <mo>|</mo> <mrow> <msub> <msup> <mover accent="true"> <mi>v</mi> <mo stretchy="false">→</mo> </mover> <mo>′</mo> </msup> <mrow> <mi>v</mi> <mi>o</mi> <mi>r</mi> <mi>t</mi> <mi>i</mi> <mi>c</mi> <mi>e</mi> <mi>s</mi> </mrow> </msub> </mrow> <mo>|</mo> </mrow> </mrow> <mo>/</mo> <mrow> <msub> <mi>V</mi> <mo>∞</mo> </msub> </mrow> </mrow> </mrow> </semantics></math> (Airfoil S809, <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>6</mn> <mo>°</mo> </mrow> </semantics></math>, Reynolds number = <math display="inline"><semantics> <mrow> <mn>1</mn> <mo>×</mo> <msup> <mrow> <mn>10</mn> </mrow> <mn>6</mn> </msup> </mrow> </semantics></math>).</p>
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<p>Contours of relative error (Airfoil S809, <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>6</mn> <mo>°</mo> </mrow> </semantics></math>, Reynolds number = 1 × 10<sup>6</sup>).</p>
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<p>Illustration of the vortex at a small face of blade surface: the faces are intentionally enlarged in order to be displayed clearly.</p>
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<p>Contours of axial interference factor before self-induction subtraction in the flattened section of <span class="html-italic">r</span>/<span class="html-italic">R</span> = 47%, where <math display="inline"><semantics> <mi>ψ</mi> </semantics></math> denotes the azimuthal angle (<math display="inline"><semantics> <mrow> <msub> <mi>V</mi> <mo>∞</mo> </msub> <mo>=</mo> <mn>7</mn> <mrow> <mi mathvariant="normal">m</mi> <mo>/</mo> <mi mathvariant="normal">s</mi> </mrow> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>ω</mi> <mo>=</mo> <mn>72</mn> <mi>rpm</mi> </mrow> </semantics></math>).</p>
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<p>Contours of axial interference factor after self-induction subtraction in the flattened section of <span class="html-italic">r</span>/<span class="html-italic">R</span> = 47% (<math display="inline"><semantics> <mrow> <msub> <mi>V</mi> <mo>∞</mo> </msub> <mo>=</mo> <mn>7</mn> <mrow> <mi mathvariant="normal">m</mi> <mo>/</mo> <mi mathvariant="normal">s</mi> </mrow> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>ω</mi> <mo>=</mo> <mn>72</mn> <mi>rpm</mi> </mrow> </semantics></math>).</p>
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<p>Variation of axial interference factor with azimuthal angle in the rotor plane at <span class="html-italic">r</span>/<span class="html-italic">R</span> = 47% (<math display="inline"><semantics> <mrow> <msub> <mi>V</mi> <mo>∞</mo> </msub> <mo>=</mo> <mn>7</mn> <mrow> <mi mathvariant="normal">m</mi> <mo>/</mo> <mi mathvariant="normal">s</mi> </mrow> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>ω</mi> <mo>=</mo> <mn>72</mn> <mi>rpm</mi> </mrow> </semantics></math>).</p>
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<p>Contours of axial interference factor after the self-induction subtraction in the flatten section of <span class="html-italic">r</span>/<span class="html-italic">R</span> = 95% (<math display="inline"><semantics> <mrow> <msub> <mi>V</mi> <mo>∞</mo> </msub> <mo>=</mo> <mn>7</mn> <mrow> <mi mathvariant="normal">m</mi> <mo>/</mo> <mi mathvariant="normal">s</mi> </mrow> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>ω</mi> <mo>=</mo> <mn>72</mn> <mi>rpm</mi> </mrow> </semantics></math>).</p>
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<p>Contours of axial interference factor after the self-induction subtraction around the blade the dashed black line is a typical contour line (<math display="inline"><semantics> <mrow> <msub> <mi>V</mi> <mo>∞</mo> </msub> <mo>=</mo> <mn>7</mn> <mrow> <mi mathvariant="normal">m</mi> <mo>/</mo> <mi mathvariant="normal">s</mi> </mrow> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>ω</mi> <mo>=</mo> <mn>72</mn> <mi>rpm</mi> </mrow> </semantics></math>).</p>
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<p>Values of AoA determined by the local velocity after the self-induction subtraction at various azimuthal locations on the <span class="html-italic">r</span>/<span class="html-italic">R</span> = 95% ring in the rotor plane; the dashed black curves near the blade axis as well as the effective AoA are estimated (<math display="inline"><semantics> <mrow> <msub> <mi>V</mi> <mo>∞</mo> </msub> <mo>=</mo> <mn>7</mn> <mrow> <mi mathvariant="normal">m</mi> <mo>/</mo> <mi mathvariant="normal">s</mi> </mrow> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>ω</mi> <mo>=</mo> <mn>72</mn> <mi>rpm</mi> </mrow> </semantics></math>).</p>
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<p>Contours of AoA determined by the local velocity after the self-induction subtraction in the flattened section of <span class="html-italic">r</span>/<span class="html-italic">R</span> = 95%; the solid points P1, P2, P3, P1’, P2’, P3’ are the monitoring points of 3-point AT; the dashed circle are used for Line AT; the star points A and A’ are the monitoring points of the present determination (<math display="inline"><semantics> <mrow> <msub> <mi>V</mi> <mo>∞</mo> </msub> <mo>=</mo> <mn>7</mn> <mrow> <mi mathvariant="normal">m</mi> <mo>/</mo> <mi mathvariant="normal">s</mi> </mrow> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>ω</mi> <mo>=</mo> <mn>72</mn> <mi>rpm</mi> </mrow> </semantics></math>).</p>
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<p>Variation of AoA along the blade span: (<b>a</b>) nominal AoA; (<b>b</b>) effective AoA.</p>
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<p>Variation of the downwash angle along the blade span: (<b>a</b>) downwash angle; (<b>b</b>) the ratio of downwash angle to nominal AoA.</p>
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<p>Lift coefficient extracted through: (<b>a</b>) nominal AoA; (<b>b</b>) effective AoA.</p>
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<p>Drag coefficient extracted through: (<b>a</b>) nominal AoA; (<b>b</b>) effective AoA.</p>
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21 pages, 6578 KiB  
Article
An Electro-Pneumatic Force Tracking System using Fuzzy Logic Based Volume Flow Control
by Zhonglin Lin, Qingyan Wei, Runmin Ji, Xianghua Huang, Yuan Yuan and Zhiwen Zhao
Energies 2019, 12(20), 4011; https://doi.org/10.3390/en12204011 - 22 Oct 2019
Cited by 16 | Viewed by 3588
Abstract
In this paper, a fuzzy logic based volume flow control method is proposed to precisely control the force of a pneumatic actuator in an electro-pneumatic system including four on-off valves. The volume flow feature, which is the relationship between the duty cycle of [...] Read more.
In this paper, a fuzzy logic based volume flow control method is proposed to precisely control the force of a pneumatic actuator in an electro-pneumatic system including four on-off valves. The volume flow feature, which is the relationship between the duty cycle of the pulse width modulation (PWM) period, pressure difference, and volume flow of an on-off valve, is based on the experimental data measured by a high-precision volume flow meter. Through experimental data analysis, the maximum and minimum duty cycles are acquired. A new volume flow control method is introduced for the pneumatic system. In this method, the raw measured data are innovatively processed by a segmented, polynomial fitting method, and a newly designed procedure for calculating the duty cycle is adopted. This procedure makes it possible to combine the original data with fuzzy logic control (FLC). Additionally, the method allows us to accurately control the minimum and maximum opening pulse width of the valve. Several experiments are performed based on the experimental data, instead of the traditional theoretical models. Only 0.141 N (1.41%) overshoot and 0.03 N (0.03%) steady-state error are observed in the step response experiment, and 0.123 N average error is found while tracking the sine wave reference. Full article
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<p>Measure the volume flow: (<b>a</b>) Procedure; (<b>b</b>) Programming interface; (<b>c</b>) Main program. Pulse width modulation (PWM).</p>
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<p>Experimental setup of measuring the volume flow. Central Processing Unit (CPU), RAM (Random Access Memory), National Instruments (NI), and CompactRIO (cRIO).</p>
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<p>Standard and normalized results from 0.01 MPa pressure difference using three PWM frequencies: (<b>a</b>) Standard results; (<b>b</b>) Normalized results.</p>
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<p>Standard and normalized results from 0.1 MPa pressure difference using three PWM frequencies: (<b>a</b>) Standard results; (<b>b</b>) Normalized results.</p>
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<p>Standard and normalized results from 0.3 MPa pressure difference using three PWM frequencies: (<b>a</b>) Standard results; (<b>b</b>) Normalized results.</p>
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<p>The minimum pulse width of three different PWM frequencies under different pressure differences.</p>
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<p>The maximum pulse width of three different PWM frequencies under different pressure difference.</p>
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<p>Normalized volume flow results from eight different pressure difference using a 100 Hz PWM frequency.</p>
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<p>Schematic diagram of the force tracking system.</p>
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<p>The fuzzy logic controller with volume flow feature. (<b>a</b>) Block diagram; (<b>b</b>)The program in Labview. digital outputs (DOs), and analog inputs (AIs).</p>
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<p>Final polynomial fitting of the volume flow feature: (<b>a</b>) 3-D relationship between pressure difference, duty cycle, and volume flow; (<b>b</b>) 2-D relationship between duty cycle and volume flow; (<b>c</b>) Method for calculating the output duty cycle.</p>
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<p>Membership functions of the fuzzy logic controller: (<b>a</b>) Input variable error; (<b>b</b>) Input variable error rate; (<b>c</b>) Output variable. negative big (NB), negative medium (NM), negative small (NS), zero (Z), positive small (PS), positive medium (PM), and positive big (PB).</p>
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<p>Control surface of the fuzzy logic controller.</p>
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<p>Final experimental setup.</p>
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<p>Pictures of the real system.</p>
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<p>Tasks utilized on PC, cRIO-9074 RT, and cRIO-9074. personal computer (PC), Real-Time (RT), and field programmable gate array (FPGA).</p>
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<p>Experimental results of the step response for proposed scheme and conventional proportional–integral–derivative (PID) controller: (<b>a</b>) Force; (<b>b</b>) Force error.</p>
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<p>Experimental results of the triangular wave: (<b>a</b>) Force; (<b>b</b>) Force error.</p>
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<p>Experimental results of the sinusoidal response: (<b>a</b>) Force; (<b>b</b>) Force error.</p>
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10 pages, 4407 KiB  
Article
Implementation of Dual-Circuit System for Additional Power Supply Based on Photovoltaic Converters for Electric Vehicles
by Alexey Kolbasov, Rinat Kurmaev and Kirill Karpukhin
Energies 2019, 12(20), 4010; https://doi.org/10.3390/en12204010 - 22 Oct 2019
Cited by 4 | Viewed by 3074
Abstract
The article presents a process of designing the photovoltaic (PHV) converters system for an electric vehicle, shows the scheme of photovoltaic converters usage, the results of electric vehicle motion modeling with photovoltaic converters, and the results of road tests of an electric vehicle [...] Read more.
The article presents a process of designing the photovoltaic (PHV) converters system for an electric vehicle, shows the scheme of photovoltaic converters usage, the results of electric vehicle motion modeling with photovoltaic converters, and the results of road tests of an electric vehicle with an additional power source based on photovoltaic converters. The photovoltaic converters system and low-voltage system of an electric vehicle have a shared low-voltage battery, which allows the implementation of two schemes of electric vehicle power supply. Initially, the aggregate base was selected, then, taking into account the efficiency of each device included in the design of the new electric vehicle, mathematical modeling was carried out and showed good efficiency results of the photovoltaic converters system. Then, the prototype was manufactured and tested. The aggregate base included the battery of photovoltaic converters assembled in a certain way on the vehicle roof, the MPPT (maximum power point tracking) controller, the buffer storage device in the form of a 12 V battery, and the DC (direct current) converter that allows transmitting electricity from the buffer battery to the high-voltage system. Modeling of the electric vehicle motion considered typical operating modes, including energy costs for the operation of assistant systems of the electric vehicle, as well as including the consumption of low-voltage components. The tests were carried out according to the NEDC (New European Driving Cycle). As a result, implementation of photovoltaic converters with 21% efficiency allowed for the power reserve of the electric vehicle to be increased by up to 9%. Full article
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<p>Vehicles with photovoltaic (PHV) battery: (<b>a</b>) Ford C-MAX Solar Energy concept; (<b>b</b>) Pininfarina B Zero; (<b>c</b>) Volkswagen Space Up Blue (hydrogen-powered); (<b>d</b>) Sono Sion electric vehicle; (<b>e</b>) Solar Roof as an option that Toyota offers to Prius and Auris hybrids; (<b>f</b>) Fisker Karma hybrid vehicle.</p>
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<p>Statistics for solar power radiation and CO<sub>2</sub> emissions per 1 kWh of solar power produced.</p>
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<p>Recalculation of electricity consumption by an electric vehicle to the CO<sub>2</sub> emissions.</p>
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<p>General view of the electric vehicle with PHV designed and manufactured in Federal State Unitary Enterprise (FSUE) “NAMI”.</p>
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<p>Scheme of the dual-circuit system of photovoltaic converters.</p>
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<p>Electric vehicle model with the photovoltaic converters battery in Simulink (upper level of the model). Subsystem 1 creates two signals: energy density on the roof surface and on the hood surface; subsystem 2 contains different variables. Subsystem 3 contains the energy conversion model. Subsystem 4 describes the energy consumption of the low-voltage system. Subsystem 5 contains the service brake system model. Subsystem 6 is the electric motor model. Subsystem 7 is the high-voltage battery. Subsystem 9 is the gearbox model. Subsystem 8 and 10 are submodels of the electric vehicle speed control system. Subsystem 11 allows the forces of resistance to linear motion of the electric vehicle to be calculated.</p>
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<p>Calculated increase in electric vehicle mileage depending on the time of day and driving cycle. Day 196 (July). Mixed cycles: (<b>a</b>) clear sky; (<b>b</b>) cloudy sky. City cycles: (<b>c</b>) clear sky; (<b>d</b>) cloudy sky.</p>
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<p>Photovoltaic converters system diagram (section): 1—PHV; 2—protective glazing; 3—filler compound; 4—bypass diode.</p>
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<p>Testing the electric vehicles with the PHV system on the dynamometer road.</p>
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<p>Laboratory tests of the developed electric vehicle (on the left: test at the bench with running drums in the driving cycle mode; on the right: speed monitor).</p>
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<p>Readings of operation of the PHV system in the process of charging the buffer storage with solar energy.</p>
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21 pages, 2430 KiB  
Article
Control Strategy of Intergrated Photovoltaic-UPQC System for DC-Bus Voltage Stability and Voltage Sags Compensation
by Dongsheng Yang, Zhanchao Ma, Xiaoting Gao, Zhuang Ma and Enchang Cui
Energies 2019, 12(20), 4009; https://doi.org/10.3390/en12204009 - 22 Oct 2019
Cited by 16 | Viewed by 3195
Abstract
Power quality problem, because of its various forms and occurrence frequency, has become one of the most critical challenges confronted by a power system. Meanwhile, the development of renewable energy has led to more demands for an integrated system that combines both merits [...] Read more.
Power quality problem, because of its various forms and occurrence frequency, has become one of the most critical challenges confronted by a power system. Meanwhile, the development of renewable energy has led to more demands for an integrated system that combines both merits of sustainable energy generation and power quality improvement. In this context, this paper discusses an integrated photovoltaic-unified power quality conditioner (PV-UPQC) and its control strategy. The system is composed of a series compensator, shunt compensator, dc-bus, and photovoltaic array, which conducts an integration of photovoltaic generation and power quality mitigation. The fuzzy adaptive PI controller and the improved Maximum Power Point Tracking (MPPT) technique are proposed to enhance the stability of dc-bus voltage, which is aimed at the power balance and steady operation of the whole system. Additionally, the coordinate control strategy is studied in order to ensure the normal operation and compensation performance of the system under severe voltage sag condition. In comparison to the existing PV-UPQC system, the proposed control method could improve the performance of dc-bus stability and the compensation ability. The dynamic behavior of the integrated system were verified by simulation in MATLAB and PLECS. Selected results are reported to show that the dc-bus voltage was stable and increased under severe situations, which validates the effectiveness of the proposed integrated PV-UPQC system and its control strategy. Full article
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<p>The complete configuration of the proposed photovoltaic system integrated unified power quality conditioner.</p>
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<p>Control loop of the series compensator.</p>
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<p>Control loop of the shunt compensator.</p>
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<p>Flowchart of the improved MPPT (maximum power point tracking) technique.</p>
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<p>Control loop of the fuzzy adaptive-PI controller for the dc-bus.</p>
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<p>Active power flow through the proposed system under voltage sags. (<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mi>V</mi> <mi>s</mi> </msub> <mo>&lt;</mo> <msub> <mi>V</mi> <mi>L</mi> </msub> </mrow> </semantics></math> with <math display="inline"><semantics> <mrow> <msub> <mi>P</mi> <mi>L</mi> </msub> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>, (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mi>V</mi> <mi>s</mi> </msub> <mo>&lt;</mo> <msub> <mi>V</mi> <mi>L</mi> </msub> </mrow> </semantics></math> with <math display="inline"><semantics> <mrow> <msub> <mi>P</mi> <mi>L</mi> </msub> <mo>&lt;</mo> <msub> <mi>P</mi> <mrow> <mi>p</mi> <mi>v</mi> </mrow> </msub> </mrow> </semantics></math>, and (<b>c</b>) <math display="inline"><semantics> <mrow> <msub> <mi>V</mi> <mi>s</mi> </msub> <mo>&lt;</mo> <msub> <mi>V</mi> <mi>L</mi> </msub> </mrow> </semantics></math> with <math display="inline"><semantics> <mrow> <msub> <mi>P</mi> <mi>L</mi> </msub> <mo>&gt;</mo> <msub> <mi>P</mi> <mrow> <mi>p</mi> <mi>v</mi> </mrow> </msub> </mrow> </semantics></math>.</p>
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<p>Simulation results under voltage sag with <math display="inline"><semantics> <mrow> <msub> <mi>P</mi> <mi>L</mi> </msub> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>.</p>
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<p>Simulation results of harmonic and reactive power compensation with <math display="inline"><semantics> <mrow> <mn>0</mn> <mo>&lt;</mo> <msub> <mi>P</mi> <mi>L</mi> </msub> <mo>&lt;</mo> <msub> <mi>P</mi> <msub> <mi>p</mi> <mi>v</mi> </msub> </msub> </mrow> </semantics></math>.</p>
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<p>Total harmonic distortion (THD) analysis of grid and load current under conditions with <math display="inline"><semantics> <mrow> <mn>0</mn> <mo>&lt;</mo> <msub> <mi>P</mi> <mi>L</mi> </msub> <mo>&lt;</mo> <msub> <mi>P</mi> <msub> <mi>p</mi> <mi>v</mi> </msub> </msub> </mrow> </semantics></math>. (<b>a</b>) THD analysis of grid current and (<b>b</b>) THD analysis of load current.</p>
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<p>Simulation results under voltage swell with <math display="inline"><semantics> <mrow> <msub> <mi>P</mi> <mi>L</mi> </msub> <mo>&gt;</mo> <msub> <mi>P</mi> <mrow> <mi>p</mi> <mi>v</mi> </mrow> </msub> </mrow> </semantics></math>.</p>
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<p>Simulation results under loads sudden decrease.</p>
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<p>Simulation results under irradiation sudden increase.</p>
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<p>Comparison results of feedforward unit under condition of irradiation sudden decrease. (<b>a</b>) With a feedforward unit and (<b>b</b>) without a feedforward unit.</p>
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<p>Comparison results between conventional PI controller and fuzzy adaptive PI controller. (<b>a</b>) Conventional PI controller with <math display="inline"><semantics> <mrow> <msub> <mi>k</mi> <mi>p</mi> </msub> <mo>=</mo> <mn>2</mn> <mo>.</mo> <mn>5</mn> <mo>,</mo> <mspace width="0.166667em"/> <msub> <mi>k</mi> <mi>i</mi> </msub> <mo>=</mo> <mn>5</mn> </mrow> </semantics></math>, (<b>b</b>) conventional PI controller with <math display="inline"><semantics> <mrow> <msub> <mi>k</mi> <mi>p</mi> </msub> <mo>=</mo> <mn>2</mn> <mo>.</mo> <mn>5</mn> <mo>,</mo> <mspace width="0.166667em"/> <msub> <mi>k</mi> <mi>i</mi> </msub> <mo>=</mo> <mn>50</mn> </mrow> </semantics></math>, (<b>c</b>) conventional PI controller with <math display="inline"><semantics> <mrow> <msub> <mi>k</mi> <mi>p</mi> </msub> <mo>=</mo> <mn>10</mn> <mo>,</mo> <mspace width="0.166667em"/> <msub> <mi>k</mi> <mi>i</mi> </msub> <mo>=</mo> <mn>50</mn> </mrow> </semantics></math>, and (<b>d</b>) fuzzy adaptive PI controller.</p>
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<p>THD comparison between conventional PI controller and fuzzy adaptive PI controller. (<b>a</b>) Conventional PI controller with <math display="inline"><semantics> <mrow> <msub> <mi>k</mi> <mi>p</mi> </msub> <mo>=</mo> <mn>2</mn> <mo>.</mo> <mn>5</mn> <mo>,</mo> <mspace width="0.166667em"/> <msub> <mi>k</mi> <mi>i</mi> </msub> <mo>=</mo> <mn>5</mn> </mrow> </semantics></math>, (<b>b</b>) conventional PI controller with <math display="inline"><semantics> <mrow> <msub> <mi>k</mi> <mi>p</mi> </msub> <mo>=</mo> <mn>2</mn> <mo>.</mo> <mn>5</mn> <mo>,</mo> <mspace width="0.166667em"/> <msub> <mi>k</mi> <mi>i</mi> </msub> <mo>=</mo> <mn>50</mn> </mrow> </semantics></math>, (<b>c</b>) conventional PI controller with <math display="inline"><semantics> <mrow> <msub> <mi>k</mi> <mi>p</mi> </msub> <mo>=</mo> <mn>10</mn> <mo>,</mo> <mspace width="0.166667em"/> <msub> <mi>k</mi> <mi>i</mi> </msub> <mo>=</mo> <mn>50</mn> </mrow> </semantics></math>, and (<b>d</b>) fuzzy adaptive PI controller.</p>
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<p>Simulation results under voltage sag of 0.4 p.u.</p>
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<p>Simulation results under voltage sag of 0.8 p.u.</p>
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16 pages, 2932 KiB  
Article
A Mixed Uncertainty Power Flow Algorithm-Based Centralized Photovoltaic (PV) Cluster
by Hao Wu, Lin Zhou, Yihao Wan, Qiang Liu and Siyu Zhou
Energies 2019, 12(20), 4008; https://doi.org/10.3390/en12204008 - 22 Oct 2019
Cited by 4 | Viewed by 2787
Abstract
With the large-scale centralized PV clusters connected to grid, the grid power flow has certain randomness. Considering the fluctuation of PV output, an improved Krawczyk-Moore algorithm in a mixed coordinate system is proposed to solve the uncertain power flow problem. Firstly, aiming at [...] Read more.
With the large-scale centralized PV clusters connected to grid, the grid power flow has certain randomness. Considering the fluctuation of PV output, an improved Krawczyk-Moore algorithm in a mixed coordinate system is proposed to solve the uncertain power flow problem. Firstly, aiming at the special structure of a centralized PV cluster with only load node and no generator node, this paper proposes a power flow calculation in the mixed power flow coordinate, and then the Krawczyk-Moore operator is used to combine interval and affine arithmetic to overcome the shortcoming of over-conservative interval algorithm. Finally, the voltage operating condition under different volatility and different partial shading conditions is studied through the simulation of a practical example, and the out-of-limit voltage problem inside the centralized PV cluster is analyzed. Meanwhile, the effectiveness of the proposed algorithm is verified. Full article
(This article belongs to the Section F: Electrical Engineering)
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<p>Take the centralized PV cluster in Bayannur city of Inner Mongolia province as an example to describe. (<b>a</b>) Electrical wiring diagram of boost station; (<b>b</b>) Wiring diagram of centralized PV clusters. Abbreviations: PV, photovoltaic; PCC, point of common coupling.</p>
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<p>Take the centralized PV cluster in Bayannur city of Inner Mongolia province as an example to describe. (<b>a</b>) Electrical wiring diagram of boost station; (<b>b</b>) Wiring diagram of centralized PV clusters. Abbreviations: PV, photovoltaic; PCC, point of common coupling.</p>
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<p>Line impedance of the centralized PV cluster.</p>
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<p>Calculation process of the mixed power flow.</p>
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<p>Diagram of the centralized PV cluster simulation system.</p>
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<p>The voltage amplitude intervals of the PV power generation unit.</p>
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<p>The voltage phase angle intervals of the PV power generation unit.</p>
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<p>The voltage phase angle intervals under different volatility.</p>
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<p>The voltage amplitude intervals under different partial shading conditions.</p>
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16 pages, 2130 KiB  
Article
Research on the Real-Time Distributed Economic Dispatch Strategy for Microgrids
by Jian Le, Qian Zhou, Liangang Zhao and Yang Wang
Energies 2019, 12(20), 4007; https://doi.org/10.3390/en12204007 - 21 Oct 2019
Cited by 6 | Viewed by 2224
Abstract
A microgrid (MG) is one of the most efficient ways to cope with the grid-connection of a large number of small-sized distributed energy resources. This paper presents a consensus-based fully distributed economic dispatch (ED) strategy for MGs, with the aim of tackling the [...] Read more.
A microgrid (MG) is one of the most efficient ways to cope with the grid-connection of a large number of small-sized distributed energy resources. This paper presents a consensus-based fully distributed economic dispatch (ED) strategy for MGs, with the aim of tackling the difficulties of existing algorithms in modeling network power loss and providing global information. The external power grid to which the MG connects is treated as a special power source called a virtual generator, and participates in the economic dispatch process. Taking the incremental cost of a power generator as the consensus variable, a distributed ED model was formulated based on consensus protocol and a sub-gradient-based optimization method for solving this model has been proposed. The convergence of the distributed ED system was investigated by utilizing matrix spectrum radius analysis theory. The effectiveness of the proposed strategy was verified by carrying out simulation under normal operation of the MG, both with and without the consideration of network power loss. Moreover, simulation results under several scenarios, including exchanged power order variation and distributed generation plug and play, are provided to demonstrate the robustness of the distributed ED strategy. Full article
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<p>General structure of a microgrid (MG) with a virtual generator.</p>
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<p>Flow chat of the real-time distributed economic dispatch (ED) strategy.</p>
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<p>Topology of the communication network.</p>
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<p>Simulation results of scenario 1. (<b>a</b>) incremental cost; (<b>b</b>) power of each unit; (<b>c</b>) power information of the system.</p>
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<p>Simulation results of scenario 1. (<b>a</b>) incremental cost; (<b>b</b>) power of each unit; (<b>c</b>) power information of the system.</p>
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<p>Simulation results by using the distributed ED strategy in reference [<a href="#B30-energies-12-04007" class="html-bibr">30</a>]. (<b>a</b>) Incremental cost; (<b>b</b>) power of each unit; (<b>c</b>) power information of the system.</p>
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<p>Simulation results by using the distributed ED strategy in reference [<a href="#B30-energies-12-04007" class="html-bibr">30</a>]. (<b>a</b>) Incremental cost; (<b>b</b>) power of each unit; (<b>c</b>) power information of the system.</p>
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<p>Simulation results of scenario 2. (<b>a</b>) Incremental cost; (<b>b</b>) power of each unit; (<b>c</b>) power information of the system.</p>
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<p>Simulation results of scenario 2. (<b>a</b>) Incremental cost; (<b>b</b>) power of each unit; (<b>c</b>) power information of the system.</p>
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<p>Simulation results of scenario 4. (<b>a</b>) Incremental cost; (<b>b</b>) power of each unit; (<b>c</b>) power information of the system.</p>
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<p>Simulation results of scenario 4. (<b>a</b>) Incremental cost; (<b>b</b>) power of each unit; (<b>c</b>) power information of the system.</p>
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12 pages, 2964 KiB  
Article
Cost Uncertainties in Energy System Optimization Models: A Quadratic Programming Approach for Avoiding Penny Switching Effects
by Peter Lopion, Peter Markewitz, Detlef Stolten and Martin Robinius
Energies 2019, 12(20), 4006; https://doi.org/10.3390/en12204006 - 21 Oct 2019
Cited by 13 | Viewed by 3494
Abstract
Designing the future energy supply in accordance with ambitious climate change mitigation goals is a challenging issue. Common tools for planning and calculating future investments in renewable and sustainable technologies are often linear energy system models based on cost optimization. However, input data [...] Read more.
Designing the future energy supply in accordance with ambitious climate change mitigation goals is a challenging issue. Common tools for planning and calculating future investments in renewable and sustainable technologies are often linear energy system models based on cost optimization. However, input data and the underlying assumptions of future developments are subject to uncertainties that negatively affect the robustness of results. This paper introduces a quadratic programming approach to modifying linear, bottom-up energy system optimization models to take cost uncertainties into account. This is accomplished by implementing specific investment costs as a function of the installed capacity of each technology. In contrast to established approaches such as stochastic programming or Monte Carlo simulation, the computation time of the quadratic programming approach is only slightly higher than that of linear programming. The model’s outcomes were found to show a wider range as well as a more robust allocation of the considered technologies than the linear model equivalent. Full article
(This article belongs to the Special Issue Model Coupling and Energy Systems)
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<p>Investment cost range and deviations of onshore wind turbines, based on the IRENA (International Renewable Energy Agency) renewable cost database [<a href="#B24-energies-12-04006" class="html-bibr">24</a>].</p>
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<p>Comparison of the consideration of investment costs in the linear programming (LP) (red) and quadratic programming (QP) model (blue).</p>
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<p>Qualitative comparison of two similar technology shares in the optimized solution of the LP and QP model.</p>
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<p>Comparison of installed capacities in the results of the LP (red) and QP (blue) model for an 80% CO<sub>2</sub> emission reduction scenario for Germany.</p>
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<p>Sensitivity analysis of the investment costs for offshore wind turbines and its impact on the installed capacity in the cost-optimized energy system in the LP and QP model.</p>
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<p>Comparison of computation time of different LP and QP problems.</p>
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17 pages, 10038 KiB  
Article
Control Strategy of Three-Phase Inverter with Isolation Transformer
by Jie Chen, Jun Li, Ruichang Qiu and Zhigang Liu
Energies 2019, 12(20), 4005; https://doi.org/10.3390/en12204005 - 21 Oct 2019
Cited by 4 | Viewed by 3802
Abstract
In order to improve the control performance of a train auxiliary inverter and satisfy the requirements of power quality, harmonics, and unbalanced factor, this paper proposed a design method of a double closed-loop control system based on a complex state variable structure. The [...] Read more.
In order to improve the control performance of a train auxiliary inverter and satisfy the requirements of power quality, harmonics, and unbalanced factor, this paper proposed a design method of a double closed-loop control system based on a complex state variable structure. The method simplifies the design process and takes full account of the effects of coupling and discretization. In the current closed-loop process, this paper analyzed the limitations of the proportional integral (PI) controller and simplified to P controller. In the voltage closed-loop, the paper employed the PI controller plus the resonant controller, designed the parameters of the PI controller. and analyzed the optimal discretization method of the resonant controller under dq axis coupling. Finally, experiments and simulations were conducted to show that the proposed method can achieve the above improvements. Full article
(This article belongs to the Section F: Electrical Engineering)
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<p>Topology in the current paper.</p>
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<p>Vector relation of transformer. (<b>a</b>) Winding of transformer; (<b>b</b>) Vector relation of primary voltage and secondary voltage.</p>
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<p>Complex state variable structure.</p>
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<p>Voltage and current double-loop.</p>
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<p>Root locus diagrams with different parameters of <span class="html-italic">G</span><sub>i</sub>(s).</p>
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<p>Root locus diagrams with P controller. (<b>a</b>) Regardless of coupling; (<b>b</b>) Considering coupling.</p>
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<p>Bode diagram of current closed loop.</p>
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<p>Zero and pole distribution diagram of voltage closed loop without resonant controller. (<b>a</b>) Increase of integral coefficient; (<b>b</b>) Increase of proportional coefficient when integral coefficient is 200.</p>
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<p>Figures of three-level error of resonance point. (<b>a</b>) Type A; (<b>b</b>) Type B; (<b>c</b>) Type C; (<b>d</b>) Type D.</p>
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<p>Bode diagram of Types C and D. (<b>a</b>) ZOH/FOH/PRE; (<b>b</b>) ZPM/IMP.</p>
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<p>Bode diagram of Z(s). (<b>a</b>) Considering coupling; (<b>b</b>) Regardless of coupling.</p>
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<p>Sequence diagram of simulation system.</p>
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<p>Simulation waveform with instantaneous unbalanced load.</p>
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<p>Simulation waveform with non-linear load. (<b>a</b>) Without resonant controller; (<b>b</b>) With resonant controller.</p>
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<p>Voltage and current waveforms with instantaneous load. (<b>a</b>) Load input experiment; (<b>b</b>) Load removal experiment.</p>
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<p>Voltage and current waveforms with unbalanced load.</p>
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<p>Voltage and current waveforms with non-linear load. (<b>a</b>) Without resonant controller; (<b>b</b>) With resonant controller.</p>
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<p>Voltage and current waveforms with non-linear load. (<b>a</b>) Without resonant controller; (<b>b</b>) With resonant controller.</p>
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14 pages, 2329 KiB  
Article
Comparative Study on Game-Theoretic Optimum Sizing and Economical Analysis of a Networked Microgrid
by Liaqat Ali, S. M. Muyeen, Hamed Bizhani and Arindam Ghosh
Energies 2019, 12(20), 4004; https://doi.org/10.3390/en12204004 - 21 Oct 2019
Cited by 16 | Viewed by 3220
Abstract
In this paper, two techniques of game theory are considered for sizing and comparative analysis of a grid-connected networked microgrid, based on a multi-objective imperialistic competition algorithm (ICA) for system optimization. The selected networked microgrid, which consists of two different grid-connected microgrids with [...] Read more.
In this paper, two techniques of game theory are considered for sizing and comparative analysis of a grid-connected networked microgrid, based on a multi-objective imperialistic competition algorithm (ICA) for system optimization. The selected networked microgrid, which consists of two different grid-connected microgrids with common electrical load and main grid, might have different combinations of generation resources including wind turbine, photovoltaic panels, and batteries. The game theory technique of Nash equilibrium is developed to perform the effective sizing of the networked microgrid in which capacities of the generation resources and batteries are considered as players and annual profit as payoff. In order to meet the equilibrium point and the optimum sizes of generation resources, all possible coalitions between the players are considered; ICA, which is frequently used in optimization applications, is implemented using MATLAB software. Both techniques of game theory, Shapley values and Nash equilibrium, are used to find the annual profit of each microgrid, and results are compared based on optimum sizing, and maximum values of annual profit are identified. Finally, in order to validate the results of the networked microgrid, the sensitivity analysis is studied to examine the impact of electricity price and discount rates on maximum values of profit for both game theory techniques. Full article
(This article belongs to the Special Issue Smart Power & Internet Energy Systems)
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<p>Mount Magnet in Western Australia.</p>
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<p>Hourly profiles: (<b>a</b>) electrical load; (<b>b</b>) wind speed; (<b>c</b>) solar radiation.</p>
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<p>Block diagram of a networked microgrid.</p>
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<p>Imperialistic competition algorithm.</p>
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<p>Sensitivity analysis for game theory techniques: (<b>a</b>) Nash equilibrium; (<b>b</b>) Shapley values.</p>
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17 pages, 4918 KiB  
Article
Voltage Balance Switching Scheme for Series-Connected SiC MOSFET LLC Resonant Converter
by Hwa-Rang Cha and Rae-Young Kim
Energies 2019, 12(20), 4003; https://doi.org/10.3390/en12204003 - 21 Oct 2019
Cited by 3 | Viewed by 4207
Abstract
To achieve high efficiency and power density, silicon carbide (SiC)-based Inductor-Inductor-Capacitor (LLC) resonant converters are applied to the DC/DC converter stage of a solid-state transformer (SST). However, because the input voltage of an SST is higher than the rated voltage of a commercial [...] Read more.
To achieve high efficiency and power density, silicon carbide (SiC)-based Inductor-Inductor-Capacitor (LLC) resonant converters are applied to the DC/DC converter stage of a solid-state transformer (SST). However, because the input voltage of an SST is higher than the rated voltage of a commercial SiC device, it is essential to connect SiC devices in series. This structure is advantageous in terms of voltage rating, but a parasitic capacitance tolerance between series-connected SiC devices causes voltage imbalance. Such imbalance greatly reduces system stability as it causes overvoltage breakdown of SiC device. Therefore, this paper proposes a switching scheme to solve the voltage imbalance between SiC metal-oxide-semiconductor field-effect transistors (MOSFETs). The proposed scheme sequentially turns off series-connected SiC MOSFETs to compensate for the turn-off delays caused by parasitic capacitor tolerances. In addition, dead-time selection methods to achieve voltage balance and zero voltage switching simultaneously are provided in detail. To verify the effectiveness of the proposed scheme, experiments were conducted on a 2 kW series-connected SiC MOSFET LLC resonant converter prototype. Full article
(This article belongs to the Special Issue Advanced in Resonant Converter and Dual Active Bridge Converter)
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<p>The block diagram of solid-state transformer.</p>
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<p>Circuit diagram of the series-connected silicone carbide (SiC)-MOSFETs LLC resonant converter.</p>
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<p>Operational waveforms of the LLC resonant converter.</p>
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<p>Equivalent circuits for each mode: (<b>a</b>) Mode 1; (<b>b</b>) Mode 2; (<b>c</b>) Mode 3; (<b>d</b>) Mode 4; (<b>e</b>) Mode 5.</p>
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<p>Equivalent circuits for each mode: (<b>a</b>) Mode 1; (<b>b</b>) Mode 2; (<b>c</b>) Mode 3; (<b>d</b>) Mode 4; (<b>e</b>) Mode 5.</p>
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<p>Turn-on switching transient waveforms.</p>
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<p>Equivalent circuits during turn-on switching transient: (<b>a</b>) Interval 1; (<b>b</b>) Interval 2; (<b>c</b>) Interval 3.</p>
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<p>Turn-off switching waveforms: (<b>a</b>) with different input capacitances <span class="html-italic">C<sub>iss</sub></span>; (<b>b</b>) with different output capacitances <span class="html-italic">C<sub>oss</sub></span>.</p>
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<p>Turn-off switching waveforms: (<b>a</b>) without proposed switching scheme; (<b>b</b>) with proposed switching scheme.</p>
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<p>Equivalent circuit during dead-time.</p>
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<p>Test setup for series-connected SiC-MOSFETs LLC resonant converter.</p>
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<p>Experimental results with series-connected SiC MOSFETs without the proposed method: (<b>a</b>) <span class="html-italic">V<sub>in</sub></span> = 600 V; (<b>b</b>) <span class="html-italic">V<sub>in</sub></span> = 700 V; (<b>c</b>) <span class="html-italic">V<sub>in</sub></span> = 800 V.</p>
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<p>Experimental results with series-connected SiC MOSFETs with the proposed method: (<b>a</b>) <span class="html-italic">V<sub>in</sub></span> = 600 V; (<b>b</b>) <span class="html-italic">V<sub>in</sub></span> = 700 V; (<b>c</b>) <span class="html-italic">V<sub>in</sub></span> = 800 V.</p>
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<p>Comparison of the drain-source voltage of series-connected switches at different input voltages: (<b>a</b>) without the proposed scheme; (<b>b</b>) with the proposed scheme.</p>
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<p>Comparison of the maximum voltage imbalance between the series-connected switches at different input voltages.</p>
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15 pages, 4161 KiB  
Article
Research on Access Mode of the Flexible DC Power Distribution System into AC System
by Yao Liu, Jianfu Chen, Lu Qu, Zhanqing Yu, Zipan Nie and Rong Zeng
Energies 2019, 12(20), 4002; https://doi.org/10.3390/en12204002 - 21 Oct 2019
Cited by 4 | Viewed by 2635
Abstract
The connection mode of the direct current (DC) power distribution system and the alternating current (AC) system is the foundation of system design, and it is also one of key technologies of the DC power distribution network. Based on the topology structure, grounding [...] Read more.
The connection mode of the direct current (DC) power distribution system and the alternating current (AC) system is the foundation of system design, and it is also one of key technologies of the DC power distribution network. Based on the topology structure, grounding method, main equipment parameters, load parameters and system control protection strategy of the DC power distribution system, this paper establishes the system simulation model in the case of configuring the connection transformer and not configuring the connection transformer. Simulation results show that, when no connecting transformer is installed, the interaction between AC and DC systems will be great when faults occur, and the cost of converter valves and DC reactors will be increased. When connecting transformers are installed, the interaction between AC and DC systems can be effectively isolated, and the operation reliability of the system will be greatly improved while the cost is saved. Therefore, it is recommended to configure an independent connection transformer in the DC distribution system. Full article
(This article belongs to the Section F: Electrical Engineering)
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<p>System structure of a flexible DC power distribution system.</p>
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<p>Converter Topology of Tangjia Station and Jishan II Station.</p>
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<p>Topology of MMC based on Integrated Gate Commutated Thyristors (IGCT) cross-clamped.</p>
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<p>Equivalent circuit of capacitance discharge in the sub-module.</p>
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<p>Diagram of the DC distribution system with connection transformers.</p>
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<p>Voltage waveform across the neutral point grounding resistance.</p>
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<p>Current waveform flowing through the grounding resistance.</p>
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<p>Voltage waveform of the ground of the 10 kV AC bus.</p>
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<p>Voltage waveform of the AC bus under the single-pole grounding fault.</p>
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<p>Voltage waveform of the AC bus under the bipolar short-circuit fault.</p>
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<p>Diagram of the DC distribution system without connection transformers.</p>
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<p>Single-pole to ground voltage waveform of the DC line.</p>
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<p>Voltage and current waveform of grounding resistance.</p>
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<p>Relative ground voltage waveform of the 10 kV AC bus.</p>
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<p>Voltage waveform of the DC-side single-pole to ground.</p>
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<p>Voltage and current waveform of grounding resistance.</p>
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<p>Relative ground voltage waveform of the 10 kV AC bus.</p>
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18 pages, 2617 KiB  
Article
Multivariate Analysis to Relate CTOD Values with Material Properties in Steel Welded Joints for the Offshore Wind Power Industry
by Álvaro Presno Vélez, Antonio Bernardo Sánchez, Marta Menéndez Fernández and Zulima Fernández Muñiz
Energies 2019, 12(20), 4001; https://doi.org/10.3390/en12204001 - 21 Oct 2019
Cited by 6 | Viewed by 3542
Abstract
The increasingly mechanical requirements of offshore structures have established the relevance of fracture mechanics-based quality control in welded joints. For this purpose, crack tip opening displacement (CTOD) at a given distance from the crack tip has been considered one of the most suited [...] Read more.
The increasingly mechanical requirements of offshore structures have established the relevance of fracture mechanics-based quality control in welded joints. For this purpose, crack tip opening displacement (CTOD) at a given distance from the crack tip has been considered one of the most suited parameters for modeling and control of crack growth, and it is broadly used at the industrial level. We have modeled, through multivariate analysis techniques, the relationships among CTOD values and other material properties (such as hardness, chemical composition, toughness, and microstructural morphology) in high-thickness offshore steel welded joints. In order to create this model, hundreds of tests were done on 72 real samples, which were welded with a wide range of real industrial parameters. The obtained results were processed and evaluated with different multivariate techniques, and we established the significance of all the chosen explanatory variables and the good predictive capability of the CTOD tests within the limits of the experimental variation. By establishing the use of this model, significant savings can be achieved in the manufacturing of wind generators, as CTOD tests are more expensive and complex than the proposed alternatives. Additionally, this model allows for some technical conclusions. Full article
(This article belongs to the Special Issue Predicting the Future—Big Data and Machine Learning)
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<p>Sampling position. Color zones mark targeted areas for microstructural, hardness, and chemical analysis (red, green and blue).</p>
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<p>Correlation, kernel density estimation (KDE), and scatterplots (the trendline that best fit linear relation is represented in blue) among the different variables.</p>
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<p>Quantiles of input sample versus standard normal quantiles.</p>
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<p>Normal distribution of the residuals without variables. (<b>a</b>) Normal probability plot of residuals (<b>b</b>) Plot of residuals vs. fitted values.</p>
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<p>(<b>a</b>) Multivariate adaptive regression splines (MARS) model plot for two of the explanatory variables together with its knot locations (up) and (<b>b</b>) the analysis of variance (ANOVA) function for the pairs CTOD-M. Strength (left) and (<b>c</b>) CTOD-microstructure (right) (using ARESLab toolbox: Jekabsons G., ARESLab: Adaptive Regression Splines Toolbox for Matlab/Octave, 2016, available at <a href="http://www.cs.rtu.lv/jekabsons/" target="_blank">http://www.cs.rtu.lv/jekabsons/</a>).</p>
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<p>(<b>a</b>) Multivariate adaptive regression splines (MARS) model plot for two of the explanatory variables together with its knot locations (up) and (<b>b</b>) the analysis of variance (ANOVA) function for the pairs CTOD-M. Strength (left) and (<b>c</b>) CTOD-microstructure (right) (using ARESLab toolbox: Jekabsons G., ARESLab: Adaptive Regression Splines Toolbox for Matlab/Octave, 2016, available at <a href="http://www.cs.rtu.lv/jekabsons/" target="_blank">http://www.cs.rtu.lv/jekabsons/</a>).</p>
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25 pages, 3823 KiB  
Article
Thermodynamic Assessment and Multi-Objective Optimization of Performance of Irreversible Dual-Miller Cycle
by Shahriyar Abedinnezhad, Mohammad Hossein Ahmadi, Seyed Mohsen Pourkiaei, Fathollah Pourfayaz, Amir Mosavi, Michel Feidt and Shahaboddin Shamshirband
Energies 2019, 12(20), 4000; https://doi.org/10.3390/en12204000 - 21 Oct 2019
Cited by 14 | Viewed by 2936
Abstract
In this study, a new series of assessments and evaluations of the Dual-Miller cycle is performed. Furthermore, the specified output power and the thermal performance associated with the engine are determined. Besides, multi-objective optimization of thermal efficiency, ecological coefficient of performance (ECOP) and [...] Read more.
In this study, a new series of assessments and evaluations of the Dual-Miller cycle is performed. Furthermore, the specified output power and the thermal performance associated with the engine are determined. Besides, multi-objective optimization of thermal efficiency, ecological coefficient of performance (ECOP) and ecological function ( E u n ) by means of NSGA-II technique and thermodynamic analysis are presented. The Pareto optimal frontier obtaining the best optimum solution is identified by fuzzy Bellman-Zadeh, Linear Programming Technique for Multidimensional Analysis of Preference (LINMAP), and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) decision-making techniques. Based on the results, performances of dual-Miller cycles and their optimization are improved. For the results of the condition that (n < k) the best point has been LINMAP answer. The thermal efficiency for this point has been 0.5388. In addition, ECOP and E u n have been 1.6899 and 279.221, respectively. For the results of the condition that (n > k) the best point has been LINMAP and TOPSIS answer. The thermal efficiency for this point has been 0.5385. Also, ECOP and E u n have been 1.6875 and 279.7315, respectively. Furthermore, the errors are examined through comparison of the average and maximum errors of the two scenarios. Full article
(This article belongs to the Section J: Thermal Management)
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<p><span class="html-italic">T</span>-<span class="html-italic">S</span> diagram of Dual-Miller cycle (DMC): <span class="html-italic">n</span> less than the <span class="html-italic">k</span> (<b>a</b>) and <span class="html-italic">n</span> higher than the <span class="html-italic">k</span> (<b>b</b>) [<a href="#B102-energies-12-04000" class="html-bibr">102</a>].</p>
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<p>Impact of <span class="html-italic">n</span> (<span class="html-italic">n</span> &lt; <span class="html-italic">k</span>) on <span class="html-italic">P</span><sub>1</sub>–<span class="html-italic">ε</span> (<b>a</b>), <span class="html-italic">η</span><sub>1</sub><span class="html-italic">–ε</span> (<b>b</b>) and <span class="html-italic">P</span><sub>1</sub>–<span class="html-italic">η</span><sub>1</sub> (<b>c</b>) relations.</p>
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<p>Effect of <span class="html-italic">ρ</span> on <span class="html-italic">P</span><sub>1</sub> versus ε (<span class="html-italic">n</span> = 1.2).</p>
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<p>Impact of (<span class="html-italic">n</span> &lt; <span class="html-italic">k</span>) <span class="html-italic">n</span> on <span class="html-italic">E<sub>un</sub></span>–<span class="html-italic">P</span><sub>1</sub> (<b>a</b>) and <span class="html-italic">E<sub>un</sub></span>–<span class="html-italic">η</span><sub>1</sub> (<b>b</b>).</p>
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<p>Impact of <span class="html-italic">n</span> (<span class="html-italic">n</span> &lt; <span class="html-italic">k</span>) on ecological coefficient of performance (ECOP)–<span class="html-italic">ε</span> (<b>a</b>), ECOP–<span class="html-italic">P</span><sub>1</sub> (<b>b</b>) and ECOP–<span class="html-italic">η</span><sub>1</sub> (<b>c</b>).</p>
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<p>Impact of <span class="html-italic">n</span> (<span class="html-italic">n</span> &gt; <span class="html-italic">k</span>) on P<sub>2</sub>–ε (<b>a</b>), <span class="html-italic">η</span><sub>2</sub>–ε (<b>b</b>) and P<sub>2</sub>–<span class="html-italic">η</span><sub>2</sub> (<b>c</b>).</p>
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<p>Effect of <span class="html-italic">ρ</span> on <span class="html-italic">P</span><sub>2</sub> against ε (<span class="html-italic">n</span> = 1.6).</p>
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<p>Impact of <span class="html-italic">n</span> (<span class="html-italic">n &gt; k</span>) on <span class="html-italic">E<sub>un</sub></span>–<span class="html-italic">P</span><sub>2</sub> (<b>a</b>) and <span class="html-italic">E<sub>un</sub></span>–<span class="html-italic">η</span><sub>2</sub> (<b>b</b>).</p>
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<p>Impact of <span class="html-italic">n</span> (<span class="html-italic">n &gt; k</span>) on <span class="html-italic">E<sub>un</sub></span>–<span class="html-italic">P</span><sub>2</sub> (<b>a</b>) and <span class="html-italic">E<sub>un</sub></span>–<span class="html-italic">η</span><sub>2</sub> (<b>b</b>).</p>
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<p>Effect of <span class="html-italic">n</span> (<span class="html-italic">n &gt; k</span>) on ECOP−ε (<b>a</b>), ECOP–<span class="html-italic">P</span><sub>2</sub> (<b>b</b>) and ECOP<span class="html-italic">–</span><span class="html-italic">η</span><sub>2</sub> (<b>c</b>).</p>
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<p>Effect of <span class="html-italic">n</span> (<span class="html-italic">n &gt; k</span>) on ECOP−ε (<b>a</b>), ECOP–<span class="html-italic">P</span><sub>2</sub> (<b>b</b>) and ECOP<span class="html-italic">–</span><span class="html-italic">η</span><sub>2</sub> (<b>c</b>).</p>
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<p>Distribution of the Pareto optimal frontier.</p>
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<p>Distribution of the Pareto optimal frontier.</p>
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18 pages, 8663 KiB  
Article
Design of an Electromagnetic Variable Valve Train with a Magnetorheological Buffer
by He Guo, Liang Liu, Xiangbin Zhu, Siqin Chang and Zhaoping Xu
Energies 2019, 12(20), 3999; https://doi.org/10.3390/en12203999 - 21 Oct 2019
Cited by 8 | Viewed by 3481
Abstract
In this paper, an electromagnetic variable valve train with a magnetorheological buffer (EMVT with MR buffer) is proposed. This system is mainly composed of an electromagnetic linear actuator (EMLA) and a magnetorheological buffer (MR buffer). The valves of an internal combustion engine are [...] Read more.
In this paper, an electromagnetic variable valve train with a magnetorheological buffer (EMVT with MR buffer) is proposed. This system is mainly composed of an electromagnetic linear actuator (EMLA) and a magnetorheological buffer (MR buffer). The valves of an internal combustion engine are driven by the EMLA directly to open and close, which can adjust the valve lift and phase angle of the engine. At the same time, MR buffer can reduce the seat velocity of the valve and realize the seat buffer of the electromagnetic variable valve. In this paper, the overall design scheme of the system is proposed and the structure design, finite element simulation of the EMLA, and the MR buffer are carried out. The electromagnetic force characteristics of the EMLA and buffer force of the MR buffer are measured, and the seat buffering performance is verified as well. Experiments and simulation results show that the electromagnetic force of the EMLA can reach 320.3 N when the maximum coil current is 40 A. When the current of the buffer coil is 2.5 A and the piston’s motion frequency is 5 Hz, the buffering force can reach 35 N. At the same time, a soft landing can be realized when the valve is seated. Full article
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<p>Structure of the EMVT with the MR buffer.</p>
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<p>Magnetic field distribution of the EMLA: (<b>a</b>) Finite element model; (<b>b</b>) Magnetic field distribution.</p>
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<p>Electromagnetic force characteristic curve of the EMLA.</p>
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<p>Structure of the MR buffer.</p>
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<p>Magnetization characteristic curve of the MR fluid.</p>
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<p>Magnetic field distribution of the MR buffer.</p>
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<p>Magnetic induction intensity distribution curve in the annular gap.</p>
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<p>Multi-physical field model of the MR buffer.</p>
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<p>Fluid flow and velocity distribution of the MR fluid.</p>
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<p>Velocity distribution map of the MR fluid when the current is 0 A.</p>
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<p>Velocity distribution map of the MR fluid when the current is 2.5 A.</p>
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<p>Dynamic viscosity distribution map of the MR fluid: (<b>a</b>) I = 1 A; (<b>b</b>) I = 2.5 A.</p>
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<p>Experimental bench for the electromagnetic force characteristic.</p>
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<p>Electromagnetic force characteristic curve of the EMLA.</p>
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<p>Experimental bench for the buffer force measurement.</p>
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<p>Displacement–velocity curve of the MR buffer.</p>
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<p>Buffer force curve at 1 Hz frequency.</p>
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<p>Buffer force curve at 3 Hz frequency.</p>
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<p>Buffer force curve at 5 Hz frequency.</p>
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<p>Valve seating test bench.</p>
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<p>Experimental result of seating performance when the valve lift is 8 mm.</p>
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15 pages, 7182 KiB  
Article
Computational Analysis of the Performance of a Vertical Axis Turbine in a Water Pipe
by Honggu Yeo, Woochan Seok, Soyong Shin, Young Cheol Huh, Byung Chang Jung, Cheol-Soo Myung and Shin Hyung Rhee
Energies 2019, 12(20), 3998; https://doi.org/10.3390/en12203998 - 21 Oct 2019
Cited by 8 | Viewed by 2850
Abstract
In this study, a computational analysis was performed for a vertical-axis turbine which was installed in a water pipe to collect unused energy from the flow inside the pipe. The optimized operating conditions of the turbine were identified by comparing the energy-collecting performance [...] Read more.
In this study, a computational analysis was performed for a vertical-axis turbine which was installed in a water pipe to collect unused energy from the flow inside the pipe. The optimized operating conditions of the turbine were identified by comparing the energy-collecting performance obtained at different tip-speed ratios (TSRs). The turbine achieved the maximum efficiency of 22% at a TSR of 2.4 and collected 33 kW. Additional analyses were conducted to verify the effects of tip clearance, which is the distance between the turbine blades and the pipe wall, which showed that a higher efficiency was obtained with a smaller tip clearance. We also verified the effects of the turbine’s operating conditions and tip clearance on the flow field around the blades and wake of the turbine. Full article
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<p>A schematic view of the drain system.</p>
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<p>Turbine design: (<b>a</b>) side view and (<b>b</b>) top view.</p>
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<p>Coordinate system and boundary conditions.</p>
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<p>Vertical axis turbine installed in a water pipe: (<b>a</b>) side view and (<b>b</b>) top view.</p>
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<p>Computational domains and mesh.</p>
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<p>Physics quantity interpolation between the arbitrary mesh interface.</p>
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<p>Detailed view of the tip clearance and domain interface: (<b>a</b>) 19.3% clearance, (<b>b</b>) 15.0% clearance and (<b>c</b>) 12.0% clearance.</p>
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<p>Computational domain for the Darrieus turbine: (<b>a</b>) side view and (<b>b</b>) top view.</p>
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<p>Time histories of <span class="html-italic">C<sub>P</sub></span> of the Darrieus turbine at various tip-speed ratios (TSRs).</p>
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<p>Averaged power coefficients of the Darrieus turbine at various TSRs.</p>
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<p>Time history of torque on the turbine.</p>
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<p>Schematic diagram of the angle of attack and force acting on the cross section of the turbine blade.</p>
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<p>Averaged power coefficient of the spherical turbine with respect to TSR.</p>
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<p>Y-vorticity contours and streamlines on y = 0.1D_turbine at (<b>a</b>) TSR = 1.41, (<b>b</b>) TSR = 2.00, (<b>c</b>) = TSR = 2.40 and (<b>d</b>) TSR = 3.51.</p>
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<p>Zoom on Blade 1 at (<b>a</b>) TSR = 1.41, (<b>b</b>) TSR = 2.00, (<b>c</b>) = TSR = 2.40 and (<b>d</b>) TSR = 3.51.</p>
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<p>Power coefficient of the turbine with respect to tip clearance.</p>
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<p>Y-vorticity contours on y = 0.1D_turbine at TSR = 2.4 with a tip clearance of (<b>a</b>) 19.3%, (<b>b</b>) 15.0% and (<b>c</b>) 12.0%.</p>
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<p>Wake flow streamlines configuration with tip clearance: (<b>a</b>) 19.3%, (<b>b</b>) 15.0% and (<b>c</b>) 12.0%, respectively.</p>
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16 pages, 2878 KiB  
Article
Experimental Study of the Influence of Natural Gas Constituents on CO Emission from Chinese Gas Cooker
by Pengfei Duan, Chaokui Qin and Zhiguang Chen
Energies 2019, 12(20), 3997; https://doi.org/10.3390/en12203997 - 21 Oct 2019
Cited by 3 | Viewed by 3153
Abstract
In China, it has become a more common practice to introduce natural gases from different sources into the same distribution system to improve supply security and reliability. Variable gas constituents may cause a negative impact on the performance of domestic gas appliances. This [...] Read more.
In China, it has become a more common practice to introduce natural gases from different sources into the same distribution system to improve supply security and reliability. Variable gas constituents may cause a negative impact on the performance of domestic gas appliances. This paper aims to study the CO emission of a Chinese gas cooker under different constituents of natural gas. A typical Chinese gas cooker with two burners, each of which has a nominal heat input of 3.8 kW, was selected. One of the burners was modified to a forced-mixed mode to replace primary air injection. Within operational ranges corresponding to the permissible Wobbe index—namely, primary air coefficients and heat inputs—the equivalence between original gas and the CH4/C3H8/N2 three-component mixture in terms of CO emission was experimentally validated. Then, different three-component mixtures were input into the other unmodified burner, which operates under injected primary air, to investigate how the CO emission changed with different gas constituents. It was found that the CO emission of a natural gas and a CH4/C3H8/N2 three-component mixture, in terms of CO emission, were equivalent. The combination of the two indexes, W and PN, can describe the CO emission from a gas cooker accurately. By means of a three-component mixture, the empirical formula, which can correlate CO and the gas property parameters, was proposed. A set of equal-CO lines was revealed for a given initial primary air adjustment. Finally, a feasible approach to manage gas quality management in China was put forward, and the conclusion can help control the CO emission of gas cookers and improve indoor air quality. Full article
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<p>Two burners to be tested. (<b>a</b>) original atmospheric burner with injectors; (<b>b</b>) modified forced-mixed burner.</p>
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<p>Schematic illustration of test rig; the three sub-systems are shown in the red dash boxes.</p>
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<p>Comparison of CO emission fueled by ET13, E13, and 12T-0 under several primary air coefficients (<b>a</b>) <span class="html-italic">α′</span> = 0.40, (<b>b</b>) <span class="html-italic">α′</span> = 0.44, (<b>c</b>) <span class="html-italic">α′</span> = 0.48, and (<b>d</b>) <span class="html-italic">α′</span> = 0.52.</p>
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<p>Comparison of CO emission fueled by ET13, E13, and 12T-0 under several primary air coefficients (<b>a</b>) <span class="html-italic">α′</span> = 0.40, (<b>b</b>) <span class="html-italic">α′</span> = 0.44, (<b>c</b>) <span class="html-italic">α′</span> = 0.48, and (<b>d</b>) <span class="html-italic">α′</span> = 0.52.</p>
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<p>Three-dimensional view of CO emission of <span class="html-italic">E</span>8 and <span class="html-italic">ET</span>8.</p>
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<p>Images of the flame: (<b>a</b>) “soft” flame; (<b>b</b>) satisfactory flame; (<b>c</b>) satisfactory flame; (<b>d</b>) “hard” flame.</p>
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<p>Distribution of testing three-component gases and experimental data of CO emission.</p>
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<p>CO emission changing with <span class="html-italic">PN</span> under constant <span class="html-italic">Q.</span></p>
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<p>CO emission changing with <span class="html-italic">W</span> under constant <span class="html-italic">PN</span> number.</p>
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<p>Equal-CO lines of tested cooker, the thickening line is a 500-ppm equal-CO line, and the Chinese National Standard [<a href="#B24-energies-12-03997" class="html-bibr">24</a>] stipulates that CO emission from cooker should not exceed 500 ppm.</p>
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14 pages, 1026 KiB  
Article
Harvesting Scenedesmus obliquus via Flocculation of Moringa oleifera Seed Extract from Urban Wastewater: Proposal for the Integrated Use of Oil and Flocculant
by Alejandro Ruiz-Marin, Yunuen Canedo-Lopez, Asteria Narvaez-Garcia, José del Carmen Zavala-Loría, Luis Alonso Dzul-López, María Luisa Sámano-Celorio, Jorge Crespo-Álvarez, Eduardo García-Villena and Pablo Agudo-Toyos
Energies 2019, 12(20), 3996; https://doi.org/10.3390/en12203996 - 21 Oct 2019
Cited by 6 | Viewed by 2931
Abstract
The objectives this study were to examine the integrated use of oil–coagulant for the direct extraction of coagulant from Moringa oleifera (MO) with 5% and 10% (NH4)2SO4 extractor solution to harvest Scenedesmus obliquus cultivated in urban wastewater and [...] Read more.
The objectives this study were to examine the integrated use of oil–coagulant for the direct extraction of coagulant from Moringa oleifera (MO) with 5% and 10% (NH4)2SO4 extractor solution to harvest Scenedesmus obliquus cultivated in urban wastewater and to analyze the oil extracted from MO and S. obliquus. An average content of 0.47 g of coagulant and 0.5 g of oil per gram of MO was obtained. Highly efficient algal harvest, 80.33% and 72.13%, was achieved at a dose of 0.38 g L−1 and pH 8–9 for 5% and 10% extractor solutions, respectively. For values above pH 9, the harvest efficiency decreases, producing a whitish water with 10% (NH4)2SO4 solution. The oil profile (MO and S. obliquus) showed contents of SFA of 36.24–36.54%, monounsaturated fatty acids of 32.78–36.13%, and polyunsaturated fatty acids of 27.63–30.67%. The biodiesel obtained by S. obliquus and MO has poor cold flow properties, indicating possible applications limited to warm climates. For both biodiesels, good fuel ignition was observed according to the high cetane number and positive correlation with SFA and negative correlation with the degree of saturation. This supports the use of MO as a potentially harmless bioflocculant for microalgal harvest in wastewater, contributing to its treatment, and a possible source of low-cost biodiesel. Full article
(This article belongs to the Section B: Energy and Environment)
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<p>Jar test, dose determination, and optimal pH during the algal flocculation with urban wastewater: (<b>a</b>) Oil extraction from <span class="html-italic">Moringa oleifera</span> seed; (<b>b</b>) flocculation with microalgae culture; and (<b>c</b>) flocculation with urban wastewater.</p>
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<p>Linearity of the oil content (g) and biomass of <span class="html-italic">Moringa oleifera</span>.</p>
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21 pages, 3342 KiB  
Article
Optimal Operation for Economic and Exergetic Objectives of a Multiple Energy Carrier System Considering Demand Response Program
by Yu Huang, Shuqin Li, Peng Ding, Yan Zhang, Kai Yang and Weiting Zhang
Energies 2019, 12(20), 3995; https://doi.org/10.3390/en12203995 - 21 Oct 2019
Cited by 21 | Viewed by 2693
Abstract
An MECS (multiple energy carrier system) could meet diverse energy needs owing to the integration of different energy carriers, while the distinction of quality of different energy resources should be taken into account during the operation stage, in addition the economic principle. Hence, [...] Read more.
An MECS (multiple energy carrier system) could meet diverse energy needs owing to the integration of different energy carriers, while the distinction of quality of different energy resources should be taken into account during the operation stage, in addition the economic principle. Hence, in this paper, the concept of exergy is adopted to evaluate each energy carrier, and an economic–exergetic optimal scheduling model is formulated into a mixed integer linear programming (MILP) problem with the implementation of a real-time pricing (RTP)-based demand response (DR) program. Moreover, a multi-objective (MO) operation strategy is applied to this scheduling model, which is divided into two parts. First, the ε-constraint method is employed to cope with the MILP problem to obtain the Pareto front by using the state-of-the-art CPLEX solver under the General Algebraic Modeling System (GAMS) environment. Then, a preferred solution selection strategy is introduced to make a trade-off between the economic and exergetic objectives. A test system is investigated on a typical summer day, and the optimal dispatch results are compared to validate the effectiveness of the proposed model and MO operation strategy with and without DR. It is concluded that the MECS operator could more rationally allocate different energy carriers and decrease energy cost and exergy input simultaneously with the consideration of the DR scheme. Full article
(This article belongs to the Section A1: Smart Grids and Microgrids)
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Graphical abstract

Graphical abstract
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<p>An MECS (multiple energy carrier system) coupled with diverse energy carriers.</p>
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<p>Flowchart of multi-objective (MO) operation strategy.</p>
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<p>Energy demands of a typical summer day: (<b>a</b>) electricity demand and (<b>b</b>) heat and cooling demands.</p>
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<p>TOU (time of use) and RTP (real-time pricing) tariffs.</p>
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<p>Ambient temperature and solar radiation.</p>
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<p>Pareto front with and without demand response (DR).</p>
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<p>The purchased electricity and natural gas: (<b>a</b>) purchased electricity; (<b>b</b>) purchased gas.</p>
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<p>Optimal scheduling results for electricity: (<b>a</b>) without DR; (<b>b</b>) with DR.</p>
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<p>Natural gas distribution ratio: (<b>a</b>) without DR; (<b>b</b>) with DR.</p>
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<p>Optimal scheduling results for heat: (<b>a</b>) without DR; (<b>b</b>) with DR.</p>
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<p>Heat balance for each device: (<b>a</b>) without DR; (<b>b</b>) with DR.</p>
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<p>Optimal scheduling results for cooling: (<b>a</b>) without DR; (<b>b</b>) with DR.</p>
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<p>State variation of energy storage devices: (<b>a</b>) state of TES; (<b>b</b>) state of CES.</p>
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<p>Values of <span class="html-italic">Ps</span> and <span class="html-italic">Pc</span> at each hour.</p>
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<p>Load profile of DR for each configuration: (<b>a</b>) Configurations 1–11 and (<b>b</b>) Configurations 12–21.</p>
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<p>Energy cost, exergy input, exergy efficiency and energy efficiency for each configuration. (<b>a</b>) Energy cost and exergy input for Configurations 1–11; (<b>b</b>) energy cost and exergy input for Configurations 12–21; (<b>c</b>) exergy efficiency and energy efficiency for Configurations 1–11; (<b>d</b>) exergy efficiency and energy efficiency for Configurations 12–21.</p>
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<p>Energy cost, exergy input, exergy efficiency and energy efficiency for each configuration. (<b>a</b>) Energy cost and exergy input for Configurations 1–11; (<b>b</b>) energy cost and exergy input for Configurations 12–21; (<b>c</b>) exergy efficiency and energy efficiency for Configurations 1–11; (<b>d</b>) exergy efficiency and energy efficiency for Configurations 12–21.</p>
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26 pages, 9946 KiB  
Article
Fault Ride-Through Enhancement of Grid Supporting Inverter-Based Microgrid Using Delayed Signal Cancellation Algorithm Secondary Control
by Elutunji Buraimoh, Innocent E. Davidson and Fernando Martinez-Rodrigo
Energies 2019, 12(20), 3994; https://doi.org/10.3390/en12203994 - 21 Oct 2019
Cited by 23 | Viewed by 4950
Abstract
The growing level of grid-connected renewable energy sources in the form of microgrids has made it highly imperative for grid-connected microgrids to contribute to the overall system stability. Consequently, secondary services which include the fault ride-through (FRT) capability are expected to be possessed [...] Read more.
The growing level of grid-connected renewable energy sources in the form of microgrids has made it highly imperative for grid-connected microgrids to contribute to the overall system stability. Consequently, secondary services which include the fault ride-through (FRT) capability are expected to be possessed characteristics by inverter-based microgrids. This enhances the stable operation of the main grid and sustained microgrid grid interconnection during grid faults in conformity with the emerging national grid codes. This paper proposes an effective FRT secondary control strategy to coordinate power injection during balanced and unbalanced fault conditions. This complements the primary control to form a two-layer hierarchical control structure in the microgrids. The primary level is comprised of voltage/power and current inner loops fed by a droop control. The droop control coordinates grid power-sharing amongst the voltage source inverters. When a fault occurs, the participating inverters operate to support the grid voltage, by injecting supplementary reactive power based on their droop gains. Similarly, under unbalanced voltage condition due to asymmetrical faults in the grid, the proposed secondary control ensures the positive sequence component compensation and negative and zero sequence components clearance using a delayed signal cancellation (DSC) algorithm and power electronic switched series impedance placed in-between the point of common coupling (PCC) and the main grid. While ensuring that FRT ancillary service is rendered to the main utility, the strategy proposed ensures relatively interrupted quality power is supplied to the microgrid load. Consequently, this strategy ensures the microgrid ride-through the voltage sag and supports the grid utility voltage during the period of the main utility grid fault. Results of the study are presented and discussed. Full article
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<p>FRT/LVRT curves defined by Spanish grid code [<a href="#B26-energies-12-03994" class="html-bibr">26</a>].</p>
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<p>Grid-connected inverter interfaced DER primary control consisting droop, voltage, and current control loops.</p>
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<p>Topology of the MG examined.</p>
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<p>Reactive currents injection/absorbed during faults, according to Spanish network code P.O. 12.2 Spanish grid code requirements [<a href="#B45-energies-12-03994" class="html-bibr">45</a>].</p>
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<p>Proposed FRT control scheme.</p>
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<p>Power flow diagram between the inverter and host grid.</p>
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<p>Phasor diagram of reactive current injection under transient condition.</p>
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<p>DER active power, voltage, current, and frequency under changing power set-points.</p>
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<p>Pulses generated in all three-phases under L-L-L-G.</p>
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<p>Voltages in the grid and microgrid at grid voltage sag of 50% produced by L-L-L-G fault.</p>
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<p>Current waveforms in the microgrid at grid voltage sag of 50% produced by L-L-L-G fault.</p>
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<p>Power supplied by DER 1 and DER 2 under voltage sag of 50% produced by L-L-L-G fault.</p>
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<p>Power supply to local microgrid load under voltage sag of 50% produced by L-L-L-G fault.</p>
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<p>Power generated under a voltage sag of 20% produced by L-L-L-G fault.</p>
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<p>Grid voltages under L-G fault on the main grid.</p>
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<p>Microgrid voltages under L-G fault on the main grid.</p>
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<p>Grid and microgrid current under L-G fault on the main grid.</p>
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<p>Power supplied by DER 1 and DER 2 under L-G fault.</p>
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<p>Power supplied to the local microgrid load under L-G fault.</p>
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19 pages, 2834 KiB  
Article
Numerical Study of Nacelle Wind Speed Characteristics of a Horizontal Axis Wind Turbine under Time-Varying Flow
by Xiaodong Wang, Yunong Liu, Luyao Wang, Lin Ding and Hui Hu
Energies 2019, 12(20), 3993; https://doi.org/10.3390/en12203993 - 20 Oct 2019
Cited by 10 | Viewed by 3219
Abstract
Nacelle wind speed transfer function (NTF) is usually used for power prediction and operational control of a horizontal axis wind turbine. Nacelle wind speed exhibits high instability as it is influenced by both incoming flow and near wake of a wind turbine rotor. [...] Read more.
Nacelle wind speed transfer function (NTF) is usually used for power prediction and operational control of a horizontal axis wind turbine. Nacelle wind speed exhibits high instability as it is influenced by both incoming flow and near wake of a wind turbine rotor. Enhanced understanding of the nacelle wind speed characteristics is critical for improving the accuracy of NTF. This paper presents Reynolds-averaged Navier–Stokes (RANS) simulation results obtained for a multi-megawatt wind turbine under both stable and dynamic incoming flows. The dynamic inlet wind speed varies in the form of simplified sinusoidal and superposed sinusoidal functions. The simulation results are analyzed in time and frequency domains. For a stable inlet flow, the variation of nacelle wind speed is mainly influenced by the blade rotation. The influence of wake flow shows high frequency characteristics. The results with stable inlet flow show that the reduction of the nacelle wind speed with respect to the inlet wind speed is overestimated for low wind speed condition, and underestimated for high wind speed condition. Under time-varing inflow conditions, for the time scale and fluctuation amplitude subject to the International Electrotechnical Commission (IEC) standard, the nacelle wind speed is mainly influenced by the dynamic inflow. The variation of inflow can be recovered by choosing a suitable low pass filter. The work in this paper demonstrates the potential for building accurate NTF based on Computational Fluid Dynamic (CFD) simulations and signal analysis. Full article
(This article belongs to the Section A3: Wind, Wave and Tidal Energy)
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<p>Computational model.</p>
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<p>Computational mesh of wind turbine.</p>
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<p>Computational mesh of the wind turbine. (<b>a</b>) Mesh in rotational domain. (<b>b</b>) Mesh on the rotor-nacelle surface.</p>
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<p>Variations of the inlet wind speed. (<b>a</b>) Case 4. (<b>b</b>) Case 5. (<b>c</b>) Case 6.</p>
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<p>Verification of nacelle wind speed. (<b>a</b>) Mesh independence studies. (<b>b</b>) Turbulence model independence studies.</p>
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<p>Validation of power.</p>
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<p>Variation of <span class="html-italic">V<sub>N</sub></span> respecting the blade azimuth angle with stable inlet flow. (<b>a</b>) Case 1 (Stable 5 m/s). (<b>b</b>) Case 2 (Stable 9 m/s). (<b>c</b>) Case 3 (Stable 13 m/s).</p>
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<p>Variation of <span class="html-italic">V<sub>N</sub></span> in frequency domain with stable inlet flow. (<b>a</b>) Case 1 (Stable 5 m/s). (<b>b</b>) Case 2 (Stable 9 m/s). (<b>c</b>) Case 3 (Stable 13 m/s).</p>
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<p>Variation of <span class="html-italic">V<sub>N</sub></span> respecting the blade azimuth angle with dynamic inlet flow. (<b>a</b>) Case 4 (9 m/s, 0.5 Hz). (<b>b</b>) Case 5 (9 m/s, 1.0 Hz). (<b>c</b>) Case 6 (9 m/s, 0.5 Hz + 1.0 Hz).</p>
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<p>Variation of <span class="html-italic">V<sub>N</sub></span> in frequency domain with dynamic inlet flow. (<b>a</b>) Case 4. (<b>b</b>) Case 5. (<b>c</b>) Case 6.</p>
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<p>NTF curves with stable or dynamic inlet flow.</p>
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<p>Variation of <span class="html-italic">V<sub>N</sub></span> at low wind speed, 5 m/s. (<b>a</b>) Time domain. (<b>b</b>) Frequency domain.</p>
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<p>Variation of <span class="html-italic">V<sub>N</sub></span> at high wind speed, 13 m/s. (<b>a</b>) Time domain. (<b>b</b>) Frequency domain.</p>
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<p>Comparison between inlet wind speed and nacelle wind speed. (<b>a</b>) No filtering. (<b>b</b>) Blade rotational frequency filtered. (<b>c</b>) Blade rotational frequency and high frequencies filtered.</p>
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18 pages, 7240 KiB  
Article
An Investigation into Sub-Critical Choke Flow Performance in High Rate Gas Condensate Wells
by Hamid Reza Nasriani, Khalid Khan, Tony Graham, Shephard Ndlovu, Mehrdad Nasriani, Jianqiang Mai and Mohammad Rafie Rafiee
Energies 2019, 12(20), 3992; https://doi.org/10.3390/en12203992 - 20 Oct 2019
Cited by 5 | Viewed by 3211
Abstract
There have been some correlations in the literature to predict the gas and liquid flow rate through wellhead chokes under subcritical flow conditions. The majority of these empirical correlations have been developed based on limited production data sets that were collected from a [...] Read more.
There have been some correlations in the literature to predict the gas and liquid flow rate through wellhead chokes under subcritical flow conditions. The majority of these empirical correlations have been developed based on limited production data sets that were collected from a small number of fields. Therefore, these correlations are valid within the parameter variation ranges of those fields. If such correlations are used elsewhere for the prediction of the subcritical choke flow performance of the other fields, significant errors will occur. Additionally, there are only a few empirical correlations for sub-critical choke flow performance in high rate gas condensate wells. These led the authors to develop a new empirical correlation based on a wider production data set from different gas condensate fields in the world; 234 production data points were collected from a large number of production wells in twenty different gas condensate fields with diverse reservoir conditions and different production histories. A non-linear regression analysis method was applied to their production. The new correlation was validated with a new set of data points from some other production wells to confirm the accuracy of the established correlation. The results show that the new correlation had minimal errors and predicted the gas flow rate more accurately than the other three existing models over a wider range of parameter variation ranges. Full article
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<p>Estimated gas flow rate versus real gas flow rate (all data).</p>
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<p>Estimated gas flow rate versus real gas flow rate (all data) for all four models.</p>
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<p>Estimated gas flow rate versus real gas flow rate (validation).</p>
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<p>Pressure drop across the choke vs flow rate per choke size.</p>
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<p>Estimated gas flow rate versus real gas flow rate (24/64 inch).</p>
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<p>Estimated gas flow rate versus real gas flow rate (40/64 inch).</p>
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<p>Estimated gas flow rate versus real gas flow rate (64/64 inch).</p>
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<p>Estimated gas flow rate versus real gas flow rate (128/64 inch).</p>
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<p>Estimated gas flow rate versus real gas flow rate (144/64 inch).</p>
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<p>Estimated gas flow rate versus real gas flow rate (160/64 inch).</p>
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<p>Estimated gas flow rate versus real gas flow rate (192/64 inch).</p>
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