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Energies, Volume 11, Issue 2 (February 2018) – 215 articles

Cover Story (view full-size image): Axial turbocharger turbines have, in recent years, become viable alternatives to radial turbines for certain vehicle applications where response and efficiency are the driving requirements. In this study, a novel axial-inflow turbine has been coupled with variable geometry technology to additionally benefit both efficiency and performance. Engine simulations demonstrated that engine power and torque are significantly increased through the application of the proposed technology. View this paper
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13 pages, 1967 KiB  
Article
Estimation of the Diesel Particulate Filter Soot Load Based on an Equivalent Circuit Model
by Yanting Du, Guangdi Hu, Shun Xiang, Ke Zhang, Hongxing Liu and Feng Guo
Energies 2018, 11(2), 472; https://doi.org/10.3390/en11020472 - 23 Feb 2018
Cited by 15 | Viewed by 6926
Abstract
In order to estimate the diesel particulate filter (DPF) soot load and improve the accuracy of regeneration timing, a novel method based on an equivalent circuit model is proposed based on the electric-fluid analogy. This proposed method can reduce the impact of the [...] Read more.
In order to estimate the diesel particulate filter (DPF) soot load and improve the accuracy of regeneration timing, a novel method based on an equivalent circuit model is proposed based on the electric-fluid analogy. This proposed method can reduce the impact of the engine transient operation on the soot load, accurately calculate the flow resistance, and improve the estimation accuracy of the soot load. Firstly, the least square method is used to identify the flow resistance based on the World Harmonized Transient Cycle (WHTC) test data, and the relationship between flow resistance, exhaust temperature and soot load is established. Secondly, the online estimation of the soot load is achieved by using the dual extended Kalman filter (DEKF). The results show that this method has good convergence and robustness with the maximal absolute error of 0.2 g/L at regeneration timing, which can meet engineering requirements. Additionally, this method can estimate the soot load under engine transient operating conditions and avoids a large number of experimental tests, extensive calibration and the analysis of complex chemical reactions required in traditional methods. Full article
(This article belongs to the Special Issue Recent Advances in Internal Combustion Engines Operation and Emissions)
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Figure 1
<p>Diesel particulate filter equivalent circuit model.</p> Full article ">Figure 2
<p>The relationship between <span class="html-italic">b</span> and <span class="html-italic">m</span>.</p> Full article ">Figure 3
<p>Comparison between simulation and test values of the pressure drop: (<b>a</b>) Pressure drop under the 1st cycle; (<b>b</b>) Pressure drop under the 4th cycle; (<b>c</b>) Pressure drop under the 15th cycle; (<b>d</b>) Pressure drop under the 20th cycle.</p> Full article ">Figure 4
<p>Online estimation process of the soot load.</p> Full article ">Figure 5
<p>The results of the soot load: (<b>a</b>) Soot load of the 1st cycle; (<b>b</b>) Soot load of the 4th cycle; (<b>c</b>) Soot load of the 15th cycle; (<b>d</b>) Soot load of the 20th cycle.</p> Full article ">Figure 5 Cont.
<p>The results of the soot load: (<b>a</b>) Soot load of the 1st cycle; (<b>b</b>) Soot load of the 4th cycle; (<b>c</b>) Soot load of the 15th cycle; (<b>d</b>) Soot load of the 20th cycle.</p> Full article ">Figure 6
<p>The results of the soot load with an incorrect initial value: (<b>a</b>) Soot load of the 4th cycle; (<b>b</b>) Soot load of the 15th cycle; (<b>c</b>) Soot load of the 20th cycle.</p> Full article ">Figure 6 Cont.
<p>The results of the soot load with an incorrect initial value: (<b>a</b>) Soot load of the 4th cycle; (<b>b</b>) Soot load of the 15th cycle; (<b>c</b>) Soot load of the 20th cycle.</p> Full article ">
14 pages, 6132 KiB  
Article
Analysis of dc-Link Voltage Switching Ripple in Three-Phase PWM Inverters
by Marija Vujacic, Manel Hammami, Milan Srndovic and Gabriele Grandi
Energies 2018, 11(2), 471; https://doi.org/10.3390/en11020471 - 23 Feb 2018
Cited by 61 | Viewed by 14001
Abstract
The three-phase voltage source inverter (VSI) is de facto standard in power conversion systems. To realize high power density systems, one of the items to be correctly addressed is the design and selection of the dc-link capacitor in relation to the voltage switching [...] Read more.
The three-phase voltage source inverter (VSI) is de facto standard in power conversion systems. To realize high power density systems, one of the items to be correctly addressed is the design and selection of the dc-link capacitor in relation to the voltage switching ripple. In this paper, effective formulas for designing the dc-link capacitor as a function of the switching voltage ripple amplitude are obtained, considering the operating conditions such as the modulation index and the output current amplitude. The calculations are obtained considering the requirements and restrictions referring to the high (switching)-frequency dc-link voltage ripple component. Analyses have been performed considering the dc source impedance (non-ideal dc voltage source at the switching frequency) and a balanced load. Analytical expressions are derived for the dc-link voltage switching ripple amplitude and its maximum value over the fundamental period. Different values of modulation index and output phase angle have been considered and different diagrams are presented. Analytical results were validated both by simulations and comprehensive experimental tests. Full article
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<p>Circuit scheme of the three-phase voltage source inverter (VSI) under investigation.</p> Full article ">Figure 2
<p>Space vector diagram of inverter output voltage emphasizing the input dc current (framed) for each inverter state (S<sub>1</sub> S<sub>2</sub> S<sub>3</sub>).</p> Full article ">Figure 3
<p>dc-link current and voltage ripple in one switching period: (<b>a</b>) Case A (0 ≤ <math display="inline"> <semantics> <mi>ϑ</mi> </semantics> </math> ≤ π/3, <span class="html-italic">i</span><sub>1</sub> ≥ <span class="html-italic">I<sub>dc</sub></span>), (<b>b</b>) Case B (0 ≤ <math display="inline"> <semantics> <mi>ϑ</mi> </semantics> </math>≤ π/3, <span class="html-italic">i</span><sub>1</sub> &lt; <span class="html-italic">I<sub>dc</sub></span>).</p> Full article ">Figure 4
<p>Normalized peak-to-peak dc-link voltage ripple amplitude <span class="html-italic">r<sub>pp</sub></span>(<math display="inline"> <semantics> <mi>ϑ</mi> </semantics> </math>) over the period [0, 60°] for different modulation indices, <span class="html-italic">m</span> = 1/4, 1/3, 1/2 and 1/<math display="inline"> <semantics> <mrow> <msqrt> <mn>3</mn> </msqrt> </mrow> </semantics> </math>, and output phase angles (<b>a</b>) <math display="inline"> <semantics> <mi>φ</mi> </semantics> </math> = 0° and (<b>b</b>) 50°.</p> Full article ">Figure 5
<p>Maximum of normalized peak-to-peak voltage ripple amplitude vs. modulation index for different output phase angles.</p> Full article ">Figure 6
<p>Three-phase load circuit.</p> Full article ">Figure 7
<p>Experimental setup with a zoom of the three-phase inverter board.</p> Full article ">Figure 8
<p>dc-link voltage switching ripple: simulation results (blue trace) and calculated peak-to- peak envelope (red trace) over a fundamental period for <math display="inline"> <semantics> <mi>φ</mi> </semantics> </math> = 0 (left) and <math display="inline"> <semantics> <mi>φ</mi> </semantics> </math> = 50° (right), with different modulation indicies: (<b>a</b>,<b>b</b>) <span class="html-italic">m</span> = 0.25; (<b>c</b>,<b>d</b>) <span class="html-italic">m</span> = 0.33; (<b>e</b>,<b>f</b>) <span class="html-italic">m</span> = 0.50; (<b>g</b>,<b>h</b>) <span class="html-italic">m</span> = 0.577.</p> Full article ">Figure 8 Cont.
<p>dc-link voltage switching ripple: simulation results (blue trace) and calculated peak-to- peak envelope (red trace) over a fundamental period for <math display="inline"> <semantics> <mi>φ</mi> </semantics> </math> = 0 (left) and <math display="inline"> <semantics> <mi>φ</mi> </semantics> </math> = 50° (right), with different modulation indicies: (<b>a</b>,<b>b</b>) <span class="html-italic">m</span> = 0.25; (<b>c</b>,<b>d</b>) <span class="html-italic">m</span> = 0.33; (<b>e</b>,<b>f</b>) <span class="html-italic">m</span> = 0.50; (<b>g</b>,<b>h</b>) <span class="html-italic">m</span> = 0.577.</p> Full article ">Figure 9
<p>Experimental results for <math display="inline"> <semantics> <mi>φ</mi> </semantics> </math> = 0° (left) and <math display="inline"> <semantics> <mi>φ</mi> </semantics> </math> = 50° (right). Upper half: output voltage and current. Lower half: calculated peak-to-peak envelope and measured dc-link voltage switching ripple with different modulation indices: (<b>a</b>,<b>b</b>) <span class="html-italic">m</span> = 0.25; (<b>c</b>,<b>d</b>) <span class="html-italic">m</span> = 0.33; (<b>e</b>,<b>f</b>) <span class="html-italic">m</span> = 0.50; (<b>g</b>,<b>h</b>) <span class="html-italic">m</span> = 0.577.</p> Full article ">Figure 9 Cont.
<p>Experimental results for <math display="inline"> <semantics> <mi>φ</mi> </semantics> </math> = 0° (left) and <math display="inline"> <semantics> <mi>φ</mi> </semantics> </math> = 50° (right). Upper half: output voltage and current. Lower half: calculated peak-to-peak envelope and measured dc-link voltage switching ripple with different modulation indices: (<b>a</b>,<b>b</b>) <span class="html-italic">m</span> = 0.25; (<b>c</b>,<b>d</b>) <span class="html-italic">m</span> = 0.33; (<b>e</b>,<b>f</b>) <span class="html-italic">m</span> = 0.50; (<b>g</b>,<b>h</b>) <span class="html-italic">m</span> = 0.577.</p> Full article ">
13 pages, 4323 KiB  
Article
Analysis of Switching Transients during Energization in Large Offshore Wind Farms
by Gang Liu, Yaxun Guo, Yanli Xin, Lei You, Xiaofeng Jiang, Ming Zheng and Wenhu Tang
Energies 2018, 11(2), 470; https://doi.org/10.3390/en11020470 - 23 Feb 2018
Cited by 14 | Viewed by 6086
Abstract
In order to study switching transients in an offshore wind farm (OWF) collector system, we employ modeling methods of the main components in OWFs, including vacuum circuit breakers (VCBs), submarine cables, and wind turbine transformers (WTTs). In particular, a high frequency (HF) VCB [...] Read more.
In order to study switching transients in an offshore wind farm (OWF) collector system, we employ modeling methods of the main components in OWFs, including vacuum circuit breakers (VCBs), submarine cables, and wind turbine transformers (WTTs). In particular, a high frequency (HF) VCB model that reflects the prestrike characteristics of VCBs was developed. Moreover, a simplified experimental system of an OWF electric collection system was set up to verify the developed models, and a typical OWF medium voltage (MV) cable collection system was built in PSCAD/EMTDC based on the developed models. Finally, we investigated the influences of both the initial closing phase angle of VCBs and typical system operation scenarios on the amplitude and steepness of transient overvoltages (TOVs) at the high-voltage side of WTTs. Full article
(This article belongs to the Special Issue Emerging Power Electronics Technologies for Power Systems and Machine Drives)
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<p>Simplified diagram of the investigated offshore wind farm (OWF).</p> Full article ">Figure 2
<p>The cable arrangement.</p> Full article ">Figure 3
<p>The vacuum circuit breaker (VCB) model diagram.</p> Full article ">Figure 4
<p>The program flow chart of the developed VCB model.</p> Full article ">Figure 5
<p>The test system wiring diagram.</p> Full article ">Figure 6
<p>Voltage and current waveforms at the high voltage side of TX2: (<b>a</b>) voltages; (<b>b</b>) currents.</p> Full article ">Figure 7
<p>The zoomed-in views of the voltages and currents at the high side of TX2: (<b>a</b>) voltages; (<b>b</b>) currents.</p> Full article ">Figure 8
<p>The relationship between different indicators of TOV and the initial closing phase angle for Scenario 1: (<b>a</b>) the amplitude and steepness; (<b>b</b>) the number of prestrikes (#prestrikes).</p> Full article ">Figure 9
<p>The relationship between the initial closing angle and overvoltage.</p> Full article ">Figure 10
<p>The overvoltage of different transformer locations for Scenario 2.</p> Full article ">Figure 11
<p>The overvoltage of different transformer locations for Scenario 3: (<b>a</b>) amplitude; (<b>b</b>) steepness.</p> Full article ">Figure 12
<p>The overvoltages for different transformers when the number of running feeders changes: (<b>a</b>) amplitude; (<b>b</b>) steepness.</p> Full article ">Figure 13
<p>The overvoltage waveform across VCB1.</p> Full article ">
18 pages, 7150 KiB  
Article
A PSO-Optimized Fuzzy Logic Control-Based Charging Method for Individual Household Battery Storage Systems within a Community
by Yu-Shan Cheng, Yi-Hua Liu, Holger C. Hesse, Maik Naumann, Cong Nam Truong and Andreas Jossen
Energies 2018, 11(2), 469; https://doi.org/10.3390/en11020469 - 23 Feb 2018
Cited by 29 | Viewed by 5585
Abstract
Self-consumption of household photovoltaic (PV) storage systems has become profitable for residential owners under the trends of limited feed-in power and decreasing PV feed-in tariffs. For individual PV-storage systems, the challenge mainly lies in managing surplus generation of battery and grid power flow, [...] Read more.
Self-consumption of household photovoltaic (PV) storage systems has become profitable for residential owners under the trends of limited feed-in power and decreasing PV feed-in tariffs. For individual PV-storage systems, the challenge mainly lies in managing surplus generation of battery and grid power flow, ideally without relying on error-prone forecasts for both generation and consumption. Considering the large variation in power profiles of different houses in a neighborhood, the strategy is also supposed to be beneficial and applicable for the entire community. In this study, an adaptable battery charging control strategy is designed in order to obtain minimum costs for houses without any meteorological or load forecasts. Based on fuzzy logic control (FLC), battery state-of-charge (SOC) and the variation of SOC (∆SOC) are taken as input variables to dynamically determine output charging power with minimum costs. The proposed FLC-based algorithm benefits from the charging battery as much as possible during the daytime, and meanwhile properly preserves the capacity at midday when there is high possibility of curtailment loss. In addition, due to distinct power profiles in each individual house, input membership functions of FLC are improved by particle swarm optimization (PSO) to achieve better overall performance. A neighborhood with 74 houses in Germany is set up as a scenario for comparison to prior studies. Without forecasts of generation and consumption power, the proposed method leads to minimum costs in 98.6% of houses in the community, and attains the lowest average expenses for a single house each year. Full article
(This article belongs to the Section F: Electrical Engineering)
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<p>The investigated system scheme.</p> Full article ">Figure 2
<p>Strong variation is visible in terms of load fluctuation from observation of (<b>a</b>) a long time interval of one day, and from (<b>b</b>) a short time interval of 10 min.</p> Full article ">Figure 3
<p>The cycle life versus depth of discharge (DoD; %) curve of the utilized LiFePO<sub>4</sub> battery (adapted from [<a href="#B26-energies-11-00469" class="html-bibr">26</a>]).</p> Full article ">Figure 4
<p>Input MFs (state-of-charge (SOC) and variation of SOC(∆SOC)) as well as the output MF (charging ratio). MF: membership function; M: medium; S: small; MS: medium small; ML: medium large; L: large; NS: negatively small; PS: positively small; NL: negatively large; Z: zero; PL: positively large; CR: charging ratio.</p> Full article ">Figure 5
<p>Rule table of the proposed fuzzy logic control (FLC) controller.</p> Full article ">Figure 6
<p>Illustration of charging behavior from rule table.</p> Full article ">Figure 7
<p>Illustration for optimized input MFs.</p> Full article ">Figure 8
<p>Optimization flowchart.</p> Full article ">Figure 9
<p>The simulated results of power profiles for an example day.</p> Full article ">Figure 10
<p>Simulated results (<b>a</b>) percentage of achieving lowest cost (<b>b</b>) improvement space for normal fuzzy (FuzzyN) compared to Greedy and feed-in damping (FID) methods.</p> Full article ">Figure 11
<p>Convergence of <span class="html-italic">gBest</span>.</p> Full article ">Figure 12
<p>Additional costs compared to the perfect foresight method. FuzzyOP: optimized Fuzzy.</p> Full article ">
15 pages, 3074 KiB  
Article
Multi-Rate and Parallel Electromagnetic Transient Simulation Considering Nonlinear Characteristics of a Power System
by Ji Han, Shihong Miao, Jing Yu, Yifeng Dong, Junxian Hou, Simo Duan and Lixing Li
Energies 2018, 11(2), 468; https://doi.org/10.3390/en11020468 - 23 Feb 2018
Cited by 4 | Viewed by 3510
Abstract
The electromagnetic transient simulation of a power system with nonlinear characteristics is very time-consuming due to numerous inversion calculations of the admittance matrix. To speed up the simulation of the power system with nonlinear characteristics, a multi-rate and parallel electromagnetic transient simulation method [...] Read more.
The electromagnetic transient simulation of a power system with nonlinear characteristics is very time-consuming due to numerous inversion calculations of the admittance matrix. To speed up the simulation of the power system with nonlinear characteristics, a multi-rate and parallel electromagnetic transient simulation method is proposed. Firstly, a Multi-Area Thevenin Equivalents (MATE)-based parallel algorithm considering nonlinear characteristics of the power system is proposed. This method guarantees the admittance matrix is constant by considering changing branches as link current without dividing the subnet again. Secondly, considering the differences of the time constant of the AC/DC subnet, different simulation steps are used for these subnets. The Lagrange interpolation method is used for calculating the Thevenin voltage of the AC subnet in non-synchronous time. Calculation methods of the DC subnet Thevenin voltage is proposed by considering the simulation results during the entire large simulation step. Finally, the simulation process is optimized for improving the simulation efficiency further. The simulation results show that the proposed method could greatly improve the simulation efficiency without losing simulation accuracy too much compared with the traditional method. Full article
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<p>Sample power system partition diagram.</p> Full article ">Figure 2
<p>Relationship of <math display="inline"> <semantics> <mrow> <mo>Δ</mo> <mi>T</mi> </mrow> </semantics> </math> and <math display="inline"> <semantics> <mrow> <mo>Δ</mo> <mi>t</mi> </mrow> </semantics> </math>.</p> Full article ">Figure 3
<p>Simulation flowchart.</p> Full article ">Figure 4
<p>Standard test model.</p> Full article ">Figure 5
<p>Comparison of the simulation results: (<b>a</b>) simulation comparison of <span class="html-italic">U<sub>ainv</sub></span>; (<b>b</b>) simulation comparison of <span class="html-italic">U<sub>dinv</sub></span>; (<b>c</b>) simulation comparison of <span class="html-italic">U<sub>arec</sub></span>; and (<b>d</b>) simulation comparison of <span class="html-italic">U<sub>drec</sub></span>.</p> Full article ">Figure 6
<p>The circuit used to increase node of network: (<b>a</b>) actual circuit; and (<b>b</b>) abstract circuit.</p> Full article ">Figure 7
<p>Test model after processing.</p> Full article ">Figure 8
<p>Simulation time of different methods under different partition schemes.</p> Full article ">
14 pages, 8295 KiB  
Article
Experimental Determination of Gas Relative Permeability Considering Slippage Effect in a Tight Formation
by Guangfeng Liu, Zhaoqi Fan, Yang Lu, Siying Li, Bo Feng, Yu Xia and Qimeng Zhao
Energies 2018, 11(2), 467; https://doi.org/10.3390/en11020467 - 23 Feb 2018
Cited by 14 | Viewed by 4719
Abstract
In this paper, the gas relative permeability considering slippage effect has been experimentally examined under various experimental conditions (i.e., ambient, high confining pressure, and high temperature). Experimentally, Klinkenberg permeabilities of 12 core samples have been measured by using steady-state flow experiment. It has [...] Read more.
In this paper, the gas relative permeability considering slippage effect has been experimentally examined under various experimental conditions (i.e., ambient, high confining pressure, and high temperature). Experimentally, Klinkenberg permeabilities of 12 core samples have been measured by using steady-state flow experiment. It has been found that the Klinkenberg permeability is independent of the experimental temperature and dramatically decreases as confining pressure is increasing. Furthermore, linear correlations have been newly developed between the Klinkenberg permeability and the gas-measured permeability under various conditions. Subsequently, the developed correlations are correspondingly applied to calibrate the gas relative permeability. It has been found that the gas relative permeability can be overestimated without consideration of the slippage effect, i.e., Klinkenberg effect. In addition, the newly developed correlations have been applied to analyze the sensitivity of gas–water relative permeability to gas-measured permeability, confining pressure, and temperature. It is demonstrated that mobile water greatly alleviates the gas relative permeability in comparison to irreducible water. Although an increased confining pressure simultaneously reduces the effective water phase and gas phase permeability, the gas relative permeability increases and the water relative permeability decreases as the confining pressure increases. It is attributed to the fact that the effective water phase permeability is more sensitive to the confining pressure. Given an elevated experimental temperature, the gas relative permeability is reduced while the water relative permeability is enhanced, implying the significance of temperature effect on gas–water relative permeability measurements. Full article
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<p>Schematic diagram of experimental apparatuses: (<b>1</b>) Syringe pump; (<b>2</b>), (<b>4</b>), (<b>11</b>) Valve; (<b>3</b>) Synthetic brine cylinder; (<b>5</b>), (<b>12</b>), (<b>15</b>), (<b>16</b>) Pressure transducer; (<b>6</b>) Nitrogen cylinder; (<b>7</b>) High pressure reducing valve; (<b>8</b>) Gas mass flow controller; (<b>9</b>), (<b>21</b>) Data acquisition system; (<b>10</b>) Gas humidifier; (<b>13</b>) Core holder; (<b>14</b>) Confining pressure pump; (<b>17</b>) Electric heating thermostat; (<b>18</b>) Dryer; (<b>19</b>) Balance; and (<b>20</b>) Gas mass flow meter.</p> Full article ">Figure 2
<p>Image of displacement experiment system.</p> Full article ">Figure 3
<p>Sensitivity of (<b>a</b>) nitrogen and (<b>b</b>) synthetic brine viscosity to pressure and temperature.</p> Full article ">Figure 4
<p>Experimental results of Klinkenberg correction for core samples: (<b>a</b>) #1; (<b>b</b>) #2; (<b>c</b>) #3; (<b>d</b>) #4; (<b>e</b>) #5; (<b>f</b>) #6.</p> Full article ">Figure 4 Cont.
<p>Experimental results of Klinkenberg correction for core samples: (<b>a</b>) #1; (<b>b</b>) #2; (<b>c</b>) #3; (<b>d</b>) #4; (<b>e</b>) #5; (<b>f</b>) #6.</p> Full article ">Figure 5
<p>Correlations between the Klinkenberg permeability and <math display="inline"> <semantics> <mrow> <msubsup> <mi>k</mi> <mi>g</mi> <mo>*</mo> </msubsup> </mrow> </semantics> </math> under different conditions.</p> Full article ">Figure 6
<p>Gas–water relative permeability with and without Klinkenberg correction.</p> Full article ">Figure 7
<p>Effect of permeability on gas–water relative permeability.</p> Full article ">Figure 8
<p>Pore structure illustrated by thin section analysis results of (<b>a</b>) core sample #13 and (<b>b</b>) core sample #15.</p> Full article ">Figure 9
<p>Effect of confining pressure on gas–water relative permeability.</p> Full article ">Figure 10
<p>Effect of temperature on gas–water relative permeability.</p> Full article ">
15 pages, 3149 KiB  
Article
Composite Reliability Evaluation of Load Demand Side Management and Dynamic Thermal Rating Systems
by Jiashen Teh, Chia Ai Ooi, Yu-Huei Cheng, Muhammad Ammirrul Atiqi Mohd Zainuri and Ching-Ming Lai
Energies 2018, 11(2), 466; https://doi.org/10.3390/en11020466 - 23 Feb 2018
Cited by 38 | Viewed by 4943
Abstract
Electric power utilities across the globe are facing higher demand for electricity than ever before, while juggling to balance environmental conservation with transmission corridor expansions. Demand side management (DSM) and dynamic thermal rating systems (DTR) play an important role in alleviating some of [...] Read more.
Electric power utilities across the globe are facing higher demand for electricity than ever before, while juggling to balance environmental conservation with transmission corridor expansions. Demand side management (DSM) and dynamic thermal rating systems (DTR) play an important role in alleviating some of the challenges faced by electric power utilities. In this paper, various DSM measures are explored and their interactions with the application of the DTR system in the transmission network are examined. The proposed modelling of DSM in this paper implements load shifting on load demand curves from the system, bus and load sector levels. The correlation effects of line ratings are considered in the DTR system modelling as the weather that influences line ratings is also correlated. The modelling of the line ratings was performed using the time series method, the auto regressive moving average (ARMA) model. Both the DSM and the DTR systems were implemented on the modified IEEE reliability test network. The modification was achieved by developing a load model starting from the perspective of the load sectors at each bus and a new collective hourly load curve for the system was obtained by combining the loads at all buses. Finally, the results in this paper elucidate the interaction of DSM and DTR systems. Full article
(This article belongs to the Section F: Electrical Engineering)
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<p>Various load sector curves.</p> Full article ">Figure 2
<p>The enhanced IEEE RTN.</p> Full article ">Figure 3
<p>The 48-h system load profile for BusSectorLS80, BusSectorLS85, and BusSectorLS90.</p> Full article ">Figure 4
<p>The 48-h system load profile with 80% pre-specified peak.</p> Full article ">Figure 5
<p>The 48-h system load profile with 85% pre-specified peak.</p> Full article ">Figure 6
<p>The 48-h system load profile with 90% pre-specified peak.</p> Full article ">Figure 7
<p>The correlations of the line ratings in the enhanced IEEE RTN.</p> Full article ">Figure 8
<p>An overview of the proposed methodology for the joint composite reliability evaluation of DSM and the DTR system.</p> Full article ">Figure 9
<p>The EENS of BusSectorLS measures with increasing load levels.</p> Full article ">Figure 10
<p>The EENS of BusSectorLS80 with various DTR system correlations.</p> Full article ">Figure 11
<p>The EENS of BusSectorLS80 with and without the DTR system.</p> Full article ">
13 pages, 2409 KiB  
Article
PV Hosting Capacity Dependence on Harmonic Voltage Distortion in Low-Voltage Grids: Model Validation with Experimental Data
by Tiago E. C. de Oliveira, Pedro M. S. Carvalho, Paulo F. Ribeiro and Benedito D. Bonatto
Energies 2018, 11(2), 465; https://doi.org/10.3390/en11020465 - 23 Feb 2018
Cited by 43 | Viewed by 5955
Abstract
This paper introduces a brief analysis on hosting capacity and related concepts as applied to distribution network systems. Furthermore, it addresses the applicability of hosting capacity study methodologies to harmonic voltage distortion caused by photovoltaic panels (PV) connected at a low-voltage (LV) side [...] Read more.
This paper introduces a brief analysis on hosting capacity and related concepts as applied to distribution network systems. Furthermore, it addresses the applicability of hosting capacity study methodologies to harmonic voltage distortion caused by photovoltaic panels (PV) connected at a low-voltage (LV) side of a university campus grid. The analysis of the penetration of new distributed generation technologies, such as PV panels, in the distribution grid of the campus was carried out via measurement processes, and later by computer simulations analyzing a new concept of the hosting capacity approach in relation to voltage harmonics distortion. The voltage rise due to harmonic injection is analyzed and discussed with the aim of validating the discussed model and also putting forward recommendations for connecting PV generation across other network systems. Full article
(This article belongs to the Special Issue Distributed and Renewable Power Generation)
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<p>Range curves of a generic performance index versus the amount of distributed generation.</p> Full article ">Figure 2
<p>Feeder model with PV [<a href="#B3-energies-11-00465" class="html-bibr">3</a>].</p> Full article ">Figure 3
<p>One-line diagram for the PV System of the CERIn sited in UNIFEI’s (Federal University of Itajubá) campus.</p> Full article ">Figure 4
<p>Hosting capacity experimental data.</p> Full article ">Figure 5
<p>Hosting capacity validation against (1) and (14).</p> Full article ">Figure 6
<p>Hosting capacity approach of the four scenarios.</p> Full article ">Figure 7
<p>Variation of the hosting capacity region for different power factors.</p> Full article ">
12 pages, 6957 KiB  
Article
Modulation Strategy of a 3 × 5 Modular Multilevel Matrix Converter
by Rutian Wang, Dapeng Lei, Yanfeng Zhao, Chuang Liu and Yue Hu
Energies 2018, 11(2), 464; https://doi.org/10.3390/en11020464 - 23 Feb 2018
Cited by 5 | Viewed by 4083
Abstract
In this paper, a modulation strategy of a 3 × 5 modular multilevel matrix converter (M3C) is proposed. The circuit of 3 × 5 M3C is firstly introduced. Then, operation rules of 3 × 5 M3C are illustrated, and a connection pattern of [...] Read more.
In this paper, a modulation strategy of a 3 × 5 modular multilevel matrix converter (M3C) is proposed. The circuit of 3 × 5 M3C is firstly introduced. Then, operation rules of 3 × 5 M3C are illustrated, and a connection pattern of branches is determined based on these rules. Different voltage states in the input and output side can be achieved by different connection patterns. These voltage states are represented in the form of vector. It is hard to synthesize five-phase output with the three-level synthesis method. Therefore, the five-level synthesis method is adopted in this paper; i.e., is the branch states have been increased. Ten effective vectors and a zero vector are selected based on the five-level synthesis method. With this modulation strategy, we achieve output line-to-line voltages that are in line with the trend of a sine wave. The segment division and duty cycle calculation are very simple, and the modulation strategy can be implemented easily. The simulation model of 3 × 5 M3C is constructed based on Matlab/Simulink, and the corresponding experimental platform is set up. The results of simulation and experiment show that the proposed method is reasonable and correct. Full article
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<p>Main circuit topology of 3 × 5 M3C.</p> Full article ">Figure 2
<p>Vector combination of <span class="html-italic">V</span><sub>i1</sub>–<span class="html-italic">V</span><sub>o2</sub>.</p> Full article ">Figure 3
<p>Vector combination of <span class="html-italic">V</span><sub>i0</sub>–<span class="html-italic">V</span><sub>o1</sub>.</p> Full article ">Figure 4
<p>Vectors of 3 × 5 M3C (<b>a</b>) Vectors of input side; (<b>b</b>) Vectors of output side.</p> Full article ">Figure 5
<p>Timing of space vector.</p> Full article ">Figure 6
<p>Output adjacent line-to-line voltage.</p> Full article ">Figure 7
<p>Spectrum analysis of <span class="html-italic">u</span><sub>ab</sub>.</p> Full article ">Figure 8
<p>Output phase voltages.</p> Full article ">Figure 9
<p>Five-phase output currents.</p> Full article ">Figure 10
<p>Input phase voltage and current.</p> Full article ">Figure 11
<p>Configuration of experimental platform (<b>a</b>) system structure; (<b>b</b>) photos of experimental platform.</p> Full article ">Figure 12
<p>Experimental waveforms under the output parameter 100 V/100 Hz (<b>a</b>) output line-to-line voltage (<span class="html-italic">u</span><sub>ab</sub>); (<b>b</b>) output phase voltage (<span class="html-italic">u</span><sub>a</sub>, <span class="html-italic">u</span><sub>b</sub>); (<b>c</b>) output currents (<span class="html-italic">i</span><sub>a</sub>, <span class="html-italic">i</span><sub>b</sub>, <span class="html-italic">i</span><sub>c</sub>, <span class="html-italic">i</span><sub>d</sub>); (<b>d</b>) the Fast Fourier Transformation (FFT) analysis of <span class="html-italic">i</span><sub>a</sub>; (<b>e</b>) input phase voltage and current (<span class="html-italic">u</span><sub>A</sub>, <span class="html-italic">i</span><sub>A</sub>).</p> Full article ">Figure 13
<p>Experimental waveforms under the output parameter 80 V/25 Hz (<b>a</b>) output line-to-line voltage (<span class="html-italic">u</span><sub>ab</sub>); (<b>b</b>) output phase voltage (<span class="html-italic">u</span><sub>a</sub>, <span class="html-italic">u</span><sub>b</sub>); (<b>c</b>) output currents (<span class="html-italic">i</span><sub>a</sub>, <span class="html-italic">i</span><sub>b</sub>, <span class="html-italic">i</span><sub>c</sub>, <span class="html-italic">i</span><sub>d</sub>); (<b>d</b>) the FFT analysis of <span class="html-italic">i</span><sub>a</sub>; (<b>e</b>) input phase voltage and current (<span class="html-italic">u</span><sub>A</sub>, <span class="html-italic">i</span><sub>A</sub>).</p> Full article ">
13 pages, 1508 KiB  
Article
A Comprehensive Energy Analysis and Related Carbon Footprint of Dairy Farms, Part 2: Investigation and Modeling of Indirect Energy Requirements
by Giuseppe Todde, Lelia Murgia, Maria Caria and Antonio Pazzona
Energies 2018, 11(2), 463; https://doi.org/10.3390/en11020463 - 22 Feb 2018
Cited by 33 | Viewed by 5043
Abstract
Dairy cattle farms are continuously developing more intensive systems of management, which require higher utilization of durable and non-durable inputs. These inputs are responsible for significant direct and indirect fossil energy requirements, which are related to remarkable emissions of CO2. This [...] Read more.
Dairy cattle farms are continuously developing more intensive systems of management, which require higher utilization of durable and non-durable inputs. These inputs are responsible for significant direct and indirect fossil energy requirements, which are related to remarkable emissions of CO2. This study focused on investigating the indirect energy requirements of 285 conventional dairy farms and the related carbon footprint. A detailed analysis of the indirect energy inputs related to farm buildings, machinery and agricultural inputs was carried out. A partial life cycle assessment approach was carried out to evaluate indirect energy inputs and the carbon footprint of farms over a period of one harvest year. The investigation highlights the importance and the weight related to the use of agricultural inputs, which represent more than 80% of the total indirect energy requirements. Moreover, the analyses carried out underline that the assumption of similarity in terms of requirements of indirect energy and related carbon emissions among dairy farms is incorrect especially when observing different farm sizes and milk production levels. Moreover, a mathematical model to estimate the indirect energy requirements of dairy farms has been developed in order to provide an instrument allowing researchers to assess the energy incorporated into farm machinery, agricultural inputs and buildings. Combining the results of this two-part series, the total energy demand (expressed in GJ per farm) results in being mostly due to agricultural inputs and fuel consumption, which have the largest share of the annual requirements for each milk yield class. Direct and indirect energy requirements increased, going from small sized farms to larger ones, from 1302–5109 GJ·y−1, respectively. However, the related carbon dioxide emissions expressed per 100 kg of milk showed a negative trend going from class <5000 to >9000 kg of milk yield, where larger farms were able to emit 48% less carbon dioxide than small herd size farm (43 vs. 82 kg CO2-eq per 100 kg Fat- and Protein-Corrected Milk (FPCM)). Decreasing direct and indirect energy requirements allowed reducing the anthropogenic gas emissions to the environment, reducing the energy costs for dairy farms and improving the efficient utilization of natural resources. Full article
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<p>The indirect energy results have been obtained for each category (<b>A</b>) and then distributed for each of them such as for building and facilities (<b>B</b>), machinery and equipment (<b>C</b>) and agricultural inputs (<b>D</b>).</p> Full article ">Figure 2
<p>Prediction accuracy evaluation plot (<b>A</b>) and standardized residual evaluation plot (<b>B</b>) of indirect energy requirements.</p> Full article ">Figure 3
<p>Direct and indirect energy requirements expressed as the average per dairy farm (GJ·y<sup>−1</sup>) and related direct and indirect carbon emissions per 100 kg of milk produced.</p> Full article ">
15 pages, 2462 KiB  
Article
Fuzzy Logic-Based Perturb and Observe Algorithm with Variable Step of a Reference Voltage for Solar Permanent Magnet Synchronous Motor Drive System Fed by Direct-Connected Photovoltaic Array
by Mohamed Redha Rezoug, Rachid Chenni and Djamel Taibi
Energies 2018, 11(2), 462; https://doi.org/10.3390/en11020462 - 22 Feb 2018
Cited by 17 | Viewed by 7757
Abstract
Photovoltaic pumping is considered to be the most used application amongst other photovoltaic energy applications in isolated sites. This technology is developing with a slow progression to allow the photovoltaic system to operate at its maximum power. This work introduces the modified algorithm [...] Read more.
Photovoltaic pumping is considered to be the most used application amongst other photovoltaic energy applications in isolated sites. This technology is developing with a slow progression to allow the photovoltaic system to operate at its maximum power. This work introduces the modified algorithm which is a perturb and observe (P&O) type to overcome the limitations of the conventional P&O algorithm and increase its global performance in abrupt weather condition changes. The most significant conventional P&O algorithm restriction is the difficulty faced when choosing the variable step of the reference voltage value, a good compromise between the swift dynamic response and the stability in the steady state. To adjust the step reference voltage according to the location of the operating point of the maximum power point (MPP), a fuzzy logic controller (FLC) block adapted to the P&O algorithm is used. This allows the improvement of the tracking pace and the steady state oscillation elimination. The suggested method was evaluated by simulation using MATLAB/SimPowerSystems blocks and compared to the classical P&O under different irradiation levels. The results obtained show the effectiveness of the technique proposed and its capacity for the practical and efficient tracking of maximum power. Full article
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<p>The electrical modeling of the photovoltaic (PV) cell.</p> Full article ">Figure 2
<p>(<b>a</b>) <span class="html-italic">P</span>–<span class="html-italic">V</span> and <span class="html-italic">I</span>–<span class="html-italic">V</span> curves for different irradiance and constant T; (<b>b</b>) <span class="html-italic">P</span>–<span class="html-italic">V</span> and <span class="html-italic">I</span>–<span class="html-italic">V</span> curves for different temperatures and constant G.</p> Full article ">Figure 3
<p>Flowchart of the modified perturb and observe (P&amp;O) algorithm.</p> Full article ">Figure 4
<p>General diagram of a fuzzy logic controller.</p> Full article ">Figure 5
<p>(<b>a</b>,<b>b</b>) Membership functions of the input variables (∆<span class="html-italic">P<sub>PV</sub></span>, ∆<span class="html-italic">I<sub>PV</sub></span>) and (<b>c</b>) the output variable ∆<span class="html-italic">V<sub>ref</sub></span>.</p> Full article ">Figure 6
<p>Global schematic of the permanent magnet synchronous motor (PMSM) controlled.</p> Full article ">Figure 7
<p>Representation of inverter status and reference voltage vectors in the stationary reference.</p> Full article ">Figure 8
<p>The simulation block diagram of the solar drive systems.</p> Full article ">Figure 9
<p>Steady-state and dynamic response comparison of the proposed fuzzy logic controller (FLC)-based P&amp;O with the P&amp;O algorithm with step of reference voltage of 0.05 and 0.01. (<b>a</b>) Changes of irradiation; (<b>b</b>) Variable Step of a Reference Voltage; (<b>c</b>) PV array output power.</p> Full article ">Figure 9 Cont.
<p>Steady-state and dynamic response comparison of the proposed fuzzy logic controller (FLC)-based P&amp;O with the P&amp;O algorithm with step of reference voltage of 0.05 and 0.01. (<b>a</b>) Changes of irradiation; (<b>b</b>) Variable Step of a Reference Voltage; (<b>c</b>) PV array output power.</p> Full article ">Figure 10
<p>Simulation results of FLC-based P&amp;O algorithm. Operating at 25 °C and 1000 W/m<sup>2</sup> conditions; (<b>a</b>) <span class="html-italic">P</span>-<span class="html-italic">V</span> curve of <span class="html-italic">PV</span> array, (<b>b</b>) the <span class="html-italic">PV</span> array’s current and voltage, (<b>c</b>) Stator currents, (<b>d</b>) Motor speed, operating at 25 °C and 500 W/m<sup>2</sup> conditions; (<b>e</b>) the PV array’s <span class="html-italic">P</span>-<span class="html-italic">V</span> curve, (<b>f</b>) the parameters (<span class="html-italic">I</span>, <span class="html-italic">V</span>) of the <span class="html-italic">PV</span> array, (<b>g</b>) Stator currents, (<b>h</b>) Motor speed.</p> Full article ">Figure 10 Cont.
<p>Simulation results of FLC-based P&amp;O algorithm. Operating at 25 °C and 1000 W/m<sup>2</sup> conditions; (<b>a</b>) <span class="html-italic">P</span>-<span class="html-italic">V</span> curve of <span class="html-italic">PV</span> array, (<b>b</b>) the <span class="html-italic">PV</span> array’s current and voltage, (<b>c</b>) Stator currents, (<b>d</b>) Motor speed, operating at 25 °C and 500 W/m<sup>2</sup> conditions; (<b>e</b>) the PV array’s <span class="html-italic">P</span>-<span class="html-italic">V</span> curve, (<b>f</b>) the parameters (<span class="html-italic">I</span>, <span class="html-italic">V</span>) of the <span class="html-italic">PV</span> array, (<b>g</b>) Stator currents, (<b>h</b>) Motor speed.</p> Full article ">
31 pages, 13279 KiB  
Article
A Steady-State Analysis Method for Modular Multilevel Converters Connected to Permanent Magnet Synchronous Generator-Based Wind Energy Conversion Systems
by Zhijie Liu, Kejun Li, Yuanyuan Sun, Jinyu Wang, Zhuodi Wang, Kaiqi Sun and Meiyan Wang
Energies 2018, 11(2), 461; https://doi.org/10.3390/en11020461 - 22 Feb 2018
Cited by 24 | Viewed by 4772
Abstract
Modular multilevel converters (MMCs) have shown great potential in the area of multi-megawatt wind energy conversion system (WECS) based on permanent magnet synchronous generators (PMSGs). However, the studies in this area are few, and most of them refer to the MMC used in [...] Read more.
Modular multilevel converters (MMCs) have shown great potential in the area of multi-megawatt wind energy conversion system (WECS) based on permanent magnet synchronous generators (PMSGs). However, the studies in this area are few, and most of them refer to the MMC used in high-voltage direct current (HVDC) systems, and hence the characteristics of the PMSG are not considered. This paper proposes a steady-state analysis method for MMCs connected to a PMSG-based WECS. In the proposed method, only the wind speed (operating condition) is required as input, and all the electrical quantities in the MMC, including the amplitudes, phase angles and their harmonics, can be calculated step by step. The analysis method is built on the proposed d-q frame mathematical model. Interactions of electrical quantities between the MMC and PMSG are comprehensively considered. Moreover, a new way to calculate the average switching functions are adopted in order to improve the accuracy of the analysis method. Applications of the proposed method are also presented, which includes the characteristic analysis of capacitor voltage ripples and the capacitor sizing. Finally, the accuracy of the method and the correctness of the analysis are verified by simulations and experiments. Full article
(This article belongs to the Section F: Electrical Engineering)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Diagram of MMC connected to the PMSG-based WECS.</p> Full article ">Figure 2
<p>D-q frame equivalent circuit of MMC connected to the PMSG-based WECS.</p> Full article ">Figure 3
<p>Control scheme of MMC connected to the PMSG-based WECS.</p> Full article ">Figure 4
<p>Flow chart of the proposed steady-state analysis method.</p> Full article ">Figure 5
<p>Steady-state analysis results of fluctuation magnitude at different frequencies.</p> Full article ">Figure 6
<p>Flow chart of drawing the parameter curve.</p> Full article ">Figure 7
<p>Parameter curve between the capacitance and the voltage fluctuation ratio.</p> Full article ">Figure 8
<p>Diagram of the simulation model.</p> Full article ">Figure 9
<p>Comparisons between modulation ratio and amplitude of IGBT gate signal.</p> Full article ">Figure 10
<p>Comparisons between the simulation and calculation results using the traditional method to calculate the average switching function.</p> Full article ">Figure 11
<p>Comparisons between the simulation results and calculation results. (<b>a</b>) The wind speed is 6 m/s; (<b>b</b>) the wind speed is 9 m/s; (<b>c</b>) the wind speed is 12 m/s (rated wind speed).</p> Full article ">Figure 11 Cont.
<p>Comparisons between the simulation results and calculation results. (<b>a</b>) The wind speed is 6 m/s; (<b>b</b>) the wind speed is 9 m/s; (<b>c</b>) the wind speed is 12 m/s (rated wind speed).</p> Full article ">Figure 12
<p>Comparisons between the simulation results and the calculation results.</p> Full article ">Figure 13
<p>Waveforms when wind speed increases from 6 m/s at 2 s to12 m/s at 6 s. (<b>a</b>) Input power and electromagnetic torque; (<b>b</b>) wind speed and system frequency; (<b>c</b>) capacitor voltage.</p> Full article ">Figure 14
<p>Waveforms of capacitor voltages under the different capacitance at the rated wind speed. (<b>a</b>) <span class="html-italic">C</span> = 4000 μF; (<b>b</b>) <span class="html-italic">C</span> = 5000 μF; (<b>c</b>) <span class="html-italic">C</span> = 6000 μF; (<b>d</b>) <span class="html-italic">C = </span> 7000 μF.</p> Full article ">Figure 15
<p>Down-scaled prototype for MMC connected to the PMSG-based WECS. (<b>a</b>) Photograph, (<b>b</b>) Diagram.</p> Full article ">Figure 16
<p>Comparisons between the experiment waveforms and calculation results when the wind speed is 12 m/s. (<b>a</b>) Capacitor voltage ripples and reference signal; (<b>b</b>) Arm currents and additional signal.</p> Full article ">Figure 17
<p>Comparisons between the experiment waveforms and calculation results when the wind speed is 9 m/s. (<b>a</b>) Capacitor voltage ripples and reference signal; (<b>b</b>) Arm currents and additional signal.</p> Full article ">Figure 18
<p>Comparisons between the experiment waveforms and calculation results when the wind speed is 6 m/s. (<b>a</b>) Capacitor voltage ripples and reference signal; (<b>b</b>) Arm currents and additional signal.</p> Full article ">
14 pages, 5051 KiB  
Article
Estimation of Effective Diffusion Coefficient of O2 in Ash Layer in Underground Coal Gasification by Thermogravimetric Apparatus
by Xi Lin, Qingya Liu and Zhenyu Liu
Energies 2018, 11(2), 460; https://doi.org/10.3390/en11020460 - 22 Feb 2018
Cited by 4 | Viewed by 4434
Abstract
Underground coal gasification (UCG) proceeds generally in the presence of an ash layer on coal (or char) surface. The ash layer increases the mass transfer resistance of O2 to the gasification surface, which may become the limiting step of whole process. This [...] Read more.
Underground coal gasification (UCG) proceeds generally in the presence of an ash layer on coal (or char) surface. The ash layer increases the mass transfer resistance of O2 to the gasification surface, which may become the limiting step of whole process. This paper studies O2 diffusion in ash layer formed on cylindrical char samples using a specially designed one-dimension setup in a thermogravimetric apparatus (TGA). The effective internal diffusion coefficient (De) is found to increase with an increase in ash layer thickness, due to an increase in median pore diameter. Methods are established to correlate De with operating conditions and to estimate the role of internal diffusion resistance in overall mass transfer resistance. Full article
(This article belongs to the Special Issue Biomass Chars: Elaboration, Characterization and Applications Ⅱ)
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<p>Schematic representation of a char sample with one-dimensional ash layer formation.</p> Full article ">Figure 2
<p>The appearance of char sample before and after gasification at 1273 K for 99 min.</p> Full article ">Figure 3
<p>The cross-section view of a sample gasified at 1273 K to different mass losses.</p> Full article ">Figure 4
<p>TG and DTG curves during gasification at 1273 K.</p> Full article ">Figure 5
<p>Diffusion coefficient at different temperatures in gasification.</p> Full article ">Figure 6
<p>Samples subjected to gasification at 1273 K for different time: (<b>a</b>) with an ash layer of 2 mm; (<b>b</b>) with an ash layer of 4 mm.</p> Full article ">Figure 7
<p>Relation between <span class="html-italic">t</span><sup>0.5</sup> and thickness of ash layer <span class="html-italic">L</span>.</p> Full article ">Figure 8
<p>The contribution of <span class="html-italic">L</span>/<span class="html-italic">D<sub>e</sub></span> to overall mass transfer resistance (<span class="html-italic">η</span>) at different ash layer thickness.</p> Full article ">Figure 9
<p>Gasification at 1273 K under different O<sub>2</sub> concentrations.</p> Full article ">Figure 10
<p>Effective diffusion coefficient in ash layer under different temperatures and O<sub>2</sub> concentrations.</p> Full article ">Figure 11
<p>Accuracy of diffusion coefficient (<span class="html-italic">θ</span>) under omitting external diffusion resistance.</p> Full article ">Figure 12
<p>Diffusion coefficient in ash layer under different temperature and ash layer thickness.</p> Full article ">Figure 13
<p>The contribution of <span class="html-italic">L</span>/<span class="html-italic">D<sub>e</sub></span> to overall mass transfer resistance (<span class="html-italic">η</span>) under different temperatures and ash layer thickness.</p> Full article ">
17 pages, 16671 KiB  
Article
Real-Time Genetic Algorithms-Based MPPT: Study and Comparison (Theoretical an Experimental) with Conventional Methods
by Slimane Hadji, Jean-Paul Gaubert and Fateh Krim
Energies 2018, 11(2), 459; https://doi.org/10.3390/en11020459 - 22 Feb 2018
Cited by 96 | Viewed by 7282
Abstract
Maximum Power Point Tracking (MPPT) methods are used in photovoltaic (PV) systems to continually maximize the PV array output power, which strongly depends on both solar radiation and cell temperature. The PV power oscillations around the maximum power point (MPP) resulting from the [...] Read more.
Maximum Power Point Tracking (MPPT) methods are used in photovoltaic (PV) systems to continually maximize the PV array output power, which strongly depends on both solar radiation and cell temperature. The PV power oscillations around the maximum power point (MPP) resulting from the conventional methods and complexity of the non-conventional ones are convincing reasons to look for novel MPPT methods. This paper deals with simple Genetic Algorithms (GAs) based MPPT method in order to improve the convergence, rapidity, and accuracy of the PV system. The proposed method can also efficiently track the global MPP, which is very useful for partial shading. At first, a review of the algorithm is given, followed with many test examples; then, a comparison by means Matlab/Simulink© (R2009b) is conducted between the proposed MPPT and, the popular Perturb and Observe (PO) and Incremental Conductance (IC) techniques. The results show clearly the superiority of the proposed controller. Indeed, with the proposed algorithm, oscillations around the MPP are dramatically minimized, a better stability is observed and increase in the output power efficiency is obtained. All these results are experimentally validated by a test bench developed at LIAS laboratory (Poitiers University, Poitiers, France) using real PV panels and a PV emulator which allows one to define a profile insolation model. In addition, the proposed method permits one to perform the test of linearity between the optimal current I mp (current at maximum power) and the short-circuit current I sc , and between the optimal voltage V mp and open-circuit voltage V oc , so the current and voltage factors can be easily obtained with our algorithm. Full article
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<p>Tracking the MPP: (<b>a</b>) with PO; (<b>b</b>) with IC.</p> Full article ">Figure 2
<p>GAs-based MPPT system.</p> Full article ">Figure 3
<p>Flow chart of GAs-based MPPT.</p> Full article ">Figure 4
<p>Equivalent circuit of solar cell with single diode.</p> Full article ">Figure 5
<p>GAs block used to track the MPP.</p> Full article ">Figure 6
<p>The MPP with GAs: (<b>a</b>) MPP evolution with generations; (<b>b</b>) The position of MPP on the P–V curve.</p> Full article ">Figure 7
<p>Maximum fitness evolution using: (<b>a</b>) 30 individuals; (<b>b</b>) 10 individuals.</p> Full article ">Figure 8
<p>Maximum fitness evolution using: (<b>a</b>) 8 bits; (<b>b</b>) 32 bits.</p> Full article ">Figure 9
<p>PV system with climatic variation.</p> Full article ">Figure 10
<p>Evolution of power with time under insolation model: (<b>a</b>) Insolation; (<b>b</b>) PV power.</p> Full article ">Figure 11
<p>Evolution of power with time under insolation model (GAs and PO).</p> Full article ">Figure 12
<p>GAs-based MPPT and PO controller comparison: (<b>a</b>) response time; (<b>b</b>) stability.</p> Full article ">Figure 13
<p>Evolution of power with time under insolation model (GAs and IC).</p> Full article ">Figure 14
<p>GAs-based MPPT and IC controller comparison: (<b>a</b>) response time; (<b>b</b>) stability.</p> Full article ">Figure 15
<p>The experimental PV system: (<b>a</b>) The test bench; (<b>b</b>) Architecture.</p> Full article ">Figure 16
<p>Experimental evolution of the duty cycle with GAs and IC: (<b>a</b>) <math display="inline"> <semantics> <mrow> <mo>Δ</mo> <mi mathvariant="normal">D</mi> <mo>=</mo> <mn>0.01</mn> </mrow> </semantics> </math>; (<b>b</b>) <math display="inline"> <semantics> <mrow> <mo>Δ</mo> <mi mathvariant="normal">D</mi> <mo>=</mo> <mn>0.02</mn> </mrow> </semantics> </math>.</p> Full article ">Figure 17
<p>Experimental evolution of PV current with GAs and IC: (<b>a</b>) <math display="inline"> <semantics> <mrow> <mo>Δ</mo> <mi mathvariant="normal">D</mi> <mo>=</mo> <mn>0.01</mn> </mrow> </semantics> </math>; (<b>b</b>) <math display="inline"> <semantics> <mrow> <mo>Δ</mo> <mi mathvariant="normal">D</mi> <mo>=</mo> <mn>0.02</mn> </mrow> </semantics> </math>.</p> Full article ">Figure 18
<p>Experimental evolution of PV power with GAs and IC: (<b>a</b>) <math display="inline"> <semantics> <mrow> <mo>Δ</mo> <mi mathvariant="normal">D</mi> <mo>=</mo> <mn>0.01</mn> </mrow> </semantics> </math>; (<b>b</b>) <math display="inline"> <semantics> <mrow> <mo>Δ</mo> <mi mathvariant="normal">D</mi> <mo>=</mo> <mn>0.02</mn> </mrow> </semantics> </math>.</p> Full article ">Figure 19
<p>Experimental evolution of the duty cycle with GAs and PO: (<b>a</b>) <math display="inline"> <semantics> <mrow> <mo>Δ</mo> <mi mathvariant="normal">D</mi> <mo>=</mo> <mn>0.01</mn> </mrow> </semantics> </math>; (<b>b</b>) <math display="inline"> <semantics> <mrow> <mo>Δ</mo> <mi mathvariant="normal">D</mi> <mo>=</mo> <mn>0.02</mn> </mrow> </semantics> </math>.</p> Full article ">Figure 20
<p>Experimental evolution of PV current with GAs and PO: (<b>a</b>) <math display="inline"> <semantics> <mrow> <mo>Δ</mo> <mi mathvariant="normal">D</mi> <mo>=</mo> <mn>0.01</mn> </mrow> </semantics> </math>; (<b>b</b>) <math display="inline"> <semantics> <mrow> <mo>Δ</mo> <mi mathvariant="normal">D</mi> <mo>=</mo> <mn>0.02</mn> </mrow> </semantics> </math>.</p> Full article ">Figure 21
<p>Experimental evolution of PV power with GAs and PO: (<b>a</b>) <math display="inline"> <semantics> <mrow> <mo>Δ</mo> <mi mathvariant="normal">D</mi> <mo>=</mo> <mn>0.01</mn> </mrow> </semantics> </math>; (<b>b</b>) <math display="inline"> <semantics> <mrow> <mo>Δ</mo> <mi mathvariant="normal">D</mi> <mo>=</mo> <mn>0.02</mn> </mrow> </semantics> </math>.</p> Full article ">Figure 22
<p>Experimental test with PV Emulator: (<b>a</b>) The insolation profile; (<b>b</b>) The PV Emulator.</p> Full article ">Figure 23
<p>Evolution of the duty cycle with GAs and IC: (<b>a</b>) <math display="inline"> <semantics> <mrow> <mo>Δ</mo> <mi mathvariant="normal">D</mi> <mo>=</mo> <mn>0.01</mn> </mrow> </semantics> </math>; (<b>b</b>) <math display="inline"> <semantics> <mrow> <mo>Δ</mo> <mi mathvariant="normal">D</mi> <mo>=</mo> <mn>0.02</mn> </mrow> </semantics> </math>.</p> Full article ">Figure 24
<p>Evolution of PV power with GAs and IC: (<b>a</b>) <math display="inline"> <semantics> <mrow> <mo>Δ</mo> <mi mathvariant="normal">D</mi> <mo>=</mo> <mn>0.01</mn> </mrow> </semantics> </math>; (<b>b</b>) <math display="inline"> <semantics> <mrow> <mo>Δ</mo> <mi mathvariant="normal">D</mi> <mo>=</mo> <mn>0.02</mn> </mrow> </semantics> </math>.</p> Full article ">Figure 25
<p>Evolution of duty cycle with GAs and PO: (<b>a</b>) <math display="inline"> <semantics> <mrow> <mo>Δ</mo> <mi mathvariant="normal">D</mi> <mo>=</mo> <mn>0.01</mn> </mrow> </semantics> </math>; (<b>b</b>) <math display="inline"> <semantics> <mrow> <mo>Δ</mo> <mi mathvariant="normal">D</mi> <mo>=</mo> <mn>0.02</mn> </mrow> </semantics> </math>.</p> Full article ">Figure 26
<p>Evolution of PV power with GAs and PO: (<b>a</b>) <math display="inline"> <semantics> <mrow> <mo>Δ</mo> <mi mathvariant="normal">D</mi> <mo>=</mo> <mn>0.01</mn> </mrow> </semantics> </math>; (<b>b</b>) <math display="inline"> <semantics> <mrow> <mo>Δ</mo> <mi mathvariant="normal">D</mi> <mo>=</mo> <mn>0.02</mn> </mrow> </semantics> </math>.</p> Full article ">Figure 27
<p>Verification of the linear MPPT method: (<b>a</b>) I<sub>mp</sub>–I<sub>sc</sub> curve; (<b>b</b>) V<sub>mp</sub>–V<sub>oc</sub> curve.</p> Full article ">
16 pages, 6681 KiB  
Article
Selective Harmonic Elimination in a Wide Modulation Range Using Modified Newton–Raphson and Pattern Generation Methods for a Multilevel Inverter
by Mohammed Al-Hitmi, Salman Ahmad, Atif Iqbal, Sanjeevikumar Padmanaban and Imtiaz Ashraf
Energies 2018, 11(2), 458; https://doi.org/10.3390/en11020458 - 22 Feb 2018
Cited by 46 | Viewed by 5775
Abstract
Considering the aim of having low switching losses, especially in medium-voltage and high-power converters, the pre-programmed pulse width modulation technique is very useful because the generated harmonic content can be known in advance and optimized. Among the different low switching frequency techniques, the [...] Read more.
Considering the aim of having low switching losses, especially in medium-voltage and high-power converters, the pre-programmed pulse width modulation technique is very useful because the generated harmonic content can be known in advance and optimized. Among the different low switching frequency techniques, the Selective Harmonics Elimination (SHE) modulation method is most suitable because of its direct control over the harmonic spectrum. This paper proposes a method for obtaining multiple solutions for selectively eliminating specific harmonics in a wide range of modulation indices by using modified Newton–Raphson (NR) and pattern generation techniques. The different pattern generation and synthesis approach provide more degrees of freedom and a way to operate the converter in a wide range of modulation. The modified Newton–Raphson technique is not complex and ensures fast convergence on a solution. Moreover, multiple solutions are obtained by keeping a very small increase in the modulation index. In the previous methods, solutions were not obtainable at all modulation indices. In this paper, only exact solutions to the low-order harmonics elimination for Cascaded H-bridge inverter are reported for all modulation indices. Analytical and simulation results prove the robustness and correctness of the technique proposed in this paper. Full article
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Figure 1
<p>Eleven-level, three-phase cascaded H-bridge inverter.</p> Full article ">Figure 2
<p>Generalized quarter wave symmetrical L-level output voltage waveform.</p> Full article ">Figure 3
<p>Differentpattern synthesis switching transitions from the 11-level cascaded H-bridge inverter. (<b>a</b>) <math display="inline"> <semantics> <mrow> <msub> <mi>α</mi> <mrow> <mn>1</mn> <mo>↑</mo> </mrow> </msub> <mo>,</mo> <msub> <mi>α</mi> <mrow> <mn>2</mn> <mo>↑</mo> </mrow> </msub> <mo>,</mo> <msub> <mi>α</mi> <mrow> <mn>3</mn> <mo>↑</mo> </mrow> </msub> <mo>,</mo> <msub> <mi>α</mi> <mrow> <mn>4</mn> <mo>↑</mo> </mrow> </msub> <mo>,</mo> <msub> <mi>α</mi> <mrow> <mn>5</mn> <mo>↑</mo> </mrow> </msub> </mrow> </semantics> </math>; (<b>b</b>) <math display="inline"> <semantics> <mrow> <msub> <mi>α</mi> <mrow> <mn>1</mn> <mo>↑</mo> </mrow> </msub> <mo>,</mo> <msub> <mi>α</mi> <mrow> <mn>2</mn> <mo>↑</mo> </mrow> </msub> <mo>,</mo> <msub> <mi>α</mi> <mrow> <mn>3</mn> <mo>↑</mo> </mrow> </msub> <mo>,</mo> <msub> <mi>α</mi> <mrow> <mn>4</mn> <mo>↑</mo> </mrow> </msub> <mo>,</mo> <msub> <mi>α</mi> <mrow> <mn>5</mn> <mo>↓</mo> </mrow> </msub> </mrow> </semantics> </math>; (<b>c</b>) <math display="inline"> <semantics> <mrow> <msub> <mi>α</mi> <mrow> <mn>1</mn> <mo>↑</mo> </mrow> </msub> <mo>,</mo> <msub> <mi>α</mi> <mrow> <mn>2</mn> <mo>↑</mo> </mrow> </msub> <mo>,</mo> <msub> <mi>α</mi> <mrow> <mn>3</mn> <mo>↑</mo> </mrow> </msub> <mo>,</mo> <msub> <mi>α</mi> <mrow> <mn>4</mn> <mo>↓</mo> </mrow> </msub> <mo>,</mo> <msub> <mi>α</mi> <mrow> <mn>5</mn> <mo>↑</mo> </mrow> </msub> </mrow> </semantics> </math>; (<b>d</b>) <math display="inline"> <semantics> <mrow> <msub> <mi>α</mi> <mrow> <mn>1</mn> <mo>↑</mo> </mrow> </msub> <mo>,</mo> <msub> <mi>α</mi> <mrow> <mn>2</mn> <mo>↑</mo> </mrow> </msub> <mo>,</mo> <msub> <mi>α</mi> <mrow> <mn>3</mn> <mo>↓</mo> </mrow> </msub> <mo>,</mo> <msub> <mi>α</mi> <mrow> <mn>4</mn> <mo>↑</mo> </mrow> </msub> <mo>,</mo> <msub> <mi>α</mi> <mrow> <mn>5</mn> <mo>↑</mo> </mrow> </msub> </mrow> </semantics> </math>; (<b>e</b>) <math display="inline"> <semantics> <mrow> <msub> <mi>α</mi> <mrow> <mn>1</mn> <mo>↑</mo> </mrow> </msub> <mo>,</mo> <msub> <mi>α</mi> <mrow> <mn>2</mn> <mo>↓</mo> </mrow> </msub> <mo>,</mo> <msub> <mi>α</mi> <mrow> <mn>3</mn> <mo>↑</mo> </mrow> </msub> <mo>,</mo> <msub> <mi>α</mi> <mrow> <mn>4</mn> <mo>↑</mo> </mrow> </msub> <mo>,</mo> <msub> <mi>α</mi> <mrow> <mn>5</mn> <mo>↑</mo> </mrow> </msub> </mrow> </semantics> </math>; (<b>f</b>) <math display="inline"> <semantics> <mrow> <msub> <mi>α</mi> <mrow> <mn>1</mn> <mo>↑</mo> </mrow> </msub> <mo>,</mo> <msub> <mi>α</mi> <mrow> <mn>2</mn> <mo>↑</mo> </mrow> </msub> <mo>,</mo> <msub> <mi>α</mi> <mrow> <mn>3</mn> <mo>↑</mo> </mrow> </msub> <mo>,</mo> <msub> <mi>α</mi> <mrow> <mn>4</mn> <mo>↓</mo> </mrow> </msub> <mo>,</mo> <msub> <mi>α</mi> <mrow> <mn>5</mn> <mo>↓</mo> </mrow> </msub> </mrow> </semantics> </math>; (<b>g</b>) <math display="inline"> <semantics> <mrow> <msub> <mi>α</mi> <mrow> <mn>1</mn> <mo>↑</mo> </mrow> </msub> <mo>,</mo> <msub> <mi>α</mi> <mrow> <mn>2</mn> <mo>↑</mo> </mrow> </msub> <mo>,</mo> <msub> <mi>α</mi> <mrow> <mn>3</mn> <mo>↓</mo> </mrow> </msub> <mo>,</mo> <msub> <mi>α</mi> <mrow> <mn>4</mn> <mo>↑</mo> </mrow> </msub> <mo>,</mo> <msub> <mi>α</mi> <mrow> <mn>5</mn> <mo>↓</mo> </mrow> </msub> </mrow> </semantics> </math>; (<b>h</b>) <math display="inline"> <semantics> <mrow> <msub> <mi>α</mi> <mrow> <mn>1</mn> <mo>↑</mo> </mrow> </msub> <mo>,</mo> <msub> <mi>α</mi> <mrow> <mn>2</mn> <mo>↑</mo> </mrow> </msub> <mo>,</mo> <msub> <mi>α</mi> <mrow> <mn>3</mn> <mo>↓</mo> </mrow> </msub> <mo>,</mo> <msub> <mi>α</mi> <mrow> <mn>4</mn> <mo>↓</mo> </mrow> </msub> <mo>,</mo> <msub> <mi>α</mi> <mrow> <mn>5</mn> <mo>↑</mo> </mrow> </msub> </mrow> </semantics> </math>; (<b>i</b>) <math display="inline"> <semantics> <mrow> <msub> <mi>α</mi> <mrow> <mn>1</mn> <mo>↑</mo> </mrow> </msub> <mo>,</mo> <msub> <mi>α</mi> <mrow> <mn>2</mn> <mo>↓</mo> </mrow> </msub> <mo>,</mo> <msub> <mi>α</mi> <mrow> <mn>3</mn> <mo>↑</mo> </mrow> </msub> <mo>,</mo> <msub> <mi>α</mi> <mrow> <mn>4</mn> <mo>↓</mo> </mrow> </msub> <mo>,</mo> <msub> <mi>α</mi> <mrow> <mn>5</mn> <mo>↑</mo> </mrow> </msub> </mrow> </semantics> </math>.</p> Full article ">Figure 4
<p>Flow chart of modified NR for solving SHE problem.</p> Full article ">Figure 5
<p>Switching angles (in radians) as function of modulation <span class="html-italic">M</span> for various switching transition (<b>a</b>) <math display="inline"> <semantics> <mrow> <msub> <mi>α</mi> <mrow> <mn>1</mn> <mo>↑</mo> </mrow> </msub> <mo>,</mo> <msub> <mi>α</mi> <mrow> <mn>2</mn> <mo>↑</mo> </mrow> </msub> <mo>,</mo> <msub> <mi>α</mi> <mrow> <mn>3</mn> <mo>↑</mo> </mrow> </msub> <mo>,</mo> <msub> <mi>α</mi> <mrow> <mn>4</mn> <mo>↑</mo> </mrow> </msub> <mo>,</mo> <msub> <mi>α</mi> <mrow> <mn>5</mn> <mo>↑</mo> </mrow> </msub> </mrow> </semantics> </math>; (<b>b</b>) <math display="inline"> <semantics> <mrow> <msub> <mi>α</mi> <mrow> <mn>1</mn> <mo>↑</mo> </mrow> </msub> <mo>,</mo> <msub> <mi>α</mi> <mrow> <mn>2</mn> <mo>↑</mo> </mrow> </msub> <mo>,</mo> <msub> <mi>α</mi> <mrow> <mn>3</mn> <mo>↑</mo> </mrow> </msub> <mo>,</mo> <msub> <mi>α</mi> <mrow> <mn>4</mn> <mo>↑</mo> </mrow> </msub> <mo>,</mo> <msub> <mi>α</mi> <mrow> <mn>5</mn> <mo>↓</mo> </mrow> </msub> </mrow> </semantics> </math>; (<b>c</b>) <math display="inline"> <semantics> <mrow> <msub> <mi>α</mi> <mrow> <mn>1</mn> <mo>↑</mo> </mrow> </msub> <mo>,</mo> <msub> <mi>α</mi> <mrow> <mn>2</mn> <mo>↑</mo> </mrow> </msub> <mo>,</mo> <msub> <mi>α</mi> <mrow> <mn>3</mn> <mo>↑</mo> </mrow> </msub> <mo>,</mo> <msub> <mi>α</mi> <mrow> <mn>4</mn> <mo>↓</mo> </mrow> </msub> <mo>,</mo> <msub> <mi>α</mi> <mrow> <mn>5</mn> <mo>↑</mo> </mrow> </msub> </mrow> </semantics> </math>; (<b>d</b>) <math display="inline"> <semantics> <mrow> <msub> <mi>α</mi> <mrow> <mn>1</mn> <mo>↑</mo> </mrow> </msub> <mo>,</mo> <msub> <mi>α</mi> <mrow> <mn>2</mn> <mo>↑</mo> </mrow> </msub> <mo>,</mo> <msub> <mi>α</mi> <mrow> <mn>3</mn> <mo>↓</mo> </mrow> </msub> <mo>,</mo> <msub> <mi>α</mi> <mrow> <mn>4</mn> <mo>↑</mo> </mrow> </msub> <mo>,</mo> <msub> <mi>α</mi> <mrow> <mn>5</mn> <mo>↑</mo> </mrow> </msub> </mrow> </semantics> </math>; (<b>e</b>) <math display="inline"> <semantics> <mrow> <msub> <mi>α</mi> <mrow> <mn>1</mn> <mo>↑</mo> </mrow> </msub> <mo>,</mo> <msub> <mi>α</mi> <mrow> <mn>2</mn> <mo>↓</mo> </mrow> </msub> <mo>,</mo> <msub> <mi>α</mi> <mrow> <mn>3</mn> <mo>↑</mo> </mrow> </msub> <mo>,</mo> <msub> <mi>α</mi> <mrow> <mn>4</mn> <mo>↑</mo> </mrow> </msub> <mo>,</mo> <msub> <mi>α</mi> <mrow> <mn>5</mn> <mo>↑</mo> </mrow> </msub> </mrow> </semantics> </math>; (<b>f</b>) <math display="inline"> <semantics> <mrow> <msub> <mi>α</mi> <mrow> <mn>1</mn> <mo>↑</mo> </mrow> </msub> <mo>,</mo> <msub> <mi>α</mi> <mrow> <mn>2</mn> <mo>↑</mo> </mrow> </msub> <mo>,</mo> <msub> <mi>α</mi> <mrow> <mn>3</mn> <mo>↑</mo> </mrow> </msub> <mo>,</mo> <msub> <mi>α</mi> <mrow> <mn>4</mn> <mo>↓</mo> </mrow> </msub> <mo>,</mo> <msub> <mi>α</mi> <mrow> <mn>5</mn> <mo>↓</mo> </mrow> </msub> </mrow> </semantics> </math>; (<b>g</b>) <math display="inline"> <semantics> <mrow> <msub> <mi>α</mi> <mrow> <mn>1</mn> <mo>↑</mo> </mrow> </msub> <mo>,</mo> <msub> <mi>α</mi> <mrow> <mn>2</mn> <mo>↑</mo> </mrow> </msub> <mo>,</mo> <msub> <mi>α</mi> <mrow> <mn>3</mn> <mo>↓</mo> </mrow> </msub> <mo>,</mo> <msub> <mi>α</mi> <mrow> <mn>4</mn> <mo>↑</mo> </mrow> </msub> <mo>,</mo> <msub> <mi>α</mi> <mrow> <mn>5</mn> <mo>↓</mo> </mrow> </msub> </mrow> </semantics> </math>; (<b>h</b>) <math display="inline"> <semantics> <mrow> <msub> <mi>α</mi> <mrow> <mn>1</mn> <mo>↑</mo> </mrow> </msub> <mo>,</mo> <msub> <mi>α</mi> <mrow> <mn>2</mn> <mo>↑</mo> </mrow> </msub> <mo>,</mo> <msub> <mi>α</mi> <mrow> <mn>3</mn> <mo>↓</mo> </mrow> </msub> <mo>,</mo> <msub> <mi>α</mi> <mrow> <mn>4</mn> <mo>↓</mo> </mrow> </msub> <mo>,</mo> <msub> <mi>α</mi> <mrow> <mn>5</mn> <mo>↑</mo> </mrow> </msub> </mrow> </semantics> </math>; (<b>i</b>) <math display="inline"> <semantics> <mrow> <msub> <mi>α</mi> <mrow> <mn>1</mn> <mo>↑</mo> </mrow> </msub> <mo>,</mo> <msub> <mi>α</mi> <mrow> <mn>2</mn> <mo>↓</mo> </mrow> </msub> <mo>,</mo> <msub> <mi>α</mi> <mrow> <mn>3</mn> <mo>↑</mo> </mrow> </msub> <mo>,</mo> <msub> <mi>α</mi> <mrow> <mn>4</mn> <mo>↓</mo> </mrow> </msub> <mo>,</mo> <msub> <mi>α</mi> <mrow> <mn>5</mn> <mo>↑</mo> </mrow> </msub> </mrow> </semantics> </math>.</p> Full article ">Figure 6
<p>% Line THD for the entire pattern as a function of modulation index <span class="html-italic">M</span>.</p> Full article ">Figure 7
<p>Switching angle as a function of m for all the patterns.</p> Full article ">Figure 8
<p>(<b>a</b>) Maximum error in the function for pattern “<span class="html-italic">a</span>”; (<b>b</b>) harmonics profile at <span class="html-italic">M</span> = 0.845.</p> Full article ">Figure 9
<p>Simulation result for pattern ‘<span class="html-italic">a</span>’ at <span class="html-italic">M</span> = 0.6000 (<b>a</b>) Phase voltage; (<b>b</b>) line voltage; (<b>c</b>) harmonics spectrum.</p> Full article ">Figure 10
<p>Experimental setup.</p> Full article ">Figure 11
<p>Experimental results for pattern ‘<span class="html-italic">a</span>’ (<b>a</b>) output voltage waveform at <span class="html-italic">M</span> = 0.432; (<b>b</b>) FFT spectrum.</p> Full article ">Figure 12
<p>Experimental results of output voltage and THD of pattern of <a href="#energies-11-00458-f003" class="html-fig">Figure 3</a>b in (<b>a</b>,<b>b</b>) respectively.</p> Full article ">Figure 13
<p>Experimental results of output voltage and THD of pattern of <a href="#energies-11-00458-f003" class="html-fig">Figure 3</a>c in (<b>a</b>,<b>b</b>) respectively.</p> Full article ">Figure 14
<p>Summary report presenting FPGA devices’ utilization.</p> Full article ">
21 pages, 22211 KiB  
Article
Suitability Evaluation of Specific Shallow Geothermal Technologies Using a GIS-Based Multi Criteria Decision Analysis Implementing the Analytic Hierarchic Process
by Francesco Tinti, Sara Kasmaee, Mohamed Elkarmoty, Stefano Bonduà and Villiam Bortolotti
Energies 2018, 11(2), 457; https://doi.org/10.3390/en11020457 - 22 Feb 2018
Cited by 31 | Viewed by 7168
Abstract
The exploitation potential of shallow geothermal energy is usually defined in terms of site-specific ground thermal characteristics. While true, this assumption limits the complexity of the analysis, since feasibility studies involve many other components that must be taken into account when calculating the [...] Read more.
The exploitation potential of shallow geothermal energy is usually defined in terms of site-specific ground thermal characteristics. While true, this assumption limits the complexity of the analysis, since feasibility studies involve many other components that must be taken into account when calculating the effective market viability of a geothermal technology or the economic value of a shallow geothermal project. In addition, the results of a feasibility study are not simply the sum of the various factors since some components may be conflicting while others will be of a qualitative nature only. Different approaches are therefore needed to evaluate the suitability of an area for shallow geothermal installation. This paper introduces a new GIS platform-based multicriteria decision analysis method aimed at comparing as many different shallow geothermal relevant factors as possible. Using the Analytic Hierarchic Process Tool, a geolocalized Suitability Index was obtained for a specific technological case: the integrated technologies developed within the GEOTeCH Project. A suitability map for the technologies in question was drawn up for Europe. Full article
(This article belongs to the Special Issue Geothermal Heating and Cooling)
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<p>Workflow of the methodology developed within the GEOTeCH project to provide a suitability assessment of the technologies developed.</p> Full article ">Figure 2
<p>European grid (<b>Left</b>) with resolution of 1 × 1 km<sup>2</sup> (<b>Right</b>); zoom extracted from the country of Andorra.</p> Full article ">Figure 3
<p>Percentage probability of suitable conditions for drilling in Europe.</p> Full article ">Figure 4
<p>Estimation of average (<b>Left</b>) and standard deviation (<b>Right</b>) of top layer depth of neutral zone in Europe.</p> Full article ">Figure 5
<p>Estimation of average (<b>Left</b>) and standard deviation (<b>Right</b>) of bottom layer depth of neutral zone in Europe.</p> Full article ">Figure 6
<p>Estimation of average (<b>Left</b>) and standard deviation (<b>Right</b>) of ground temperature at neutral zone depth in Europe.</p> Full article ">Figure 7
<p>Estimation of average (<b>Left</b>) and standard deviation (<b>Right</b>) of ground temperature at 50 m depth in Europe.</p> Full article ">Figure 8
<p>Qualitative suitability of GEOTeCH technology in Europe.</p> Full article ">
16 pages, 5498 KiB  
Article
Experimental and Numerical Vibrational Analysis of a Horizontal-Axis Micro-Wind Turbine
by Francesco Castellani, Davide Astolfi, Matteo Becchetti, Francesco Berno, Filippo Cianetti and Alessandro Cetrini
Energies 2018, 11(2), 456; https://doi.org/10.3390/en11020456 - 22 Feb 2018
Cited by 30 | Viewed by 5879
Abstract
Micro-wind turbines are energy conversion technologies strongly affected by fatigue, as a result of their size and the variability of loads, induced by the unsteady wind conditions, and modulated by a very high rotational speed. This work is devoted to the experimental and [...] Read more.
Micro-wind turbines are energy conversion technologies strongly affected by fatigue, as a result of their size and the variability of loads, induced by the unsteady wind conditions, and modulated by a very high rotational speed. This work is devoted to the experimental and numerical characterization of the aeroelastic behavior of a test-case horizontal-axis wind turbine (HAWT) with a 2 m rotor diameter and a maximum power production of 3 kW. The experimental studies have been conducted at the wind tunnel of the University of Perugia and consisted of accelerometer measurements at the tower and the tail fin. The numerical setup was the Fatigue, Aerodynamics, Structures, and Turbulence (FAST) code for aeroelastic simulations, which was fed as input with the same wind conditions employed in the wind tunnel tests. The experimental and numerical analyses were coupled with the perspective of establishing a reciprocal feedback, and this has been accomplished. On one hand, the numerical model is important for interpreting the measured spectrum of tower oscillations and, for example, inspires the detection of a mass unbalance at the blades. On the other hand, the measurements inspire the question of how to interpret the interaction between the blades and the tower. The experimental spectrum of tail fin vibrations indicates that secondary elements, in terms of weight, can also transmit to the tower, giving meaningful contributions to the vibration spectra. Therefore, an integrated numerical and experimental approach is not only valuable but is also unavoidable, to fully characterize the dynamics of small wind-energy conversion systems. Full article
(This article belongs to the Special Issue Wind Turbine Loads and Wind Plant Performance)
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<p>The front view of the wind turbine in the wind tunnel with the recovery test section (2.7 m × 2.7 m) on the background.</p> Full article ">Figure 2
<p>The layout of the sensor arrangement.</p> Full article ">Figure 3
<p>The wind tunnel.</p> Full article ">Figure 4
<p>Flowchart for a FAST (Fatigue, Aerodynamics, Structures, and Turbulence) v7 simulation.</p> Full article ">Figure 5
<p>Reference axes for FAST tower references (black), and reference axes for accelerometer measurements (red). The figure is not drawn to scale.</p> Full article ">Figure 6
<p>Simulation of the first resonant frequency of the blades as a function of the rotational speed.</p> Full article ">Figure 7
<p>Waterfall of <span class="html-italic">Y</span> direction tower vibrations from wind tunnel measurements.</p> Full article ">Figure 8
<p>Order spectrum of the experimental <span class="html-italic">Y</span> direction tower oscillations.</p> Full article ">Figure 9
<p>Order spectrum of the experimental <span class="html-italic">Y</span> direction tower oscillations.</p> Full article ">Figure 10
<p>Revolution per minute/acceleration plot for the individual <span class="html-italic">Y</span> direction tower harmonics.</p> Full article ">Figure 11
<p>Comparison of raw experimental data (red) against filtered experimental data (blue) and simulated data (light blue): time series.</p> Full article ">Figure 12
<p>Comparison of raw experimental data (red) against filtered experimental data (blue) and simulated data (light blue): revolutions per minute/acceleration.</p> Full article ">Figure 13
<p>Order spectrum for <span class="html-italic">Y</span> direction tower vibrations from the FAST model: rotor balanced.</p> Full article ">Figure 14
<p>Order spectrum for <span class="html-italic">Y</span> direction tower vibrations from the FAST model: rotor unbalanced.</p> Full article ">Figure 15
<p>Comparison of measured acceleration on tower in <span class="html-italic">Y</span> direction against the amplitude of acceleration predicted by the mass unbalance model.</p> Full article ">Figure 16
<p>Comparison of measured acceleration on tower in <span class="html-italic">Y</span> direction, band-passed around the 1P frequency, against the amplitude of the acceleration predicted by the unbalanced mass model.</p> Full article ">Figure 17
<p>Waterfall of vibration of the tail fin in the <span class="html-italic">Y</span> direction.</p> Full article ">Figure 18
<p>Order of vibration of the tail fin in the <span class="html-italic">Y</span> direction.</p> Full article ">Figure 19
<p>Waterfall of vibration of the tail fin in the <span class="html-italic">Y</span> direction under the ramp wind time series.</p> Full article ">Figure 20
<p>Waterfall of vibration of the tail fin in the <span class="html-italic">Y</span> direction under the oscillatory wind time series.</p> Full article ">
26 pages, 9904 KiB  
Article
Height Adjustment of Vehicles Based on a Static Equilibrium Position State Observation Algorithm
by Zepeng Gao, Sizhong Chen, Yuzhuang Zhao and Jinrui Nan
Energies 2018, 11(2), 455; https://doi.org/10.3390/en11020455 - 21 Feb 2018
Cited by 12 | Viewed by 5474
Abstract
In this paper, a static state observer algorithm based on the static equilibrium position is proposed, which can realize accurate control of electric vehicle height adjustment with existing road excitation. The existence of road excitation can lead to deflection variation of the electronically [...] Read more.
In this paper, a static state observer algorithm based on the static equilibrium position is proposed, which can realize accurate control of electric vehicle height adjustment with existing road excitation. The existence of road excitation can lead to deflection variation of the electronically controlled air suspension (ECAS). The use of only dynamic deflection as the reference for the electric vehicle height adjustment will produce great errors. Therefore, this paper provides an observation algorithm, which can realize the accurate control of vehicle height. Firstly, the static equilibrium position equation of suspension is derived according to the theory of hydrodynamics and characteristics of pneumatic chamber. Secondly, a vehicle dynamics model with seven degrees of freedom (7-DOF) is established and the kinetic equations are discretized. Then, the unscented Kalman filter (UKF) algorithm is used to obtain the static equilibrium position of vehicle. According to the vehicle static equilibrium position obtained by UKF, the height of the vehicle is adjusted by using a fuzzy controller. The simulation and experimental results show that this proposed algorithm can realize the control of vehicle height with an accuracy of over 96%, which ensures the excellent driving performance of vehicles under different road conditions. Full article
(This article belongs to the Special Issue The International Symposium on Electric Vehicles (ISEV2017))
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Schematic diagram of electronically controlled air suspension system.</p> Full article ">Figure 2
<p>7 DOF dynamic model.</p> Full article ">Figure 3
<p>Vehicle height adjustment system.</p> Full article ">Figure 4
<p>Flow chart of control strategy: (<b>a</b>) Adjustment according to the dynamic deflection; (<b>b</b>) Adjustment according to static equilibrium position.</p> Full article ">Figure 5
<p>Phenomena during the process of height adjustment.</p> Full article ">Figure 6
<p>Iteration of UKF.</p> Full article ">Figure 7
<p>Membership function of input variables: (<b>a</b>) error; (<b>b</b>) change rate of error.</p> Full article ">Figure 8
<p>System responses after solenoid valves open at 2 s: (<b>a</b>) intake valve opening; (<b>b</b>) exhaust valve opening.</p> Full article ">Figure 9
<p>System responses: (<b>a</b>) front left suspension; (<b>b</b>) rear left suspension.</p> Full article ">Figure 10
<p>System responses: (<b>a</b>) height change; (<b>b</b>) pitch angle change.</p> Full article ">Figure 11
<p>System responses: (<b>a</b>) front left suspension; (<b>b</b>) rear left suspension.</p> Full article ">Figure 12
<p>System responses: (<b>a</b>) height change; (<b>b</b>) pitch angle change.</p> Full article ">Figure 13
<p>Excitation signal of a speed bump.</p> Full article ">Figure 14
<p>System responses: (<b>a</b>) front left and front right suspensions; (<b>b</b>) rear left and rear right suspensions.</p> Full article ">Figure 15
<p>System responses: (<b>a</b>) height change; (<b>b</b>) pitch angle change.</p> Full article ">Figure 16
<p>System responses: (<b>a</b>) front left suspension; (<b>b</b>) rear left suspension.</p> Full article ">Figure 17
<p>System responses: (<b>a</b>) height change; (<b>b</b>) changes of pitch angle and roll angle.</p> Full article ">Figure 18
<p>Vehicle test.</p> Full article ">Figure 19
<p>Air suspension stiffness characteristics.</p> Full article ">Figure 20
<p>Pressure changes: (<b>a</b>) inflating; (<b>b</b>) deflating.</p> Full article ">Figure 21
<p>Vehicle centroid height changes: (<b>a</b>) suspensions inflating; (<b>b</b>) suspensions inflating.</p> Full article ">Figure 22
<p>Height changes: (<b>a</b>) suspension inflating; (<b>b</b>) suspension deflating.</p> Full article ">Figure 23
<p>Height changes: (<b>a</b>) height lifting; (<b>b</b>) height lowering.</p> Full article ">Figure 24
<p>Error changes: (<b>a</b>) height lifting; (<b>b</b>) height lowering.</p> Full article ">Figure 25
<p>Height changes: (<b>a</b>) height lifting; (<b>b</b>) height lowering.</p> Full article ">Figure 26
<p>Error changes: (<b>a</b>) height lifting; (<b>b</b>) height lowering.</p> Full article ">Figure 27
<p>State of solenoid valve: (<b>a</b>) intake solenoid valve; (<b>b</b>) exhaust solenoid valve.</p> Full article ">
14 pages, 3272 KiB  
Article
Optimal Capacity Configuration of a Hybrid Energy Storage System for an Isolated Microgrid Using Quantum-Behaved Particle Swarm Optimization
by Hui Wang, Tengxin Wang, Xiaohan Xie, Zhixiang Ling, Guoliang Gao and Xu Dong
Energies 2018, 11(2), 454; https://doi.org/10.3390/en11020454 - 21 Feb 2018
Cited by 31 | Viewed by 4405
Abstract
The capacity of an energy storage device configuration not only affects the economic operation of a microgrid, but also affects the power supply’s reliability. An isolated microgrid is considered with typical loads, renewable energy resources, and a hybrid energy storage system (HESS) composed [...] Read more.
The capacity of an energy storage device configuration not only affects the economic operation of a microgrid, but also affects the power supply’s reliability. An isolated microgrid is considered with typical loads, renewable energy resources, and a hybrid energy storage system (HESS) composed of batteries and ultracapacitors in this paper. A quantum-behaved particle swarm optimization (QPSO) algorithm that optimizes the HESS capacity is used. Based on the respective power compensation capabilities of ultracapacitors and batteries, a rational energy scheduling strategy is proposed using the principle of a low-pass filter and can help to avoid frequent batteries charging and discharging. Considering the rated power of each energy storage type, the respective compensation power is corrected. By determining whether the charging state reaches the limit, the value is corrected again. Additionally, a mathematical model that minimizes the daily cost of the HESS is derived. This paper takes an isolated micrgrid in north China as an example to verify the effectiveness of this method. The comparison between QPSO and a traditional particle swarm algorithm shows that QPSO can find the optimal solution faster and the HESS has lower daily cost. Simulation results for an isolated microgrid verified the effectiveness of the HESS optimal capacity configuration method. Full article
(This article belongs to the Section D: Energy Storage and Application)
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Figure 1
<p>Schematic diagram of an isolated microgrid.</p> Full article ">Figure 2
<p>Power revision processes for batteries and ultracapacitors.</p> Full article ">Figure 3
<p>Wind, photovoltaicunit (PV) output and load curve for an isolated microgrid.</p> Full article ">Figure 4
<p>Ideal power value of the hybrid energy storage systems (HESS), <math display="inline"> <semantics> <msubsup> <mi>P</mi> <mrow> <mi>H</mi> <mi>E</mi> <mi>S</mi> <mi>S</mi> </mrow> <mo>*</mo> </msubsup> </semantics> </math>.</p> Full article ">Figure 5
<p>Spectral analysis of <math display="inline"> <semantics> <msubsup> <mi>P</mi> <mrow> <mi>H</mi> <mi>E</mi> <mi>S</mi> <mi>S</mi> </mrow> <mo>*</mo> </msubsup> </semantics> </math>.</p> Full article ">Figure 6
<p>Charging and discharging power of the HESS.</p> Full article ">Figure 7
<p><math display="inline"> <semantics> <msubsup> <mi>P</mi> <mrow> <mi>H</mi> <mi>E</mi> <mi>S</mi> <mi>S</mi> </mrow> <mo>*</mo> </msubsup> </semantics> </math> and actual HESS power for sample points 1–48.</p> Full article ">Figure 8
<p>Actual HESS power and <math display="inline"> <semantics> <msubsup> <mi>P</mi> <mrow> <mi>H</mi> <mi>E</mi> <mi>S</mi> <mi>S</mi> </mrow> <mo>*</mo> </msubsup> </semantics> </math> for sample points 150–180.</p> Full article ">Figure 9
<p>Comparison of optimization using two methods.</p> Full article ">Figure 10
<p>Simulation model of microgrid.</p> Full article ">Figure 11
<p>Battery output power.</p> Full article ">Figure 12
<p>Ultracapacitor output power.</p> Full article ">Figure 13
<p>System voltage and frequency: (<b>a</b>) system voltage; (<b>b</b>) system frequency.</p> Full article ">
23 pages, 8154 KiB  
Article
Insights into Dynamic Tuning of Magnetic-Resonant Wireless Power Transfer Receivers Based on Switch-Mode Gyrators
by Mohamed Saad and Eduard Alarcón
Energies 2018, 11(2), 453; https://doi.org/10.3390/en11020453 - 20 Feb 2018
Cited by 7 | Viewed by 5645
Abstract
Magnetic-resonant wireless power transfer (WPT) has become a reliable contactless source of power for a wide range of applications. WPT spans different power levels ranging from low-power implantable devices up to high-power electric vehicles (EV) battery charging. The transmission range and efficiency of [...] Read more.
Magnetic-resonant wireless power transfer (WPT) has become a reliable contactless source of power for a wide range of applications. WPT spans different power levels ranging from low-power implantable devices up to high-power electric vehicles (EV) battery charging. The transmission range and efficiency of WPT have been reasonably enhanced by resonating the transmitter and receiver coils at a common frequency. Nevertheless, matching between resonance in the transmitter and receiver is quite cumbersome, particularly in single-transmitter multi-receiver systems. The resonance frequency in transmitter and receiver tank circuits has to be perfectly matched, otherwise power transfer capability is greatly degraded. This paper discusses the mistuning effect of parallel-compensated receivers, and thereof a novel dynamic frequency tuning method and related circuit topology and control is proposed and characterized in the system application. The proposed method is based on the concept of switch-mode gyrator emulating variable lossless inductors oriented to enable self-tunability in WPT receivers. Full article
(This article belongs to the Special Issue Wireless Power Transfer and Energy Harvesting Technologies)
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<p>A simplified model for wireless power transfer (WPT) parallel-compensated receiver with possible components’ variations modeled.</p> Full article ">Figure 2
<p>Mistuning effect on (<b>a</b>) normalized power; (<b>b</b>) receiver resonance frequency.</p> Full article ">Figure 3
<p>A model for the proposed approach: (<b>a</b>) Basic concept; (<b>b</b>) Gyrator-based inductance emulation in WPT receiver.</p> Full article ">Figure 4
<p>The gyrator (<b>a</b>) Schematic symbol; (<b>b</b>) Transformation property L-to-C and C-to-L.</p> Full article ">Figure 5
<p>Schematic diagram of dual active bridge (DAB) converter.</p> Full article ">Figure 6
<p>Validation of DAB-based Inductance synthesis by simulation (<b>a</b>) Waveforms at <span class="html-italic">φ</span> = 50°; (<b>b</b>) synthesized inductance <span class="html-italic">L<sub>φ</sub></span> and gyration conductance versus <span class="html-italic">φ</span>.</p> Full article ">Figure 7
<p>Block diagram of the dynamic frequency tuning gyrator-based parallel WPT receiver.</p> Full article ">Figure 8
<p>Block diagram of the Q-PLL control for gyrator-based dynamic frequency tuning system in <a href="#energies-11-00453-f007" class="html-fig">Figure 7</a>.</p> Full article ">Figure 9
<p>System operation waveforms: (<b>a</b>) steady-state waveforms of <span class="html-italic">V<sub>Ctrl</sub></span>, <span class="html-italic">V<sub>LPF</sub></span> and <span class="html-italic">V<sub>ac</sub></span>; (<b>b</b>) a zoom-in showing the waveforms of <span class="html-italic">Q<sub>RL</sub>·V<sub>oc</sub></span>, <span class="html-italic">V<sub>ac</sub></span>, <span class="html-italic">V<sub>ramp</sub></span>, <span class="html-italic">V<sub>Ctrl</sub></span>, and DAB gating signals PS1 and PS2.</p> Full article ">Figure 10
<p>System response to component variations showing <span class="html-italic">V<sub>ac</sub></span>, <span class="html-italic">V<sub>LPF</sub></span> and the phase-angle <span class="html-italic">φ</span>: (<b>a</b>) Case of −11% variation in <span class="html-italic">C<sub>R</sub></span> followed by −11% variation in <span class="html-italic">L<sub>R</sub></span>; (<b>b</b>) Case of +11% variation in <span class="html-italic">C<sub>R</sub></span> followed by +11% variation in <span class="html-italic">L<sub>R</sub></span>.</p> Full article ">Figure 11
<p>Waveforms of system response to irregular change in <span class="html-italic">C<sub>R</sub></span> and <span class="html-italic">L<sub>R</sub></span>.</p> Full article ">Figure 12
<p>Demonstration of system performance versus <span class="html-italic">C<sub>R</sub></span> mismatch of ±15%: (<b>a</b>) Normalized delivered power with dynamic tuning (<span class="html-italic">P<sub>o</sub> w</span>/<span class="html-italic">G</span>) and without (<span class="html-italic">P<sub>o</sub> w</span>/<span class="html-italic">o G</span>); (<b>b</b>) The synthesized inductance <span class="html-italic">L<sub>φ</sub></span>, the corresponding <span class="html-italic">φ</span> and <span class="html-italic">V<sub>ac</sub></span> versus <span class="html-italic">C<sub>R</sub></span> mismatch.</p> Full article ">Figure 13
<p>Schematic diagram for on chip implementation for gyrator-based dynamic tuning.</p> Full article ">Figure 14
<p>Real-time chip design results: (<b>a</b>) tuned point steady-state waveforms of <span class="html-italic">V<sub>ac</sub></span>, <span class="html-italic">V<sub>oc</sub></span> and <span class="html-italic">V<sub>PD</sub></span>; (<b>b</b>) the corresponding duty-cycle generated and driving signals for DAB circuit; (<b>c</b>) output power versus variations in <span class="html-italic">C<sub>R</sub></span> and <span class="html-italic">L<sub>R</sub></span> as well as the WPT receiver efficiency <span class="html-italic">η<sub>R</sub></span> based on real-time simulated chip implementation.</p> Full article ">Figure 15
<p>Receiver circuit with parasitic resistances included and the Norton equivalent of circuit.</p> Full article ">Figure 16
<p>Simplified block diagram for the closed-loop control.</p> Full article ">
26 pages, 7908 KiB  
Article
Big Data Mining of Energy Time Series for Behavioral Analytics and Energy Consumption Forecasting
by Shailendra Singh and Abdulsalam Yassine
Energies 2018, 11(2), 452; https://doi.org/10.3390/en11020452 - 20 Feb 2018
Cited by 190 | Viewed by 17228
Abstract
Responsible, efficient and environmentally aware energy consumption behavior is becoming a necessity for the reliable modern electricity grid. In this paper, we present an intelligent data mining model to analyze, forecast and visualize energy time series to uncover various temporal energy consumption patterns. [...] Read more.
Responsible, efficient and environmentally aware energy consumption behavior is becoming a necessity for the reliable modern electricity grid. In this paper, we present an intelligent data mining model to analyze, forecast and visualize energy time series to uncover various temporal energy consumption patterns. These patterns define the appliance usage in terms of association with time such as hour of the day, period of the day, weekday, week, month and season of the year as well as appliance-appliance associations in a household, which are key factors to infer and analyze the impact of consumers’ energy consumption behavior and energy forecasting trend. This is challenging since it is not trivial to determine the multiple relationships among different appliances usage from concurrent streams of data. Also, it is difficult to derive accurate relationships between interval-based events where multiple appliance usages persist for some duration. To overcome these challenges, we propose unsupervised data clustering and frequent pattern mining analysis on energy time series, and Bayesian network prediction for energy usage forecasting. We perform extensive experiments using real-world context-rich smart meter datasets. The accuracy results of identifying appliance usage patterns using the proposed model outperformed Support Vector Machine (SVM) and Multi-Layer Perceptron (MLP) at each stage while attaining a combined accuracy of 81.82%, 85.90%, 89.58% for 25%, 50% and 75% of the training data size respectively. Moreover, we achieved energy consumption forecast accuracies of 81.89% for short-term (hourly) and 75.88%, 79.23%, 74.74%, and 72.81% for the long-term; i.e., day, week, month, and season respectively. Full article
(This article belongs to the Special Issue Data Science and Big Data in Energy Forecasting)
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<p>Model: incremental progressive data mining and forecasting using energy time series.</p> Full article ">Figure 2
<p>Bayesian prediction model: eight input evidence nodes.</p> Full article ">Figure 3
<p>House 3: appliance-time associations [25% training dataset], (<b>a</b>) appliance-hour of day; (<b>b</b>) appliance-time of day; (<b>c</b>) appliance-weekday; (<b>d</b>) appliance-week; (<b>e</b>) appliance-season; (<b>f</b>) appliance-month.</p> Full article ">Figure 4
<p>Number of clusters discovered vs dataset size mined.</p> Full article ">Figure 5
<p>Overall prediction accuracy: proposed model vs. SVM vs. MLP.</p> Full article ">Figure 6
<p>House 3: energy consumption prediction vs. actual energy consumption.</p> Full article ">
14 pages, 1680 KiB  
Article
A Comprehensive Energy Analysis and Related Carbon Footprint of Dairy Farms, Part 1: Direct Energy Requirements
by Giuseppe Todde, Lelia Murgia, Maria Caria and Antonio Pazzona
Energies 2018, 11(2), 451; https://doi.org/10.3390/en11020451 - 20 Feb 2018
Cited by 22 | Viewed by 5302
Abstract
Dairy cattle farms are continuously developing more intensive systems of management which require higher utilization of durable and not-durable inputs. These inputs are responsible of significant direct and indirect fossil energy requirements which are related to remarkable emissions of CO2. This [...] Read more.
Dairy cattle farms are continuously developing more intensive systems of management which require higher utilization of durable and not-durable inputs. These inputs are responsible of significant direct and indirect fossil energy requirements which are related to remarkable emissions of CO2. This study aims to analyze direct energy requirements and the related carbon footprint of a large population of conventional dairy farms located in the south of Italy. A detailed survey of electricity, diesel and Liquefied Petroleum Gas (LPG) consumptions has been carried out among on-farm activities. The results of the analyses showed an annual average fuel consumption of 40 kg per tonne of milk, while electricity accounted for 73 kWh per tonne of milk produced. Expressing the direct energy inputs as primary energy, diesel fuel results the main resource used in on-farm activities, accounting for 72% of the total fossil primary energy requirement, while electricity represents only 27%. Moreover, larger farms were able to use more efficiently the direct energy inputs and reduce the related emissions of carbon dioxide per unit of milk produced, since the milk yield increases with the herd size. The global average farm emissions of carbon dioxide equivalent, due to all direct energy usages, accounted for 156 kg CO2-eq per tonne of Fat and Protein Corrected Milk (FPCM), while farms that raise more than 200 heads emitted 36% less than the average value. In this two-part series, the total energy demand (Part 1 + Part 2) per farm is mainly due to agricultural inputs and fuel consumption, which have the largest quota of the annual requirements for each milk yield class. These results also showed that large size farms held lower CO2-eq emissions when referred to the mass of milk produced. Full article
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<p>Distribution of electricity requirements among on-farm operations.</p> Full article ">Figure 2
<p>Distribution of diesel fuel utilization among on-farm activities.</p> Full article ">Figure 3
<p>Diesel fuel consumption per cultivated crop and unit of crop harvested.</p> Full article ">Figure 4
<p>Distribution of GHG emissions from electricity (<b>E</b>) and diesel (<b>D</b>) to on-farm operations.</p> Full article ">
13 pages, 1345 KiB  
Article
Hydrothermal Disintegration and Extraction of Different Microalgae Species
by Michael Kröger, Marco Klemm and Michael Nelles
Energies 2018, 11(2), 450; https://doi.org/10.3390/en11020450 - 20 Feb 2018
Cited by 34 | Viewed by 6630
Abstract
For the disintegration and extraction of microalgae to produce lipids and biofuels, a novel processing technology was investigated. The utilization of a hydrothermal treatment was tested on four different microalgae species (Scenedesmus rubescens, Chlorella vulgaris, Nannochloropsis oculata and Arthorspira platensis (Spirulina)) [...] Read more.
For the disintegration and extraction of microalgae to produce lipids and biofuels, a novel processing technology was investigated. The utilization of a hydrothermal treatment was tested on four different microalgae species (Scenedesmus rubescens, Chlorella vulgaris, Nannochloropsis oculata and Arthorspira platensis (Spirulina)) to determine whether it has an advantage in comparison to other disintegration methods for lipid extraction. It was shown, that hydrothermal treatment is a reasonable opportunity to utilize microalgae without drying and increase the lipid yield of an algae extraction process. For three of the four microalgae species, the extraction yield with a prior hydrothermal treatment elevated the lipid yield up to six times in comparison to direct extraction. Only Scenedesmus rubescens showed a different behaviour. Reason can be found in the different cell wall of the species. The investigation of the differences in cell wall composition of the used species indicate that the existence of algaenan as a cell wall compound plays a major role in stability. Full article
(This article belongs to the Collection Bioenergy and Biofuel)
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<p>Processing scheme for the comparison of different species.</p> Full article ">Figure 2
<p>Carbon content of direct and after HTC extracted lipids.</p> Full article ">Figure 3
<p>Hydrogen content of direct and after HTC extracted lipids.</p> Full article ">Figure 4
<p>Sulphur and Nitrogen content of extracted lipids after HTC in comparison to vegetable oils (unpublished data).</p> Full article ">Figure 5
<p>Extraction yields for the different species at different pre-treatments.</p> Full article ">Figure 6
<p>wt % of main fatty acids and percentage of recognized fraction of the different algae species.</p> Full article ">Figure 7
<p>Schematic view of cell wall structures, modified from [<a href="#B33-energies-11-00450" class="html-bibr">33</a>].</p> Full article ">
14 pages, 3002 KiB  
Article
Energy Analysis of a Rotary Drum Bioreactor for Composting Tomato Plant Residues
by Fahad N. Alkoaik, Ahmed M. Abdel-Ghany, Mohamed A. Rashwan, Ronnel B. Fulleros and Mansour N. Ibrahim
Energies 2018, 11(2), 449; https://doi.org/10.3390/en11020449 - 19 Feb 2018
Cited by 24 | Viewed by 8709
Abstract
Energy produced from plant residue composting has stimulated great interest in heat recovery and utilization. Composting is an exothermic process often controlled through temperature measurements. However, energy analysis of the overall composting system, especially the rotary bioreactors, is generally not well known and [...] Read more.
Energy produced from plant residue composting has stimulated great interest in heat recovery and utilization. Composting is an exothermic process often controlled through temperature measurements. However, energy analysis of the overall composting system, especially the rotary bioreactors, is generally not well known and very limited. This study presents detailed energy analysis in a laboratory-scale, batch-operated, rotary bioreactor used for composting tomato plant residues. The bioreactor was considered as a thermodynamic system operating under unsteady state conditions. The composting process was described, the input generated and lost energy terms as well as the relative importance of each term were quantitatively evaluated, and the composting phases were clearly identified. Results showed that the compost temperature peaked at 72 h of operation reaching 66.7 °C with a heat generation rate of 9.3 W·kg−1 of organic matter. During the composting process, the accumulated heat generation was 1.9 MJ·kg−1 of organic matter; only 4% of this heat was gained by the composting material, and 96% was lost outside the bioreactor. Contributions of thermal radiation, aeration, cylindrical, and side-walls surfaces of the reactor on the total heat loss were 1%, 2%, 69%, and 28%, respectively. The information obtained is applicable in the design, management, and control of composting operations and in improvement of bioreactor effectiveness and productivity. Full article
(This article belongs to the Collection Bioenergy and Biofuel)
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<p>Schematic diagram showing two views of the constructed rotary drum bioreactor system; dimensions in cm, not to scale.</p> Full article ">Figure 2
<p>Cross sectional views of the bioreactor showing: (<b>a</b>) the air inlet and outlet ports, the horizontal tube including the aeration ports and the thermocouples supports, and the energy terms cross the suggested control volume, and (<b>b</b>) thermocouple sensor locations in the bio-reactor.</p> Full article ">Figure 3
<p>Electrical analogy for the thermal resistances against heat losses transmitted between the compost-moist air mixture and the outside ambient air (<b>a</b>) through the cylindrical part and (<b>b</b>) through vertical circular side walls.</p> Full article ">Figure 4
<p>Convective heat transfer coefficient at the outer surface of the reactor cylinder, <span class="html-italic">h</span><sub>co</sub> (W·m<sup>−2</sup>·°C<sup>−1</sup>) estimated for the mixed convection (Equation (10)) and natural convection (Equation (11)) mechanisms.</p> Full article ">Figure 5
<p>Time course of temperatures measured for the ambient air (<span class="html-italic">T</span><sub>am</sub>), outer surface of reactor (<span class="html-italic">T</span><sub>s</sub>), inner surface of reactor (<span class="html-italic">T</span><sub>i</sub>) and compost (<span class="html-italic">T</span><sub>c</sub>) during the composting process.</p> Full article ">Figure 6
<p>Time course for the heat generation rate (W) and the cumulated heat (MJ), per kg of organic matter, during the composting process.</p> Full article ">Figure 7
<p>Energy-rate losses from inside to outside the bioreactor during the composting process via radiation, convection, and the exhausted air.</p> Full article ">Figure 8
<p>The total energy generation rate and the change of internal energy of compost during the composting process.</p> Full article ">Figure 9
<p>Composting phases were outlined based on the measured temperature of compost (<span class="html-italic">T</span><sub>c</sub>) in the present study.</p> Full article ">
15 pages, 245 KiB  
Article
The Share Price and Investment: Current Footprints for Future Oil and Gas Industry Performance
by Ionel Jianu and Iulia Jianu
Energies 2018, 11(2), 448; https://doi.org/10.3390/en11020448 - 19 Feb 2018
Cited by 16 | Viewed by 5749
Abstract
The share price has become a very important indicator for shareholders, banks, and financial institutions evaluating the performance of companies. The oil and gas industry seems to be in a difficult era of development, due to the market prices for its products. Moreover, [...] Read more.
The share price has become a very important indicator for shareholders, banks, and financial institutions evaluating the performance of companies. The oil and gas industry seems to be in a difficult era of development, due to the market prices for its products. Moreover, climate change and renewable energies are barriers for fossil energy. This state of affairs, and the fact that oil and gas shares are considered one of the most solid and reliable shares on the London Stock Exchange (LSE), have drawn our attention. International institutions encourage the investment in the oil and gas economic sector. This study investigates how investments of oil and gas companies in long-term assets influence the share price. Using the Ohlson share price model for a sample of 51 listed companies on the LSE proves that investments in long-term assets influence the share price in the case of companies which record losses. Investments in long-term assets are responsible for the attractiveness of the oil and gas company shares. Full article
13 pages, 3767 KiB  
Article
Prediction Model of Photovoltaic Module Temperature for Power Performance of Floating PVs
by Waithiru Charles Lawrence Kamuyu, Jong Rok Lim, Chang Sub Won and Hyung Keun Ahn
Energies 2018, 11(2), 447; https://doi.org/10.3390/en11020447 - 18 Feb 2018
Cited by 152 | Viewed by 10781
Abstract
Rapid reduction in the price of photovoltaic (solar PV) cells and modules has resulted in a rapid increase in solar system deployments to an annual expected capacity of 200 GW by 2020. Achieving high PV cell and module efficiency is necessary for many [...] Read more.
Rapid reduction in the price of photovoltaic (solar PV) cells and modules has resulted in a rapid increase in solar system deployments to an annual expected capacity of 200 GW by 2020. Achieving high PV cell and module efficiency is necessary for many solar manufacturers to break even. In addition, new innovative installation methods are emerging to complement the drive to lower $/W PV system price. The floating PV (FPV) solar market space has emerged as a method for utilizing the cool ambient environment of the FPV system near the water surface based on successful FPV module (FPVM) reliability studies that showed degradation rates below 0.5% p.a. with new encapsulation material. PV module temperature analysis is another critical area, governing the efficiency performance of solar cells and module. In this paper, data collected over five-minute intervals from a PV system over a year is analyzed. We use MATLAB to derived equation coefficients of predictable environmental variables to derive FPVM’s first module temperature operation models. When comparing the theoretical prediction to real field PV module operation temperature, the corresponding model errors range between 2% and 4% depending on number of equation coefficients incorporated. This study is useful in validation results of other studies that show FPV systems producing 10% more energy than other land based systems. Full article
(This article belongs to the Special Issue PV System Design and Performance)
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<p>Aerial view of the 100 kW (<b>left</b>) and 500 kW (<b>right</b>) floating systems on Hapcheon lake.</p> Full article ">Figure 2
<p>Monthly daily average energy of the month (kWh) and corresponding normalized power comparisons for FPV systems vs. 1000 kW rooftop system.</p> Full article ">Figure 3
<p>Predicted versus measured PV module temperature data (100 kW PV system).</p> Full article ">Figure 4
<p>Annual energy generated over module temperature of floating PV temperatures.</p> Full article ">Figure 5
<p>PV predicted cell/module temperature verses ambient temperature (<b>a</b>) and irradiance (<b>b</b>).</p> Full article ">Figure 6
<p>Histograms comparison of FPV Model 1 (<b>a</b>), Model 2 (<b>b</b>), Minitab data fitting model (<b>c</b>), and module field data (<b>d</b>).</p> Full article ">Figure 7
<p>3D surface plots of Model 1 efficiency/<span class="html-italic">T<sub>a</sub></span>/<span class="html-italic">G<sub>t</sub></span>.</p> Full article ">Figure 8
<p>(<b>a</b>) Time series plot of module efficiency (Model 1); (<b>b</b>) efficiency over module temperature.</p> Full article ">Figure 8 Cont.
<p>(<b>a</b>) Time series plot of module efficiency (Model 1); (<b>b</b>) efficiency over module temperature.</p> Full article ">
24 pages, 789 KiB  
Article
A General Mathematical Formulation for Winding Layout Arrangement of Electrical Machines
by Massimo Caruso, Antonino Oscar Di Tommaso, Fabrizio Marignetti, Rosario Miceli and Giuseppe Ricco Galluzzo
Energies 2018, 11(2), 446; https://doi.org/10.3390/en11020446 - 17 Feb 2018
Cited by 42 | Viewed by 12643
Abstract
Winding design methods have been a subject of research for many years of the past century. Many methods have been developed, each one characterized by some advantages and drawbacks. Nowadays, the star of slots is the most widespread design tool for electrical machine [...] Read more.
Winding design methods have been a subject of research for many years of the past century. Many methods have been developed, each one characterized by some advantages and drawbacks. Nowadays, the star of slots is the most widespread design tool for electrical machine windings. In this context, this paper presents a simple and effective procedure to determine the distribution of the slot EMFs over the phases and of the winding configuration in all possible typologies of electrical machines equipped with symmetrical windings. The result of this procedure gives a Winding Distribution Table (WDT), which can be used to define coils and coil groups connections and also to simply implement winding optimizations techniques, such as zone widening, imbrication, etc. Moreover, this procedure can be easily implemented on a computer in order to perform automated winding designs for rotating electrical machines. Several examples are provided in order to validate the proposed procedure. Full article
(This article belongs to the Section I: Energy Fundamentals and Conversion)
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<p>Plot of the EMF star for a symmetrical winding with <math display="inline"> <semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>24</mn> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <mi>m</mi> <mo>=</mo> <mn>3</mn> </mrow> </semantics> </math> and <math display="inline"> <semantics> <mrow> <mi>p</mi> <mo>=</mo> <mn>2</mn> </mrow> </semantics> </math>.</p> Full article ">Figure 2
<p>Winding schemes with different end winding layouts for a single-layer configuration with <math display="inline"> <semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>24</mn> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <mi>m</mi> <mo>=</mo> <mn>3</mn> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <mi>p</mi> <mo>=</mo> <mn>2</mn> </mrow> </semantics> </math> and <math display="inline"> <semantics> <mrow> <mi>q</mi> <mo>=</mo> <mn>2</mn> </mrow> </semantics> </math> derived from the WDT. (<b>a</b>) Without end connections; (<b>b</b>) for a split stator and three-plane end winding; (<b>c</b>) with two-plane end winding and (<b>d</b>) with three-plane end winding.</p> Full article ">Figure 3
<p>WDT for a generic normal system winding. The right side of WDT is shifted upwards by <math display="inline"> <semantics> <mi>ζ</mi> </semantics> </math> steps.</p> Full article ">Figure 4
<p>Swapping of the WDT quadrants in case of reduced systems and <math display="inline"> <semantics> <mrow> <mi>m</mi> <mo>∈</mo> <mi mathvariant="double-struck">G</mi> </mrow> </semantics> </math>.</p> Full article ">Figure 5
<p>A possible WDT for single phase windings.</p> Full article ">Figure 6
<p>(<b>a</b>) Slot EMF star for a symmetrical single-layer coil winding and (<b>b</b>) winding scheme. <math display="inline"> <semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>27</mn> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <mi>m</mi> <mo>=</mo> <mn>3</mn> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <mi>p</mi> <mo>=</mo> <mn>3</mn> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <mi>Q</mi> <mo>=</mo> <mn>1</mn> <mo>+</mo> <mfrac bevelled="true"> <mn>1</mn> <mn>2</mn> </mfrac> </mrow> </semantics> </math> and <math display="inline"> <semantics> <mrow> <mi>q</mi> <mo>=</mo> <mn>2</mn> </mrow> </semantics> </math>.</p> Full article ">Figure 7
<p>(<b>a</b>) Slot EMF star for a symmetrical single-layer bar winding and (<b>b</b>) winding scheme. <math display="inline"> <semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>27</mn> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <mi>m</mi> <mo>=</mo> <mn>3</mn> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <mi>p</mi> <mo>=</mo> <mn>3</mn> </mrow> </semantics> </math> and <math display="inline"> <semantics> <mrow> <mi>q</mi> <mo>=</mo> <mn>1</mn> <mo>+</mo> <mfrac bevelled="true"> <mn>1</mn> <mn>2</mn> </mfrac> </mrow> </semantics> </math>.</p> Full article ">Figure 8
<p>(<b>a</b>) Coil EMF star for a symmetrical double-layer coil winding and (<b>b</b>) winding scheme with series connected paths. <math display="inline"> <semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>27</mn> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <mi>m</mi> <mo>=</mo> <mn>3</mn> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <mi>p</mi> <mo>=</mo> <mn>3</mn> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <mi>q</mi> <mo>=</mo> <mn>1</mn> <mo>+</mo> <mfrac bevelled="true"> <mn>1</mn> <mn>2</mn> </mfrac> </mrow> </semantics> </math> and <math display="inline"> <semantics> <mrow> <msub> <mi>y</mi> <mi>c</mi> </msub> <mo>=</mo> <mn>4</mn> </mrow> </semantics> </math>.</p> Full article ">Figure 9
<p>(<b>a</b>) Star of slots and (<b>b</b>) winding configuration for a double-layer, three-phase concentrated winding with <math display="inline"> <semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>9</mn> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <mi>p</mi> <mo>=</mo> <mn>4</mn> </mrow> </semantics> </math> and <math display="inline"> <semantics> <mrow> <mi>m</mi> <mo>=</mo> <mn>3</mn> </mrow> </semantics> </math>.</p> Full article ">Figure 10
<p>(<b>a</b>) EMF star and (<b>b</b>) winding scheme for a symmetrical single layer non-reduced winding with <math display="inline"> <semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>36</mn> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <mi>m</mi> <mo>=</mo> <mn>6</mn> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <mi>p</mi> <mo>=</mo> <mn>5</mn> </mrow> </semantics> </math> and <math display="inline"> <semantics> <mrow> <mi>q</mi> <mo>=</mo> <mfrac bevelled="true"> <mn>3</mn> <mn>5</mn> </mfrac> </mrow> </semantics> </math>.</p> Full article ">Figure 11
<p>(<b>a</b>) EMF star and (<b>b</b>) winding scheme of a double layer reduced symmetrical configuration with <math display="inline"> <semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>36</mn> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <mi>m</mi> <mo>=</mo> <mn>6</mn> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <mi>p</mi> <mo>=</mo> <mn>5</mn> </mrow> </semantics> </math> and <math display="inline"> <semantics> <mrow> <mi>q</mi> <mo>=</mo> <mfrac bevelled="true"> <mn>3</mn> <mn>5</mn> </mfrac> </mrow> </semantics> </math>.</p> Full article ">Figure 12
<p>(<b>a</b>) Star of coils EMF and (<b>b</b>) Winding scheme for <math display="inline"> <semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>28</mn> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <mi>m</mi> <mo>=</mo> <mn>2</mn> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <mi>p</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <mi>q</mi> <mo>=</mo> <mn>7</mn> </mrow> </semantics> </math> and <math display="inline"> <semantics> <mrow> <msub> <mi>y</mi> <mi>c</mi> </msub> <mo>=</mo> <mn>12</mn> </mrow> </semantics> </math>.</p> Full article ">Figure 13
<p>Double layer seven-phase symmetrical winding with <math display="inline"> <semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>42</mn> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <mi>p</mi> <mo>=</mo> <mn>2</mn> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <mi>m</mi> <mo>=</mo> <mn>7</mn> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <msub> <mi>y</mi> <mi>c</mi> </msub> <mo>=</mo> <mn>9</mn> </mrow> </semantics> </math> and <math display="inline"> <semantics> <mrow> <mi>q</mi> <mo>=</mo> <mn>1</mn> <mo>+</mo> <mfrac bevelled="true"> <mn>1</mn> <mn>2</mn> </mfrac> </mrow> </semantics> </math>. (<b>a</b>) Star of slots and (<b>b</b>) winding scheme.</p> Full article ">Figure 14
<p>Three-phase symmetrical winding with <math display="inline"> <semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>72</mn> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <mi>p</mi> <mo>=</mo> <mn>5</mn> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <mi>m</mi> <mo>=</mo> <mn>3</mn> </mrow> </semantics> </math> and <math display="inline"> <semantics> <mrow> <mi>q</mi> <mo>=</mo> <mn>2</mn> <mo>+</mo> <mfrac bevelled="true"> <mn>2</mn> <mn>5</mn> </mfrac> </mrow> </semantics> </math>. (<b>a</b>) Star of slots and (<b>b</b>) winding scheme.</p> Full article ">Figure 15
<p>Double chording with a 3rd-order single-sided imbrication for <math display="inline"> <semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>72</mn> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <mi>p</mi> <mo>=</mo> <mn>5</mn> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <mi>m</mi> <mo>=</mo> <mn>3</mn> </mrow> </semantics> </math> and <math display="inline"> <semantics> <mrow> <mi>q</mi> <mo>=</mo> <mn>2</mn> <mo>+</mo> <mfrac bevelled="true"> <mn>2</mn> <mn>5</mn> </mfrac> </mrow> </semantics> </math>. (<b>a</b>) Star of slots and (<b>b</b>) winding scheme.</p> Full article ">Figure 16
<p>Double chording with a 3rd-order double-sided imbrication for <math display="inline"> <semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>72</mn> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <mi>p</mi> <mo>=</mo> <mn>5</mn> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <mi>m</mi> <mo>=</mo> <mn>3</mn> </mrow> </semantics> </math> and <math display="inline"> <semantics> <mrow> <mi>q</mi> <mo>=</mo> <mn>2</mn> <mo>+</mo> <mfrac bevelled="true"> <mn>2</mn> <mn>5</mn> </mfrac> </mrow> </semantics> </math>. (<b>a</b>) Star of slots and (<b>b</b>) winding scheme.</p> Full article ">Figure 17
<p>Single phase winding with double chording, 2nd order double-sided imbrication for <math display="inline"> <semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>60</mn> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <mi>p</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <mi>m</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics> </math> and <math display="inline"> <semantics> <mrow> <mi>q</mi> <mo>=</mo> <mn>30</mn> </mrow> </semantics> </math>. (<b>a</b>) Star of slots and (<b>b</b>) winding scheme.</p> Full article ">Figure 18
<p>(<b>a</b>) Star of slots of a 6-phase reduced single-layer winding with <math display="inline"> <semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>96</mn> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <mi>p</mi> <mo>=</mo> <mn>5</mn> </mrow> </semantics> </math> and (<b>b</b>) related winding scheme with 1-st order double sided imbrication.</p> Full article ">
23 pages, 4123 KiB  
Review
Ensemble-Based Data Assimilation in Reservoir Characterization: A Review
by Seungpil Jung, Kyungbook Lee, Changhyup Park and Jonggeun Choe
Energies 2018, 11(2), 445; https://doi.org/10.3390/en11020445 - 17 Feb 2018
Cited by 26 | Viewed by 6729
Abstract
This paper presents a review of ensemble-based data assimilation for strongly nonlinear problems on the characterization of heterogeneous reservoirs with different production histories. It concentrates on ensemble Kalman filter (EnKF) and ensemble smoother (ES) as representative frameworks, discusses their pros and cons, and [...] Read more.
This paper presents a review of ensemble-based data assimilation for strongly nonlinear problems on the characterization of heterogeneous reservoirs with different production histories. It concentrates on ensemble Kalman filter (EnKF) and ensemble smoother (ES) as representative frameworks, discusses their pros and cons, and investigates recent progress to overcome their drawbacks. The typical weaknesses of ensemble-based methods are non-Gaussian parameters, improper prior ensembles and finite population size. Three categorized approaches, to mitigate these limitations, are reviewed with recent accomplishments; improvement of Kalman gains, add-on of transformation functions, and independent evaluation of observed data. The data assimilation in heterogeneous reservoirs, applying the improved ensemble methods, is discussed on predicting unknown dynamic data in reservoir characterization. Full article
(This article belongs to the Section L: Energy Sources)
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<p>Examples of unreliable results after assimilating the dynamic data through ensemble-based data assimilation: (<b>a</b>) The reservoir properties of the true case follow a non-Gaussian distribution (bimodal distribution), but the assimilated result shows a Gaussian distribution; (<b>b</b>) When prior models (grey lines) contain the true performance (red line), ensemble-based methods estimate the reliable assimilation for their mean values to converge the true profile.</p> Full article ">Figure 2
<p>Comparison of sequence between (<b>a</b>) EnKF, and (<b>b</b>) ES: <b><span class="html-italic">y</span></b> and <b><span class="html-italic">d</span></b> mean a state vector and an observation vector, respectively. Superscripts <span class="html-italic">p</span> and <span class="html-italic">a</span> denote ‘prediction’ and ‘assimilation’, which processes are represented by arrow and solid line. The subscript number is the time step for the process. <b><span class="html-italic">d</span></b><sub>1</sub>:<b><span class="html-italic">d</span></b><span class="html-italic"><sub>n</sub></span> means an observation vector, which consists of observations from the 1st to the <span class="html-italic">n</span>-th time steps. An open circle means a prediction step, while a dark circle is an assimilation stage.</p> Full article ">Figure 3
<p>Classification of the methodologies to improve ensemble-based methods.</p> Full article ">Figure 4
<p>The concept of correlation function for drainage area: (<b>a</b>) Definition of drainage area for each production well; (<b>b</b>) Construction of correlation function. WOPR is well oil production rate, and WWPR stands for well water production rate. <span class="html-italic">P</span> represents ‘the production well’, and the subscripts <span class="html-italic">1</span>, <span class="html-italic">2</span>, and <span class="html-italic">n</span> mean the indication number for each production well.</p> Full article ">Figure 5
<p>The concept of multiple Kalman gains. The geological scenarios could be separated by clustering.</p> Full article ">Figure 6
<p>The workflow of ensemble-based method with DCT. (<b>a</b>) An ensemble member can be transformed to a matrix (original information); (<b>b</b>) The original information is converted to coefficients through DCT; (<b>c</b>) Only the low frequency area (upper left triangle) is used for assimilation by the ensemble-based method; (<b>d</b>) The coefficients in the low frequency area are updated; (<b>e</b>) The updated coefficients are inversely converted to the updated information. IDCT stands for inverse discrete cosine transform.</p> Full article ">Figure 7
<p>Example of selective usage of different observed data: As water breakthrough occurs, distinct separation of oil production rates from watercut is observed. Before the breakthrough, the oil production rates are the target of data assimilation; but after the water volume produced is significantly increased, the data assimilation should use watercut data, to obtain the reliability of ensemble-based methods.</p> Full article ">Figure 8
<p>Workflow of fractured reservoir characterization and performance prediction.</p> Full article ">Figure 9
<p>Typical workflow to forecast production performances integrating data clustering with ensemble-based methods at channelized reservoirs [<a href="#B65-energies-11-00445" class="html-bibr">65</a>].</p> Full article ">
14 pages, 6392 KiB  
Article
Analysis of Air-Side Economizers in Terms of Cooling-Energy Performance in a Data Center Considering Exhaust Air Recirculation
by Seonghyun Park and Janghoo Seo
Energies 2018, 11(2), 444; https://doi.org/10.3390/en11020444 - 17 Feb 2018
Cited by 13 | Viewed by 6061
Abstract
Demand is soaring for data centers with advanced data-processing capabilities. In data centers with high-temperature information technology (IT) equipment, enormous cooling systems are operated year-round. To date, studies have aimed to improve the cooling efficiency of server-room units, but cooling-energy-consumption analysis considering the [...] Read more.
Demand is soaring for data centers with advanced data-processing capabilities. In data centers with high-temperature information technology (IT) equipment, enormous cooling systems are operated year-round. To date, studies have aimed to improve the cooling efficiency of server-room units, but cooling-energy-consumption analysis considering the recirculation of exhaust air (EA) has not been researched to a sufficient degree. This study analyzed the cooling-energy saving effects considering the EA-recirculation and supply-air (SA)-temperature conditions when direct and indirect air-side economizers were applied to a data center in Korea. Thirteen case studies were conducted. The results showed that when the EA-recirculation ratio in the direct air-side economizer was 15%, its annual cooling-energy consumption increased by approximately 6.1% compared to the case with no recirculation. The indirect air-side economizer also exhibited an approximately 9% increase in cooling-energy consumption. On the other hand, when the SA temperature changed to 22 °C, the annual cooling-energy consumption of the direct and indirect air-side economizers decreased by approximately 67% and 55%, respectively, compared to a central chilled-water system. This indicates the importance of developing measures to prevent EA recirculation and of securing a wind path for the improvement of air-conditioning efficiency in data centers at the design stage. Full article
(This article belongs to the Section F: Electrical Engineering)
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<p>Monthly cooling load of server room.</p> Full article ">Figure 2
<p>Percentage of power consumption of each component.</p> Full article ">Figure 3
<p>Monthly cooling energy consumption of central chilled-water system.</p> Full article ">Figure 4
<p>Direct air-side economizer schematic.</p> Full article ">Figure 5
<p>Annual outdoor air condition and outdoor air (OA) mixing ratio. (<b>A</b>) Temperature control; (<b>B</b>) Enthalpy control.</p> Full article ">Figure 6
<p>SA condition according to the OA mixing ratio. (<b>A</b>) Temperature control; (<b>B</b>) Enthalpy control.</p> Full article ">Figure 7
<p>Indirect air-side economizer schematic.</p> Full article ">Figure 8
<p>Indirect air-side economizer operation algorithm.</p> Full article ">Figure 9
<p>Annual cooling energy consumption of each case.</p> Full article ">
21 pages, 6503 KiB  
Article
PWM Carrier Displacement in Multi-N-Phase Drives: An Additional Degree of Freedom to Reduce the DC-Link Stress
by Michela Diana, Riccardo Ruffo and Paolo Guglielmi
Energies 2018, 11(2), 443; https://doi.org/10.3390/en11020443 - 16 Feb 2018
Cited by 9 | Viewed by 5284
Abstract
The paper presents a particular Pulse Width Modulation (PWM) strategy to reduce the (Direct Current) DC-link capacitor stress for multi-n-phase drives. A multi-n-phase drive is composed of multiple independent systems of n inverter supplying a multi-n-phase electric machine. The paper focused on the [...] Read more.
The paper presents a particular Pulse Width Modulation (PWM) strategy to reduce the (Direct Current) DC-link capacitor stress for multi-n-phase drives. A multi-n-phase drive is composed of multiple independent systems of n inverter supplying a multi-n-phase electric machine. The paper focused on the investigation of the best phase shifting between carriers for a triple-3-phase drive compared to the 3-phase counterpart in order to reduce the capacitor bench design point. Simulation and experimental results show as the control technique proposed is able to reduce the value of the DC-link capacitor current in any operating condition including fault case. In this sense, the PWM carrier displacement appears like an additional degree of freedom that can be exploited in multi-n-phase drives but also in multi-motor application. Full article
(This article belongs to the Special Issue Emerging Power Electronics Technologies for Power Systems and Machine Drives)
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<p>Case <b>I</b>: base machine (<b>a</b>) and phasor diagrams (<b>b</b>). Red stator teeth are the axes of the coil supplied by the converter <b>a</b>. <math display="inline"> <semantics> <mi>φ</mi> </semantics> </math> is the V-I phase angle.</p> Full article ">Figure 2
<p>Case <b>II</b>: base machine (<b>a</b>) and phasor diagrams (<b>b</b>). Red stator teeth are supplied by the converter <b>a</b>, yellow teeth by the converter <b>b</b>; blue teeth by the converter <b>c</b>. <math display="inline"> <semantics> <mi>φ</mi> </semantics> </math> is the V-I phase angle and <math display="inline"> <semantics> <mi>α</mi> </semantics> </math> is the electric angle between coil supplied by different converter.</p> Full article ">Figure 3
<p>Case <b>III</b>: base machine (<b>a</b>) and phasor diagrams (<b>b</b>). Red teeth and red star supplied by the converter <b>a</b>, yellow teeth and yellow star supplied by the converter <b>b</b>; blue teeth and yellow star supplied by the converter <b>c</b>. <math display="inline"> <semantics> <mi>φ</mi> </semantics> </math> is the V-I phase angle and <math display="inline"> <semantics> <msub> <mi>θ</mi> <mi>S</mi> </msub> </semantics> </math> is the electric displacement between two sequential stars.</p> Full article ">Figure 4
<p>Triple-3-phase converter IGBT (Insulated Gate Bipolar Transistor) based. The converter is obtained from the composition of three 3-phase converter modules. Each 3-phase converter (<b>a</b>,<b>b</b>,<b>c</b>) supplies a 3-phase star of the machine.</p> Full article ">Figure 5
<p>Control block scheme for the triple-3-phase drive.</p> Full article ">Figure 6
<p>Phase shifting between carriers’ signals: the results of the analysis are the same regardless of the reference (<b>1</b>,<b>2</b>) if the three PWM carriers’ signals have equal mutual phase shift.</p> Full article ">Figure 7
<p>Carriers <math display="inline"> <semantics> <msub> <mi>t</mi> <mrow> <mi>r</mi> <mi>a</mi> </mrow> </msub> </semantics> </math>, <math display="inline"> <semantics> <msub> <mi>t</mi> <mrow> <mi>r</mi> <mi>b</mi> </mrow> </msub> </semantics> </math>, <math display="inline"> <semantics> <msub> <mi>t</mi> <mrow> <mi>r</mi> <mi>c</mi> </mrow> </msub> </semantics> </math> related to an angle of <math display="inline"> <semantics> <mrow> <msub> <mi>α</mi> <mi>u</mi> </msub> <mo>=</mo> <mn>45</mn> </mrow> </semantics> </math> degrees.</p> Full article ">Figure 8
<p>Simulated results: 3-phase machine <math display="inline"> <semantics> <msub> <mi>q</mi> <mrow> <mn>3</mn> <mo>/</mo> <mn>10</mn> </mrow> </msub> </semantics> </math> 3-phase powered: <math display="inline"> <semantics> <msub> <mi>I</mi> <msub> <mi>C</mi> <mrow> <mi>r</mi> <mi>m</mi> <mi>s</mi> </mrow> </msub> </msub> </semantics> </math> variation vs. <span class="html-italic">M</span> for different value of electric angle between voltage and current (VI-phase).</p> Full article ">Figure 9
<p>Simulated results triple-3-phase machine: <math display="inline"> <semantics> <msub> <mi>I</mi> <msub> <mi>C</mi> <mrow> <mi>r</mi> <mi>m</mi> <mi>s</mi> </mrow> </msub> </msub> </semantics> </math> for different displacement between PWM carriers signals (<math display="inline"> <semantics> <msub> <mi>α</mi> <mi>u</mi> </msub> </semantics> </math>) and different V-I phases. At <math display="inline"> <semantics> <mrow> <mi>M</mi> <mo>=</mo> <mn>0.35</mn> </mrow> </semantics> </math>, there is the larger variation of <math display="inline"> <semantics> <msub> <mi>I</mi> <msub> <mi>C</mi> <mrow> <mi>r</mi> <mi>m</mi> <mi>s</mi> </mrow> </msub> </msub> </semantics> </math> as function of <math display="inline"> <semantics> <msub> <mi>α</mi> <mi>u</mi> </msub> </semantics> </math>.</p> Full article ">Figure 10
<p>Simulated results: triple-3-phase phase machine: values of <math display="inline"> <semantics> <msub> <mi>I</mi> <msub> <mi>C</mi> <mrow> <mi>r</mi> <mi>m</mi> <mi>s</mi> </mrow> </msub> </msub> </semantics> </math> with the variation of M and <math display="inline"> <semantics> <msub> <mi>α</mi> <mi>u</mi> </msub> </semantics> </math>. Each surface represents a different value of VI-phase.</p> Full article ">Figure 11
<p>Simulated results: isolevel of greatest <math display="inline"> <semantics> <msub> <mi>I</mi> <msub> <mi>C</mi> <mrow> <mi>r</mi> <mi>m</mi> <mi>s</mi> </mrow> </msub> </msub> </semantics> </math> in p.u with respect to the rms value of the phase current worst case vs. <math display="inline"> <semantics> <msub> <mi>α</mi> <mi>u</mi> </msub> </semantics> </math> for all <span class="html-italic">M</span> value. The different figure represents the locus of the greatest <math display="inline"> <semantics> <msub> <mi>I</mi> <msub> <mi>C</mi> <mrow> <mi>r</mi> <mi>m</mi> <mi>s</mi> </mrow> </msub> </msub> </semantics> </math> values for a specific operating range.</p> Full article ">Figure 12
<p>Simulated results: Isolevel of greatest <math display="inline"> <semantics> <msub> <mi>I</mi> <msub> <mi>C</mi> <mrow> <mi>r</mi> <mi>m</mi> <mi>s</mi> </mrow> </msub> </msub> </semantics> </math> in p.u respect to the rms value of the phase current worst case vs. <math display="inline"> <semantics> <msub> <mi>α</mi> <mi>u</mi> </msub> </semantics> </math> for all <span class="html-italic">M</span> value. The different figure represent the locus of the greatest <math display="inline"> <semantics> <msub> <mi>I</mi> <msub> <mi>C</mi> <mrow> <mi>r</mi> <mi>m</mi> <mi>s</mi> </mrow> </msub> </msub> </semantics> </math> values for a specific operating range.</p> Full article ">Figure 13
<p>Simulated results: triple-3-phase phase machine: <math display="inline"> <semantics> <msub> <mi>I</mi> <msub> <mi>C</mi> <mrow> <mi>r</mi> <mi>m</mi> <mi>s</mi> </mrow> </msub> </msub> </semantics> </math> worst case vs. <math display="inline"> <semantics> <msub> <mi>α</mi> <mi>u</mi> </msub> </semantics> </math> for all <span class="html-italic">M</span> value. The different curves represent the maximum <math display="inline"> <semantics> <msub> <mi>I</mi> <msub> <mi>C</mi> <mrow> <mi>r</mi> <mi>m</mi> <mi>s</mi> </mrow> </msub> </msub> </semantics> </math> values for different V-I phases.</p> Full article ">Figure 14
<p>Simulated results: triple-3-phase machine: <math display="inline"> <semantics> <msub> <mi>I</mi> <msub> <mi>C</mi> <mrow> <mi>r</mi> <mi>m</mi> <mi>s</mi> </mrow> </msub> </msub> </semantics> </math> worst case vs. <math display="inline"> <semantics> <msub> <mi>α</mi> <mi>u</mi> </msub> </semantics> </math> for all M/VI-phase combinations including in fault conditions.</p> Full article ">Figure 15
<p>Experimental setup: three 3-phase motors, the control board and the power section with two triple-3-phase converter.</p> Full article ">Figure 16
<p>3-phase module: in the picture, the Intelligent Power Module (IPM) and the ceramic capacitors introduced in order to reduce the commutation path are highlighted.</p> Full article ">Figure 17
<p>Triple-3-phase converter IGBT based including parassitic inductance <math display="inline"> <semantics> <mrow> <mi>L</mi> <mi>p</mi> <mi>a</mi> <mi>r</mi> </mrow> </semantics> </math>, commutation path capacitors <math display="inline"> <semantics> <mrow> <mi>C</mi> <mi>c</mi> <mi>p</mi> </mrow> </semantics> </math>, DC-link connecting cable resistance <math display="inline"> <semantics> <mrow> <mi>R</mi> <mi>m</mi> </mrow> </semantics> </math> and inductance <math display="inline"> <semantics> <mrow> <mi>L</mi> <mi>m</mi> </mrow> </semantics> </math>.</p> Full article ">Figure 18
<p>Each picture shows <math display="inline"> <semantics> <msub> <mi>I</mi> <msub> <mi>C</mi> <mrow> <mi>r</mi> <mi>m</mi> <mi>s</mi> </mrow> </msub> </msub> </semantics> </math> values for different <math display="inline"> <semantics> <msub> <mi>α</mi> <mi>u</mi> </msub> </semantics> </math> and different couples of <span class="html-italic">M</span> and VI-phase. The blue line is the simulated <math display="inline"> <semantics> <msub> <mi>I</mi> <msub> <mi>C</mi> <mrow> <mi>r</mi> <mi>m</mi> <mi>s</mi> </mrow> </msub> </msub> </semantics> </math> for triple-3-phase converter in <a href="#energies-11-00443-f004" class="html-fig">Figure 4</a>, the green line is the simulated <math display="inline"> <semantics> <msub> <mi>I</mi> <msub> <mi>C</mi> <mrow> <mi>r</mi> <mi>m</mi> <mi>s</mi> </mrow> </msub> </msub> </semantics> </math> considering a model of the triple-3-phase converter used in the laboratory setup with estimated concentrated resonance parameters, and the red line is the measured evolution of <math display="inline"> <semantics> <msub> <mi>I</mi> <msub> <mi>C</mi> <mrow> <mi>r</mi> <mi>m</mi> <mi>s</mi> </mrow> </msub> </msub> </semantics> </math> with the laboratory setup.</p> Full article ">Figure 19
<p><math display="inline"> <semantics> <msub> <mi>I</mi> <msub> <mi>C</mi> <mrow> <mi>r</mi> <mi>m</mi> <mi>s</mi> </mrow> </msub> </msub> </semantics> </math> worst case for <math display="inline"> <semantics> <msub> <mi>α</mi> <mi>u</mi> </msub> </semantics> </math> variation for considered couples of M and V-I phases values. The blue line is the simulated <math display="inline"> <semantics> <msub> <mi>I</mi> <msub> <mi>C</mi> <mrow> <mi>r</mi> <mi>m</mi> <mi>s</mi> </mrow> </msub> </msub> </semantics> </math> for triple-3-phase converter in <a href="#energies-11-00443-f004" class="html-fig">Figure 4</a>, the green line is the simulated <math display="inline"> <semantics> <msub> <mi>I</mi> <msub> <mi>C</mi> <mrow> <mi>r</mi> <mi>m</mi> <mi>s</mi> </mrow> </msub> </msub> </semantics> </math> considering a model of the triple-3-phase converter used in the laboratory setup with estimated concentrated resonance parameters, and the red line is the measured evolution of <math display="inline"> <semantics> <msub> <mi>I</mi> <msub> <mi>C</mi> <mrow> <mi>r</mi> <mi>m</mi> <mi>s</mi> </mrow> </msub> </msub> </semantics> </math> with the laboratory setup.</p> Full article ">Figure 20
<p>Experimental setup: triple 3-phase motors, the control board and the power section with one triple-3-phase converter.</p> Full article ">Figure 21
<p>Measurement of the current (blue) and the corresponding modulation index (red) for a single phase. In yellow and green, the corresponding first harmonic of the waves is reported.</p> Full article ">Figure 22
<p>Each picture shows <math display="inline"> <semantics> <msub> <mi>I</mi> <msub> <mi>C</mi> <mrow> <mi>r</mi> <mi>m</mi> <mi>s</mi> </mrow> </msub> </msub> </semantics> </math> values for different <math display="inline"> <semantics> <msub> <mi>α</mi> <mi>u</mi> </msub> </semantics> </math> and different couples of <span class="html-italic">M</span> and VI-phase. The blue line is the simulated <math display="inline"> <semantics> <msub> <mi>I</mi> <msub> <mi>C</mi> <mrow> <mi>r</mi> <mi>m</mi> <mi>s</mi> </mrow> </msub> </msub> </semantics> </math> for triple-3-phase converter in <a href="#energies-11-00443-f004" class="html-fig">Figure 4</a>, the green line is the simulated <math display="inline"> <semantics> <msub> <mi>I</mi> <msub> <mi>C</mi> <mrow> <mi>r</mi> <mi>m</mi> <mi>s</mi> </mrow> </msub> </msub> </semantics> </math> considering a model of the triple-3-phase converter used in the laboratory setup with estimated concentrated resonance parameters, and the red line is the measured evolution of <math display="inline"> <semantics> <msub> <mi>I</mi> <msub> <mi>C</mi> <mrow> <mi>r</mi> <mi>m</mi> <mi>s</mi> </mrow> </msub> </msub> </semantics> </math> with the laboratory setup.</p> Full article ">
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