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Energies, Volume 11, Issue 3 (March 2018) – 217 articles

Cover Story (view full-size image): Microalgae have long been utilized as food and feed, and, more recently, fuel. When microalgae was adapted for fuel production, it was necessary to ensure that the energy required to do this was not more than what was produced. In this article, the Energy Profit Ratio (EPR) was used as the parameter for these calculations. The EPR is the ratio between energy produced and energy consumed with the aim to work out whether the energy required is lower, equal to, or higher than the energy produced. This was a case study in a pilot project located in Minamisoma City in the Fukushima Prefecture of Japan. View this paper
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1 pages, 157 KiB  
Correction
Correction: Arshad A.; et al. An Analysis of Photo-Voltaic Hosting Capacity in Finnish Low Voltage Distribution Networks. Energies 2017, 10, 1702
by Ammar Arshad, Martin Lindner and Matti Lehtonen
Energies 2018, 11(3), 689; https://doi.org/10.3390/en11030689 - 20 Mar 2018
Viewed by 3363
Abstract
The authors would like to correct following errors[...] Full article
(This article belongs to the Section F: Electrical Engineering)
11 pages, 3781 KiB  
Article
A Fast-Transient Output Capacitor-Less Low-Dropout Regulator Using Active-Feedback and Current-Reuse Feedforward Compensation
by Eun-Taek Sung, Sangyong Park and Donghyun Baek
Energies 2018, 11(3), 688; https://doi.org/10.3390/en11030688 - 19 Mar 2018
Cited by 10 | Viewed by 9065
Abstract
In this paper, output capacitor-less low-dropout (LDO) regulator using active-feedback and current-reuse feedforward compensation (AFCFC) is presented. The open-loop transfer function was obtained using small-signal modeling. The stability of the proposed LDO was analyzed using pole-zero plots, and it was confirmed by simulations [...] Read more.
In this paper, output capacitor-less low-dropout (LDO) regulator using active-feedback and current-reuse feedforward compensation (AFCFC) is presented. The open-loop transfer function was obtained using small-signal modeling. The stability of the proposed LDO was analyzed using pole-zero plots, and it was confirmed by simulations that the stability was ensured under the load current of 50 mA. The proposed compensation method increases gain-bandwidth product (GBW) and reduces the on-chip compensation capacitor. The proposed AFCFC technique was applied to a three-stage output capacitor-less LDO. The LDO has a GBW of 5.6 MHz with a small on-chip capacitor of 2.6 pF. Fast-transient time of 450 ns with low quiescent current of 65.8 μA was achieved. The LDO was fabricated in 130 nm CMOS process consuming 180 × 140 μm2 of the silicon area. Full article
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<p>Power management integrated circuit (PMIC) with output capacitor-less low dropout regulator (OCL-LDO) scheme.</p>
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<p>Output capacitor-less LDO (OCL-LDO) regulator with the compensation network.</p>
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<p>Block diagram of conventional frequency compensation methods. (<b>a</b>) NMC [<a href="#B17-energies-11-00688" class="html-bibr">17</a>,<a href="#B18-energies-11-00688" class="html-bibr">18</a>,<a href="#B19-energies-11-00688" class="html-bibr">19</a>]; (<b>b</b>) reverse NMC [<a href="#B20-energies-11-00688" class="html-bibr">20</a>,<a href="#B21-energies-11-00688" class="html-bibr">21</a>]; and (<b>c</b>) reverse NMC with current buffers [<a href="#B22-energies-11-00688" class="html-bibr">22</a>].</p>
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<p>Three stage amplifier with the proposed AFCFC.</p>
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<p>Small-signal equivalent circuit of the proposed AFCFC.</p>
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<p>Pole-zero locations of the LDOs. (<b>a</b>) Conventional LDO with NMC [<a href="#B17-energies-11-00688" class="html-bibr">17</a>]; (<b>b</b>) active-feedback compensation LDO without current-reuse feedforward paths; and (<b>c</b>) with current-reuse feedforward paths.</p>
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<p>Simulated open-loop response of LDO at different load conditions with <span class="html-italic">C<sub>L</sub></span> = 100 pF.</p>
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<p>Schematic of three-stage OCL-LDO regulator employing the proposed AFCFC.</p>
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<p>Simulated output voltage of load transient response with <span class="html-italic">C<sub>L</sub></span> = 100 pF.</p>
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<p>Chip photograph.</p>
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<p>Measured load transient responses with <span class="html-italic">C<sub>L</sub></span> = 100 pF and <span class="html-italic">I<sub>out</sub></span> = 0 to 50 mA. (<b>a</b>) <span class="html-italic">V<sub>in</sub></span> = 1.4 V and <span class="html-italic">V<sub>out</sub></span> = 1.2 V; (<b>b</b>) <span class="html-italic">V<sub>in</sub></span> = 2.7 V and <span class="html-italic">V<sub>out</sub></span> = 2.5 V.</p>
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20 pages, 2509 KiB  
Article
The Energy Cost Analysis of Hybrid Systems and Diesel Generators in Powering Selected Base Transceiver Station Locations in Nigeria
by Peter Ozaveshe Oviroh and Tien-Chien Jen
Energies 2018, 11(3), 687; https://doi.org/10.3390/en11030687 - 19 Mar 2018
Cited by 73 | Viewed by 8719
Abstract
As more locations gain access to telecommunication, there is a growing demand to provide energy in a reliable, efficient and environmentally friendly manner while effectively addressing growing energy needs. Erratic power supply and rising operation costs (OPEX) in Nigeria have increased the need [...] Read more.
As more locations gain access to telecommunication, there is a growing demand to provide energy in a reliable, efficient and environmentally friendly manner while effectively addressing growing energy needs. Erratic power supply and rising operation costs (OPEX) in Nigeria have increased the need to harness local renewable energy sources. Thus, identifying the right generator schedule with the renewable system to reduce OPEX is a priority for operators and vendors. This study evaluates the energy costs of hybrid systems with different generator schedules in powering base transceiver stations in Nigeria using the Hybrid Optimization Model for Electric Renewable (HOMER). A load range of 4 kW to 8 kW was considered using: (i) an optimised generator schedule; (ii) forced-on generator schedule and (iii) the generator-only schedule. The results showed an optimal LCOE range between averages of USD 0.156/kWh to 0.172/kWh for the 8 kW load. The percent energy contribution by generator ranges from 52.80% to 60.90%, and by the solar PV system, 39.10% to 47.20%. Excess energy ranges from 0.03% to 14.98%. The optimised generator schedule has the highest solar PV penetration of 56.8%. The OPEX savings on fuel ranges from 41.68% to 47% for the different load schedules and carbon emission savings of 4222 kg to 31,428.36 kg. The simulation results shows that powering base stations using the optimised hybrid system schedule would be a better option for the telecom industry. Full article
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<p>Yearly solar resource.</p>
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<p>Schematic diagram of a solar hybrid system.</p>
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<p>Schematic diagram of the simulated system.</p>
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<p>The cost of off-grid electricity generation, based on lcoe: Adapted [<a href="#B14-energies-11-00687" class="html-bibr">14</a>].</p>
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<p>Levelised cost of energy (<span>$</span>/kWh).</p>
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<p>Energy generated comparison of optimised and forced-on (kWh/year).</p>
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<p>PV penetration (%).</p>
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<p>Fuel energy production (kWh/year).</p>
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<p>Total carbon emission for the location (kg/year).</p>
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21 pages, 3958 KiB  
Article
Research on Economic Comprehensive Control Strategies of Tractor-Planter Combinations in Planting, Including Gear-Shift and Cruise Control
by Baogang Li, Dongye Sun, Minghui Hu and Junlong Liu
Energies 2018, 11(3), 686; https://doi.org/10.3390/en11030686 - 19 Mar 2018
Cited by 22 | Viewed by 5061
Abstract
An automatic control strategy for forward speed in the planting process is proposed to improve the fuel economy and reduce the labor intensity of drivers. Models of tractors with power-shift transmission (PST) and a precise pneumatic planter with an electric-driven seed metering device [...] Read more.
An automatic control strategy for forward speed in the planting process is proposed to improve the fuel economy and reduce the labor intensity of drivers. Models of tractors with power-shift transmission (PST) and a precise pneumatic planter with an electric-driven seed metering device are built as research objects and simulated using Matlab with Simulink. The economic comprehensive control strategies for forward speed, including gear-shift schedule and cruise control strategy, are developed. Four levels control mode with different fuel economy performances are implemented to meet different driver or operation condition requirements. In addition, the control strategy is developed for the seed-metering device motor to maintain the required seed spacing in planting. Finally, the fuel economy and effectiveness of the control strategies for forward speed and planting quality are verified by simulations with Matlab/Simulink and Matlab/Stateflow. The simulation results verify the satisfactory performance of the proposed control strategies. The error of seed spacing is less than 3% when planting with speed fluctuation. Under the premise of ensuring planting quality and driver’s demands, the cruise control strategies for forward speed have more significant effects on the fuel economy than previous cruise control strategies. Furthermore, the control mode with higher level has better fuel economy and a larger speed deviation range. Full article
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<p>Tractor-planter combination in planting operation.</p>
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<p>Schematic of external forces acting on tractor-planter combination unit.</p>
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<p>Powertrain diagram of tractor-planter combination operating unit.</p>
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<p>Travelling speed range versus speed ratios of overall drivetrain of different gears.</p>
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<p>Engine characteristics maps. (<b>a</b>) Engine torque; (<b>b</b>) Brake specific fuel consumption.</p>
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<p>The configuration of power system for centrifugal fan of pneumatic planter.</p>
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<p>Schematic of seed metering device.</p>
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<p>(<b>a</b>) Engine universal performance; (<b>b</b>) Tractor driving torque.</p>
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<p>The up gear-shift maps. (<b>a</b>) Mode of level 1; (<b>b</b>) Modes of level 2–4.</p>
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<p>The static gear-shift map for control mode of level 1.</p>
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<p>Control flow of cruise control.</p>
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<p>Control structure of the seed metering device.</p>
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<p>Simulation results of fixed load. (<b>a</b>) Speed variations; (<b>b</b>) Variations of brake specific fuel consumption and transmission gear.</p>
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<p>Simulation results of constant reference target speed.</p>
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<p>Travelling cycles for simulation.</p>
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<p>Simulation results for seed spacing.</p>
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17 pages, 7015 KiB  
Article
Hydrodynamic Performance of an Array of Wave Energy Converters Integrated with a Pontoon-Type Breakwater
by De Zhi Ning, Xuan Lie Zhao, Li Fen Chen and Ming Zhao
Energies 2018, 11(3), 685; https://doi.org/10.3390/en11030685 - 18 Mar 2018
Cited by 41 | Viewed by 5444
Abstract
The cost of wave energy converters (WECs) can be reduced significantly by integrating WECs into other marine facilities, especially in sea areas with a mild wave climate. To reduce the cost and increase the efficiency, a hybrid WEC system, comprising a linear array [...] Read more.
The cost of wave energy converters (WECs) can be reduced significantly by integrating WECs into other marine facilities, especially in sea areas with a mild wave climate. To reduce the cost and increase the efficiency, a hybrid WEC system, comprising a linear array (medium farm) of oscillating buoy-type WECs attached to the weather side of a fixed-type floating pontoon as the base structure is proposed. The performance of the WEC array is investigated numerically using a boundary element method (BEM) based on the linear potential flow theory. The linear power take-off (PTO) damping model is used to calculate the output power of the WEC array. The performance of the WEC array and each individual WEC device is balanced by using the mean interaction factor and the individual interaction factor. To quantify the effect of the pontoon, the hydrodynamic results of the WEC arrays with and without a pontoon are compared with each other. Detailed investigations on the influence of the structural and PTO parameters are performed in a wide wave frequency range. Results show that the energy conversion efficiency of a WEC array with a properly designed pontoon is much higher than that without a pontoon. This integration scheme can achieve the efficiency improvement and construction-cost reduction of the wave energy converters. Full article
(This article belongs to the Special Issue Wave Energy Potential, Behavior and Extraction)
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<p>The sketch of the proposed WEC-pontoon system: (<b>a</b>) the top view; and (<b>b</b>) the side view (the WECs in the array are labelled as devices #1–#11, respectively, on the top view).</p>
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<p>3D mesh of the calculation model for the five hemispherical WEC devices in the validation test.</p>
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<p>Comparison of the calculated mean interaction factor <span class="html-italic">q</span><sub>mean</sub> with the numerical results in [<a href="#B39-energies-11-00685" class="html-bibr">39</a>].</p>
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<p>3D mesh of the calculation model for the parametric study. (<span class="html-italic">T</span>/<span class="html-italic">h</span> = 0.25, <span class="html-italic">D</span>/<span class="html-italic">h</span> = 12, <span class="html-italic">B</span>/<span class="html-italic">h</span> = 0.6, <span class="html-italic">s</span><sub>1</sub>/<span class="html-italic">h</span> = 0.5, <span class="html-italic">s</span><sub>2</sub>/<span class="html-italic">h</span> = 0.2)</p>
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<p>Variations of mean interaction factor <span class="html-italic">q</span><sub>mean</sub> with the dimensionless wave number <span class="html-italic">kh</span> for different WEC-pontoon spacing <span class="html-italic">s</span><sub>2.</sub></p>
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<p>Variations of dimensionless total extracted power of the WEC array (<span class="html-italic">P</span><sub>total,d</sub>) and the dimensionless extracted power of the isolated WEC (<span class="html-italic">P</span><sub>isolated,d</sub>) with the dimensionless wave number <span class="html-italic">kh.</span></p>
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<p>Variations of dimensionless heave added mass (<b>a</b>), dimensionless heave damping coefficient (<b>b</b>), and dimensionless heave wave exciting force (<b>c</b>) of individual WECs with and without the pontoon. (<span class="html-italic">s</span><sub>2</sub>/<span class="html-italic">h</span> = 0.2, <span class="html-italic">β</span> = 0°).</p>
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<p>Variations of <span class="html-italic">q</span><sub>mean</sub> with <span class="html-italic">kh</span> for WEC arrays with different WEC-WEC spacing of <span class="html-italic">s</span><sub>1</sub>/<span class="html-italic">h</span> = 0.3, 0.5, 0.7, and 0.9. The WEC-pontoon spacing is <span class="html-italic">s</span><sub>2</sub>/<span class="html-italic">h</span> = 0.2.</p>
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<p>Variations of <span class="html-italic">q</span><sub>mean</sub> with <span class="html-italic">kh</span> for WEC array with (configuration A) and without (configuration B) pontoon with different wave incident angles.</p>
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<p>Individual interaction factors of all WEC devices for the WEC-pontoon system with <span class="html-italic">β</span> = 0° (<b>a</b>), 30° (<b>b</b>), 60° (<b>c</b>), and 90° (<b>d</b>).</p>
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<p>Dimensionless heave wave exciting forces on each WEC device for <span class="html-italic">β</span> = 30° (<b>a</b>), 60° (<b>b</b>), and 90° (<b>c</b>).</p>
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<p>Variations of <span class="html-italic">q</span><sub>mean</sub> vs. <span class="html-italic">kh</span> for the WEC array with different drafts <span class="html-italic">T</span> of the rear pontoon.</p>
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<p>Variations of <span class="html-italic">q</span><sub>mean</sub> vs. <span class="html-italic">kh</span> for the WEC array with different breadths <span class="html-italic">B</span> of the rear pontoon.</p>
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<p>Variations of <span class="html-italic">q</span><sub>mean</sub> vs. <span class="html-italic">kh</span> for the WEC array with different lengths <span class="html-italic">D</span> of the rear pontoon.</p>
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<p>Variations of <span class="html-italic">q</span><sub>mean</sub> vs. <span class="html-italic">kh</span> for the WEC array with various PTO damping.</p>
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9 pages, 2974 KiB  
Article
Assessment of the Induced Electric Fields in a Carbon-Fiber Electrical Vehicle Equipped with a Wireless Power Transfer System
by Valerio De Santis, Tommaso Campi, Silvano Cruciani, Ilkka Laakso and Mauro Feliziani
Energies 2018, 11(3), 684; https://doi.org/10.3390/en11030684 - 18 Mar 2018
Cited by 37 | Viewed by 5522
Abstract
In this study, the electric field induced inside two realistic anatomical models placed near or inside an electric vehicle made of carbon-fiber composite while charging its battery with a wireless power transfer (WPT) system has been investigated. The WPT source consists of two [...] Read more.
In this study, the electric field induced inside two realistic anatomical models placed near or inside an electric vehicle made of carbon-fiber composite while charging its battery with a wireless power transfer (WPT) system has been investigated. The WPT source consists of two parallel inductive coils operating with a power output of 7.7 kW at two different frequencies of 85 and 150 kHz. Since a misalignment between the primary and the secondary coil creates higher induced fields, a misalignment of 20 cm is also considered as the worst-case exposure condition. The analysis of the obtained results shows that the International Commission on Non-Ionizing Radiation Protection (ICNIRP) basic restrictions are exceeded by 1.3 dB and 4.8 dB for the aligned and misaligned coil positions, respectively. This exceedance is however confined only in a small area of the driver’s foot. Full article
(This article belongs to the Section F: Electrical Engineering)
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<p>WPT coil configuration (primary coil on the road and secondary coil below the car platform) (<b>a</b>). WPT equivalent circuit (<b>b</b>).</p>
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<p>Configuration of Duke and Fats placed near and inside an EV chassis. (<b>a</b>) Aligned and (<b>b</b>) misaligned coils. The red box is the computational domain for dosimetry assessment.</p>
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<p>Magnetic flux density distribution for the misaligned coil position. (<b>a</b>) Steel chassis; (<b>b</b>) aluminum chassis. The yellow line represents the ICNIRP limit (RL = 27 µT = 28.63 dBµT).</p>
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<p>Magnetic flux density distribution for the aligned (<b>a</b>) and misaligned (<b>b</b>) coil positions in the case of CF composite chassis. The yellow line represents the ICNIRP limit (RL = 27 µT = 28.63 dBµT).</p>
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<p>Induced electric field inside Duke and Fats for the aligned coil position. E-field normalized to the peak value of 13.3 V/m. BR = 11.48 V/m ≈ −1.3 dBV/m (green area is the portion where the BR is exceeded).</p>
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<p>Induced electric field inside Duke and Fats for the misaligned coil position. E-field normalized to the peak value of 19.9 V/m. BR = 11.48 V/m ≈ −4.8 dBV/m (green area is the portion where the BR is exceeded).</p>
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19 pages, 3094 KiB  
Article
Big Data Analytics for Discovering Electricity Consumption Patterns in Smart Cities
by Rubén Pérez-Chacón, José M. Luna-Romera, Alicia Troncoso, Francisco Martínez-Álvarez and José C. Riquelme
Energies 2018, 11(3), 683; https://doi.org/10.3390/en11030683 - 18 Mar 2018
Cited by 83 | Viewed by 10821
Abstract
New technologies such as sensor networks have been incorporated into the management of buildings for organizations and cities. Sensor networks have led to an exponential increase in the volume of data available in recent years, which can be used to extract consumption patterns [...] Read more.
New technologies such as sensor networks have been incorporated into the management of buildings for organizations and cities. Sensor networks have led to an exponential increase in the volume of data available in recent years, which can be used to extract consumption patterns for the purposes of energy and monetary savings. For this reason, new approaches and strategies are needed to analyze information in big data environments. This paper proposes a methodology to extract electric energy consumption patterns in big data time series, so that very valuable conclusions can be made for managers and governments. The methodology is based on the study of four clustering validity indices in their parallelized versions along with the application of a clustering technique. In particular, this work uses a voting system to choose an optimal number of clusters from the results of the indices, as well as the application of the distributed version of the k-means algorithm included in Apache Spark’s Machine Learning Library. The results, using electricity consumption for the years 2011–2017 for eight buildings of a public university, are presented and discussed. In addition, the performance of the proposed methodology is evaluated using synthetic big data, which cab represent thousands of buildings in a smart city. Finally, policies derived from the patterns discovered are proposed to optimize energy usage across the university campus. Full article
(This article belongs to the Special Issue Data Science and Big Data in Energy Forecasting)
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<p>Proposed methodology. RDD: Resilient Distributed Dataset; MLlib: Machine Learning Library; WSSSE: Within Set Sum of Square Errors.</p>
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<p>One concurrent execution of the k-means algorithm.</p>
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<p>BD-Silhouette, BD-Dunn, Davies-Bouldin, and WSSSE clustering validity indices for <span class="html-italic">k</span> values from 2 to 15.</p>
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<p>Centroids of the electricity consumption clusters.</p>
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<p>Cluster analysis depending on buildings, seasons of the year and days of the week.</p>
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<p>Centroids of the electricity consumption clusters.</p>
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<p>Centroids of the clusters with lower consumptions.</p>
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<p>Cluster analysis depending on buildings, seasons of the year, and days of the week.</p>
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<p>A runtime comparison between the two different hardware configurations.</p>
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24 pages, 10793 KiB  
Article
Experimental and Numerical Analyses of the Sloshing in a Fuel Tank
by Emma Frosina, Adolfo Senatore, Assunta Andreozzi, Francesco Fortunato and Pino Giliberti
Energies 2018, 11(3), 682; https://doi.org/10.3390/en11030682 - 17 Mar 2018
Cited by 19 | Viewed by 8150
Abstract
The sloshing of fuel inside the tank is an important issue in aerospace and automotive applications. This phenomenon, in fact, can cause various issues related to vehicle stability and safety, to component fatigue, audible noise, vibrations and to the level measurement of the [...] Read more.
The sloshing of fuel inside the tank is an important issue in aerospace and automotive applications. This phenomenon, in fact, can cause various issues related to vehicle stability and safety, to component fatigue, audible noise, vibrations and to the level measurement of the fuel itself. The sloshing phenomenon can be defined as a highly nonlinear oscillatory movement of the free-surface of liquid inside a container, such as a fuel tank, under the effect of continuous or instantaneous forces. This paper is the result of a research collaboration between the Industrial Engineering Department of the University of Naples “Federico II” and the R&D department of Fiat Chrysler Automobiles (F.C.A.) The activity is focused on the study of the sloshing in the fuel tank of vehicles. The goal is the optimization of the tank geometry in order to allow, for example, the correct fuel suction under all driving conditions and to prevent undesired noise and vibrations. This paper shows results obtained on a reference tank filled by water tinted with a dark blue food colorant. The geometry has been tested on a test bench designed by Moog Inc. on specification from Fiat Chrysler Automobiles with harmonic excitation of a 2D tank slice along one degree of freedom. The test bench consists of a hexapod with six independent actuators connecting the base to the top platform, allowing all six Degrees of Freedom (DOFs). On the top platform there are other two additional actuators to extend pitch and roll envelope, thus the name of “8-DOF bench”. The designed tank has been studied with a three-dimensional Computational Fluid Dynamics (CFD) modeling approach, too. By the end, the numerical and experimental data have been compared with a post-processing analysis by means of Matlab® software. For this reason, the images have been reduced in two dimensions. In particular, the percentage gaps of the free surfaces and the center of gravity have been compared each other. The comparison, for the three different levels of liquid tested, has shown a good agreement with a discrepancy always less than 3%. Full article
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<p>The Fuel Tank Test System and employed Cartesian coordinate system: (<b>a</b>) Drawing of the Moog test rig; (<b>b</b>) Real Moog test rig installed in FCA | Fiat Chrysler Automobiles; (<b>c</b>) Tank under investigation.</p>
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<p>Three tested water free surfaces.</p>
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<p>Image post-processing: (<b>a</b>) initial picture, (<b>b</b>) first elaboration, (<b>c</b>) free surface of the flow.</p>
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<p>Experimental center of gravity position during the test, (<b>a</b>) Time interval [7 ÷ 12] s, (<b>b</b>) Zoom around the center of gravity of the tank.</p>
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<p>General form of the conservation law.</p>
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<p>(<b>a</b>) Real fuel tank, (<b>b</b>) Extracted fluid volume.</p>
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<p>Free surface for the liquid level of 101 mm.</p>
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<p>Mesh of the fluid volume.</p>
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<p>Mesh sensitivity analysis: employed grids.</p>
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<p>Comparison between numerical and experimental data in the time interval [7 ÷ 12] s for H = 65 mm and frequency of 0.5 Hz and 0.7 Hz.</p>
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<p>Comparison between numerical and experimental data in the time interval [7 ÷ 12] s: horizontal displacements and vertical displacements.</p>
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<p>2D Comparison of the free surfaces—numerical vs. experimental data.</p>
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<p>Comparison between numerical and experimental data in the time interval [7 ÷ 12] s for H = 101 mm and frequency values of 0.5 Hz and 0.7 Hz.</p>
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<p>Comparison between numerical and experimental data in the time interval [7 ÷ 12] s: horizontal displacements and vertical displacements.</p>
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<p>2D Comparison of the free surfaces—numerical vs. experimental data.</p>
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<p>Comparison between numerical and experimental data in the time interval [7 ÷ 12] s for H = 165 mm and frequency values of 0.5 Hz and 0.7 Hz.</p>
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<p>Comparison between numerical and experimental data in the time interval [7 ÷ 12] s: horizontal displacements and vertical displacements.</p>
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<p>2D Comparison of the free surfaces—numerical vs. experimental data.</p>
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21 pages, 6637 KiB  
Article
Algorithm for Fast and Efficient Detection and Reaction to Angle Instability Conditions Using Phasor Measurement Unit Data
by Igor Ivanković, Igor Kuzle and Ninoslav Holjevac
Energies 2018, 11(3), 681; https://doi.org/10.3390/en11030681 - 17 Mar 2018
Cited by 12 | Viewed by 4796
Abstract
In wide area monitoring, protection, and control (WAMPAC) systems, angle stability of transmission network is monitored using data from phasor measurement units (PMU) placed on transmission lines. Based on this PMU data stream advanced algorithm for out-of-step condition detection and early warning issuing [...] Read more.
In wide area monitoring, protection, and control (WAMPAC) systems, angle stability of transmission network is monitored using data from phasor measurement units (PMU) placed on transmission lines. Based on this PMU data stream advanced algorithm for out-of-step condition detection and early warning issuing is developed. The algorithm based on theoretical background described in this paper is backed up by the data and results from corresponding simulations done in Matlab environment. Presented results aim to provide the insights of the potential benefits, such as fast and efficient detection and reaction to angle instability, this algorithm can have on the improvement of the power system protection. Accordingly, suggestion is given how the developed algorithm can be implemented in protection segments of the WAMPAC systems in the transmission system operator control centers. Full article
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<p>Principal block schemes of a wide area monitoring, protection, and control (WAMPAC) systems based on traditional transmission line relay measurement and protection system.</p>
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<p>Flow chart design for issuing tripping command for out-of-step protection in WAMPAC system to circuit breaker in transmission network substations.</p>
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<p>Scheme for CRO6BUS model in Matlab simulation environment.</p>
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<p>Details for starting point of active power oscillations on 400 kV transmission line during out-of-step condition during simulation scenario: (<b>a</b>) Voltage oscillations; and, (<b>b</b>) Current oscillations.</p>
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<p>Active and reactive power oscillations on 400 kV transmission line during out-of-step condition in simulations that last 10 s without protection action.</p>
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<p>Trajectory path for circuit breaker switching operations or short circuit faults and some trajectory path for possible active power oscillations on a single transmission line.</p>
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<p>Values for line currents and line differential currents on two adjacent transmission lines: (<b>a</b>) Short circuit fault on Tumbri-Melina line with currents from both side of line with differential current Δ<span class="html-italic">I</span>; (<b>b</b>) Tumbri-Zerjavinec line with fault current and differential current Δ<span class="html-italic">I</span>.</p>
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<p>Simplified two machine equivalent on a transmission network line equipped with PMU devices which send phasor data packages to WAMPAC system.</p>
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<p>Flow chart of the algorithm for active power oscillations and out-of-step condition detection and early warning issuing.</p>
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<p>Voltage angle difference Δ<span class="html-italic">φ</span>, for two 400 kV transmission lines during out-of-step condition occurrence in the network.</p>
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<p>Out of step condition and power swing condition on 400 kV lines: (<b>a</b>) Angle speed <span class="html-italic">ω</span>, for two 400 kV transmission lines during out-of-step conditions occurrence; and, (<b>b</b>) Angle acceleration <span class="html-italic">α</span>, for two 400 kV transmission lines during out of step condition occurrence.</p>
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<p>Details for power swing condition and out-of-step conditions on 400 kV line: (<b>a</b>) Details of the voltage angle difference Δ<span class="html-italic">φ</span>, angle speed <span class="html-italic">ω</span> and angle acceleration <span class="html-italic">α</span>, on 400 kV transmission line with stable swing condition; (<b>b</b>) Details of the voltage angle difference Δ<span class="html-italic">φ</span>, angle speed <span class="html-italic">ω</span> and angle acceleration <span class="html-italic">α</span>, on 400 kV transmission line with out-of-step conditions.</p>
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<p>Remedial protection criteria: (<b>a</b>) Konjsko-Velebit and Melina-Velebit currents from both line ends with differential current protection (Δ<span class="html-italic">I</span>). Out-of-step has developed on Konjsko-Velebit line and only stable power swing was present on Melina-Velebit line; and, (<b>b</b>) Konjsko-Velebit and Melina-Velebit lines with equivalent transmission system inertia measurements. Out-of-step developed on Konjsko-Melina line and only stable swing was present on Melina-Velebit line.</p>
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<p>Active power oscillations on two 400 kV lines with voltage angle difference Δ<span class="html-italic">φ</span> and protection operations: (<b>a</b>) On line Konjsko-Velebit there is an out-of-step (OOS) conditions and on line Velebit-Melina there is a power swing condition; and, (<b>b</b>) Detail presenting protection activations when protection setting is reached and tripped the Konjsko-Velebit line while Velebit-Melina remained in operation.</p>
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<p>Active power oscillations on two 400 kV lines with angle speed <span class="html-italic">ω</span>: (<b>a</b>) On line Konjsko-Velebit there is an out-of-step (OOS) condition and on line Velebit-Melina there is a power swing condition; and, (<b>b</b>) Detail presenting angle speed <span class="html-italic">ω</span> protection operation and tripping of the Konjsko-Velebit line while linr Velebit-Melina remained in operation.</p>
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<p>Active power oscillations on two 400 kV lines with angle acceleration <span class="html-italic">α</span>: (<b>a</b>) On line Konjsko-Velebit there is an out-of-step (OOS) condition and on line Velebit-Melina there is a power swing condition; and, (<b>b</b>) Detail presenting angle speed <span class="html-italic">ω</span> protection operation and tripping of the the Konjsko-Velebit line while Velebit-Melina remained in operation.</p>
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<p>Remedial line protection criteria: (<b>a</b>) Konjsko-Velebit and Melina-Velebit currents from both line ends with differential current protection (Δ<span class="html-italic">I</span>). Out-of-step has developed on Konjsko-Velebit line and only stable power swing was present on Melina-Velebit line; (<b>b</b>) Konjsko-Velebit and Melina-Velebit transmission line current with protection operations.</p>
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26 pages, 14014 KiB  
Article
An Artificially Intelligent Technique to Generate Synthetic Geomechanical Well Logs for the Bakken Formation
by George Parapuram, Mehdi Mokhtari and Jalel Ben Hmida
Energies 2018, 11(3), 680; https://doi.org/10.3390/en11030680 - 17 Mar 2018
Cited by 21 | Viewed by 5934
Abstract
Artificially intelligent and predictive modelling of geomechanical properties is performed by creating supervised machine learning data models utilizing artificial neural networks (ANN) and will predict geomechanical properties from basic and commonly used conventional well logs such as gamma ray, and bulk density. The [...] Read more.
Artificially intelligent and predictive modelling of geomechanical properties is performed by creating supervised machine learning data models utilizing artificial neural networks (ANN) and will predict geomechanical properties from basic and commonly used conventional well logs such as gamma ray, and bulk density. The predictive models were created by following the approach on a large volume of data acquired from 112 wells containing the Bakken Formation in North Dakota. The studied wells cover a large surface area of the formation containing the five main producing counties in North Dakota: Burke, Mountrail, McKenzie, Dunn, and Williams. Thus, with a large surface area being analyzed in this research, there is confidence with a high degree of certainty that an extensive representation of the Bakken Formation is modelled, by training neural networks to work on varying properties from the different counties containing the Bakken Formation in North Dakota. Shear wave velocity of 112 wells is also analyzed by regression methods and neural networks, and a new correlation is proposed for the Bakken Formation. The final goal of the research is to achieve supervised artificial neural network models that predict geomechanical properties of future wells with an accuracy of at least 90% for the Upper and Middle Bakken Formation. Thus, obtaining these logs by generating it from statistical and artificially intelligent methods shows a potential for significant improvements in performance, efficiency, and profitability for oil and gas operators. Full article
(This article belongs to the Special Issue Unconventional Natural Gas (UNG) Recoveries 2018)
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<p>Well logs distinguishing the Upper, Middle, and Lower Bakken Formation [<a href="#B11-energies-11-00680" class="html-bibr">11</a>].</p>
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<p>Illustration of a multilayer perceptron ANN model showing the input, hidden, and output layers.</p>
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<p>Flowchart with the phases involved in obtaining the various data-driven models.</p>
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<p>Bakken area in North Dakota, and the distribution of wells with respect to gamma ray, bulk density, compressional and shear wave velocity, sonic porosity, and geomechanical properties.</p>
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<p>Prediction of shear wave velocity linearly with its training and validation set for (<b>a</b>) the Upper Bakken Formation (<b>b</b>) Middle Bakken Formation.</p>
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<p>Flowchart process to obtain model to predict shear wave velocity for new wells.</p>
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<p>Prediction of shear wave velocity using ANN with its training and validation set for (<b>a</b>) the Upper Bakken Formation (<b>b</b>) Middle Bakken Formation.</p>
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<p>Flowchart process to calculate geomechanical properties from elastic waves.</p>
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<p>Flowchart process to obtain model to predict sonic porosity for new wells.</p>
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<p>Prediction of sonic porosity using ANN with its training and validation set for (<b>a</b>) the Upper Bakken Formation (<b>b</b>) Middle Bakken Formation.</p>
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<p>Flowchart process to obtain models to predict geomechanical properties for future wells.</p>
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<p>Distribution of all 112 wells and an additional 5 validation wells.</p>
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<p>Shear wave velocity versus compressional wave velocity of the Upper Bakken Formation, North Dakota.</p>
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<p>(<b>a</b>) Shear wave velocity versus compressional wave velocity for the Middle Bakken Formation. (<b>b</b>) Shear wave velocity versus compressional wave velocity with respect to gamma ray for the Middle Bakken Formation.</p>
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<p>V<sub>p</sub>/V<sub>s</sub> ratio for the Upper and Middle Bakken Formation.</p>
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<p>(<b>a</b>) Comparison of Upper Bakken Shale’s equation with Castagna’s equation. (<b>b</b>) Comparison of Middle Bakken Sandstone’s equation with Han’s equation.</p>
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<p>Comparison of linear and ANN model to predict shear wave velocity. (<b>a</b>) For the Upper Bakken. (<b>b</b>) For the Middle Bakken.</p>
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<p>Actual and predicted geomechanical properties along with depth (feet) for test well 1 in Burke County for the Upper Bakken Shale.</p>
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<p>Actual and predicted geomechanical properties along with depth (feet) for test well 1 in Burke County for the Middle Bakken Formation.</p>
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<p>Actual and predicted geomechanical properties along with depth (feet) for test well 2 in Mountrail County for the Upper Bakken Shale.</p>
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<p>Actual and predicted geomechanical properties along with depth (feet) for test well 2 in Mountrail County for the Middle Bakken Formation.</p>
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<p>Actual and predicted geomechanical properties along with depth (feet) for test well 3 in Dunn County for the Upper Bakken Shale.</p>
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<p>Actual and predicted geomechanical properties along with depth (feet) for test well 3 in Dunn County for the Middle Bakken Formation.</p>
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<p>Actual and predicted geomechanical properties along with depth (feet) for test well 4 in Williams County for the Upper Bakken Shale.</p>
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<p>Actual and predicted geomechanical properties along with depth (feet) for test well 4 in Williams County for the Middle Bakken Formation.</p>
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<p>Actual and predicted geomechanical properties along with depth (feet) for test well 5 in McKenzie County for the Upper Bakken Shale.</p>
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<p>Actual and predicted geomechanical properties along with depth (feet) for test well 5 in McKenzie County for the Middle Bakken Formation.</p>
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11 pages, 2932 KiB  
Article
Thermal Performance of Microencapsulated Phase Change Material (mPCM) in Roof Modules during Daily Operation
by Qi Zhou, Pin-Feng Liu, Chun-Ta Tzeng and Chi-Ming Lai
Energies 2018, 11(3), 679; https://doi.org/10.3390/en11030679 - 17 Mar 2018
Cited by 13 | Viewed by 4010
Abstract
This study combines microencapsulated phase change materials (mPCMs) (core material: paraffin; melting points: 37 and 43 °C) and aluminum honeycomb boards (8 mm core cell) to form mPCM roof modules and investigates their heat absorption and release performances, as well as their impact [...] Read more.
This study combines microencapsulated phase change materials (mPCMs) (core material: paraffin; melting points: 37 and 43 °C) and aluminum honeycomb boards (8 mm core cell) to form mPCM roof modules and investigates their heat absorption and release performances, as well as their impact on indoor heat gain by conducting experiments over a 24-h period, subject to representative weather. The outdoor boundary conditions of the module are hourly sunlight and nighttime natural cooling; on the indoor side of the module, the conditions are daytime air conditioning and nighttime natural cooling. The results indicate that compared to a roof module with a 43 °C melting point mPCM, the roof module with a 37 °C melting point mPCM had improved peak load-shifting capacity, but had a slightly increased indoor heat gain. The mPCMs in both roof modules were successfully cooled during the night, returning to their initial state, to begin a new thermal cycle the next day. Full article
(This article belongs to the Section D: Energy Storage and Application)
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<p>Test cell experimental setup (<b>left</b>) and the tested mPCM roof module (<b>right</b>).</p>
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<p>Normalized heating flux input and measured meteorological data.</p>
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<p>Heat transfer behavior of the tested module (<span class="html-italic">T<sub>m</sub></span> = 37 °C, <span class="html-italic">G<sub>s,o</sub></span> = 200 W/m<sup>2</sup>).</p>
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<p>Heat transfer behavior of the tested module (Tm = 37 °C, Gs,o = 600 W/m<sup>2</sup>).</p>
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<p>Relationship between the heating power peak and the time lag of the heat flux and decrement factor of the heat flux.</p>
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11 pages, 2325 KiB  
Article
Stochastic Stability Analysis of the Power System with Losses
by Hongyu Li, Ping Ju, Chun Gan, Feng Wu, Yichen Zhou and Zhe Dong
Energies 2018, 11(3), 678; https://doi.org/10.3390/en11030678 - 17 Mar 2018
Cited by 2 | Viewed by 3675
Abstract
Renewable energy and electric vehicles have become involved in power systems, which has attracted researchers to stochastic continuous disturbances (SDEs). This paper addresses stochastic analysis issues for the stability of a power system with losses under SDEs. Firstly, the quasi-Hamiltonian models of power [...] Read more.
Renewable energy and electric vehicles have become involved in power systems, which has attracted researchers to stochastic continuous disturbances (SDEs). This paper addresses stochastic analysis issues for the stability of a power system with losses under SDEs. Firstly, the quasi-Hamiltonian models of power systems with losses under SDEs are given. Secondly, a novel analytical method is proposed to analyze the stability of the power system with losses under SDEs based on the stochastic averaging method. Thirdly, comparisons of stability probability under different parameters are performed, from which insights to improve the stability probability of power systems with losses under SDEs can be obtained. Even though it is challenging to assess the stability of a power system with losses under SDEs, the proposed method in this paper could serve well in this regard. Full article
(This article belongs to the Section F: Electrical Engineering)
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<p>The procedure of the proposed stochastic stability analysis method. SCD: stochastic disturbance.</p>
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<p>Potential energy curve near the stable point.</p>
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<p>The coefficients of the equivalent Itō stochastic differential equation for the system energy: (<b>a</b>) the drift coefficient <math display="inline"> <semantics> <mrow> <mover accent="true"> <mi>m</mi> <mo stretchy="false">¯</mo> </mover> </mrow> </semantics> </math>(<span class="html-italic">H</span>); (<b>b</b>) the diffusion coefficient <math display="inline"> <semantics> <mrow> <mover accent="true"> <mi>σ</mi> <mo stretchy="false">¯</mo> </mover> </mrow> </semantics> </math> (<span class="html-italic">H</span>).</p>
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<p>Stability probability of power system with losses under stochastic continuous disturbances.</p>
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<p>Stability probability of power system with losses under stochastic continuous disturbances: (<b>a</b>) damping coefficient; (<b>b</b>) intensity of stochastic continuous disturbances; (<b>c</b>) mechanical power.</p>
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<p>Stability probability of power system with losses under stochastic continuous disturbances: (<b>a</b>) damping coefficient; (<b>b</b>) intensity of stochastic continuous disturbances; (<b>c</b>) mechanical power.</p>
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17 pages, 3425 KiB  
Article
Research on Improved VSG Control Algorithm Based on Capacity-Limited Energy Storage System
by Yanfeng Ma, Zijian Lin, Rennan Yu and Shuqiang Zhao
Energies 2018, 11(3), 677; https://doi.org/10.3390/en11030677 - 16 Mar 2018
Cited by 13 | Viewed by 3737
Abstract
A large scale of renewable energy employing grid connected electronic inverters fail to contribute inertia or damping to power systems, and, therefore, may bring negative effects to the stability of power system. As a solution, an advanced Virtual Synchronous Generator (VSG) control technology [...] Read more.
A large scale of renewable energy employing grid connected electronic inverters fail to contribute inertia or damping to power systems, and, therefore, may bring negative effects to the stability of power system. As a solution, an advanced Virtual Synchronous Generator (VSG) control technology based on Hamilton approach is introduced in this paper firstly to support the frequency and enhance the suitability and robustness of the system. The charge and discharge process of power storage devices forms the virtual inertia and damping of VSG, and, therefore, limits on storage capacity may change the coefficients of VSG. To provide a method in keeping system output in an acceptable level with the capacity restriction in a transient period, an energy control algorithm is designed for VSG adaptive control. Finally, simulations are conducted in DIgSILENT to demonstrate the correctness of the algorithm. The demonstration shows: (1) the proposed control model aims at better system robustness and stability; and (2) the model performs in the environment closer to practical engineering by fitting the operation state of storage system. Full article
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<p>Block diagram of the VSG control strategy.</p>
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<p>Roots of the poles with the variations of the parameters. (<b>a</b>) Pole Loci when <span class="html-italic">M</span> a constant value; (<b>b</b>) Pole Loci when <span class="html-italic">D</span> a constant value.</p>
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<p>Step response with different damping ratio. (<b>a</b>) Active power response in under-damping condition (<b>b</b>) Active power response in over-damping condition.</p>
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<p>Relationship between the Capacity limits of the energy storage equipment and the parameters. (<b>a</b>) Storage capacity limits with <span class="html-italic">M</span>; (<b>b</b>) Storage capacity limits with <span class="html-italic">D.</span></p>
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<p>Relation of <span class="html-italic">M</span> and <span class="html-italic">D</span> under different storage limits. (<b>a</b>) Relation of <span class="html-italic">M</span> and <span class="html-italic">D</span> when <math display="inline"> <semantics> <mrow> <mi mathvariant="sans-serif">∆</mi> <mi>P</mi> </mrow> </semantics> </math><span class="html-italic"><sub>e</sub></span><sub>max</sub> = Const; (<b>b</b>) Relation of <span class="html-italic">M</span> and <span class="html-italic">D</span> when <math display="inline"> <semantics> <mrow> <mi mathvariant="sans-serif">∆</mi> <mi>E</mi> </mrow> </semantics> </math><sub>max</sub> = Const.</p>
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<p>Energy-based control algorithm.</p>
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<p>Energy control algorithm process.</p>
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<p>The simulation system diagram.</p>
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<p>System outputs during short-circuit fault period. (<b>a</b>) Output active power <span class="html-italic">P<sub>e</sub></span>/p.u.; (<b>b</b>) Grid frequency <span class="html-italic">f</span>/p.u.; (<b>c</b>) Transient E.M.F <span class="html-italic">E<sub>q</sub></span>’/p.u.; (<b>d</b>) Output voltage <span class="html-italic">U</span><sub>0</sub>/p.u.</p>
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<p>Output responses with different damping under short-circuit fault condition. (<b>a</b>) Comparisons of <span class="html-italic">P<sub>e</sub></span>/p.u.; (<b>b</b>) Comparisons of <span class="html-italic">f</span>/p.u.</p>
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<p>Output responses with different virtual inertia under short-circuit fault condition. (<b>a</b>) Comparisons of <span class="html-italic">P<sub>e</sub></span>/p.u.; (<b>b</b>) Comparisons of <span class="html-italic">f</span>/p.u.</p>
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<p>Figures of system output with limited/limit-free energy storage capacity. (<b>a</b>) Variation of the virtual inertia; (<b>b</b>) Comparison of <span class="html-italic">P<sub>e</sub></span>/p.u; (<b>c</b>) Comparison of <span class="html-italic">f</span>/p.u.</p>
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8 pages, 5965 KiB  
Article
A Hybrid Excited Machine with Flux Barriers and Magnetic Bridges
by Marcin Wardach, Piotr Paplicki and Ryszard Palka
Energies 2018, 11(3), 676; https://doi.org/10.3390/en11030676 - 16 Mar 2018
Cited by 24 | Viewed by 4219
Abstract
In this paper, an U-shape flux barrier rotor concept for a hybrid excited synchronous machine with flux magnetic bridges fixed on the rotor is presented. Using 3D finite element analysis, the influence of axial flux bridges on the field-weakening and -strengthening characteristics, electromagnetic [...] Read more.
In this paper, an U-shape flux barrier rotor concept for a hybrid excited synchronous machine with flux magnetic bridges fixed on the rotor is presented. Using 3D finite element analysis, the influence of axial flux bridges on the field-weakening and -strengthening characteristics, electromagnetic torque, no-load magnetic flux linkage, rotor iron losses and back electromotive force is shown. Three different rotor designs are analyzed. Furthermore, the field control characteristics depending on additional DC control coil currents are shown. Full article
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<p>3D-FEA model (<b>a</b>); stack of axial bridge lamination using in a machine prototype (<b>b</b>).</p>
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<p>Three concepts of magnetic active parts of rotor of the electric controlled permanent magnet synchronous (ECPMS) machine: UB1 concept (<b>a</b>); UB concept (<b>b</b>); U concept (<b>c</b>).</p>
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<p>No-load magnetic flux distribution for the rotor UB1 concept.</p>
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<p>Characteristics of magnetic flux linkage Ψ<span class="html-italic"><sub>s</sub></span> versus DC field excitation.</p>
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<p>Back-EMF waveforms at different DC field excitation for the UB1 rotor concept.</p>
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<p>Torque ripple at AC 30Arms at different DC field excitations for the UB1 rotor concept.</p>
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<p>Cogging torque versus the rotor position at different DC field excitations for the UB1 rotor concept.</p>
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<p>Losses of M400-50A lamination.</p>
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<p>The <span class="html-italic">B</span>–<span class="html-italic">H</span> curve of M400-50A lamination.</p>
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<p>Mean values of rotor iron losses of UB1, UB, and U rotor concepts.</p>
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19 pages, 2100 KiB  
Article
Life Cycle Performance of Hydrogen Production via Agro-Industrial Residue Gasification—A Small Scale Power Plant Study
by Sara Rajabi Hamedani, Mauro Villarini, Andrea Colantoni, Michele Moretti and Enrico Bocci
Energies 2018, 11(3), 675; https://doi.org/10.3390/en11030675 - 16 Mar 2018
Cited by 29 | Viewed by 5722
Abstract
This study evaluates the environmental profile of a real biomass-based hydrogen production small-scale (1 MWth) system composed of catalytic candle indirectly heated steam gasifier coupled with zinc oxide (ZnO) guard bed, water gas shift (WGS) and pressure swing absorber (PSA) reactors. [...] Read more.
This study evaluates the environmental profile of a real biomass-based hydrogen production small-scale (1 MWth) system composed of catalytic candle indirectly heated steam gasifier coupled with zinc oxide (ZnO) guard bed, water gas shift (WGS) and pressure swing absorber (PSA) reactors. Environmental performance from cradle-to-gate was investigated by life cycle assessment (LCA) methodology. Biomass production shows high influence over all impact categories. In the syngas production process, the main impacts observed are global warming potential (GWP) and acidification potential (AP). Flue gas emission from gasifier burner has the largest proportion of total GWP. The residual off gas use in internal combustion engine (ICE) leads to important environmental savings for all categories. Hydrogen renewability score is computed as 90% due to over 100% decline in non-renewable energy demand. Sensitivity analysis shows that increase in hydrogen production efficiency does not necessarily result in decrease in environmental impacts. In addition, economic allocation of environmental charges increases all impact categories, especially AP and photochemical oxidation (POFP). Full article
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<p>Flow sheet of the plant evaluated in this study.</p>
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<p>A picture of the plant.</p>
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<p>Life cycle boundaries for hydrogen production system.</p>
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<p>Relative contributions from subsystems involved to each impact category.</p>
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<p>Breakdown of contributions from process involved in Subsystem 1.</p>
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<p>Breakdown of contributions from process involved in Subsystem 4.</p>
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<p>Hydrogen renewability for each technology [<a href="#B6-energies-11-00675" class="html-bibr">6</a>].</p>
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<p>Sensitive analysis results for increase in hydrogen production.</p>
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12 pages, 3810 KiB  
Article
Properties of Hydrochar as Function of Feedstock, Reaction Conditions and Post-Treatment
by Andrea Kruse and Thomas A. Zevaco
Energies 2018, 11(3), 674; https://doi.org/10.3390/en11030674 - 16 Mar 2018
Cited by 61 | Viewed by 7439
Abstract
Hydrothermal carbonization (HTC) is a promising technology to convert wet biomass into carbon-rich materials. Until now, the chemical processes occurring and their influence on the product properties are not well understood. Therefore, a target-oriented production of materials with defined properties is difficult, if [...] Read more.
Hydrothermal carbonization (HTC) is a promising technology to convert wet biomass into carbon-rich materials. Until now, the chemical processes occurring and their influence on the product properties are not well understood. Therefore, a target-oriented production of materials with defined properties is difficult, if not impossible. Here, model compounds such as cellulose and lignin, as well as different definite biomasses such as straw and beech wood are converted by hydrothermal carbonization. Following this, thermogravimetic (TGA) and FTIR measurements are used to get information about chemical structure and thermal properties of the related hydrochars. Some of the isolated materials are thermally post-treated (490 °C and 700 °C) and analyzed. The results show that at “mild” HTC conversion, the cellulose part in a lignocellulose matrix is not completely carbonized and there is still cellulose present. Thermal post-treatment makes the properties of product materials more similar and shows complete carbonization with increase aromatic cross-linking, proven by TGA and FTIR results. Full article
(This article belongs to the Special Issue Thermo Fluid Conversion of Biomass)
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Graphical abstract
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<p>Primary reaction pathways A and B for the formation of HTC-coal (modified from [<a href="#B6-energies-11-00674" class="html-bibr">6</a>]). A is a path via polymerization of solved molecules, B is a solid-to-solid conversion. The SEM pictures show hydrochar from glucose (<b>left</b>) and lignin (<b>right</b>).</p>
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<p>Structure of lignocellulose. Reproduced from Ref. [<a href="#B10-energies-11-00674" class="html-bibr">10</a>] with permission from The Royal Society of Chemistry.</p>
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<p>DTG curves of the model compounds cellulose, xylan and lignin, modified from [<a href="#B21-energies-11-00674" class="html-bibr">21</a>].</p>
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<p>DTG of straw and beach wood chips, modified from [<a href="#B21-energies-11-00674" class="html-bibr">21</a>].</p>
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<p>DTG of hydrochar from beach chips (10% dry mass, 220 °C), produced with different reaction times, modified from [<a href="#B21-energies-11-00674" class="html-bibr">21</a>].</p>
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<p>DTG of hydrochar from straw (10% dry mass, 220 °C), produced with different reaction times, modified from [<a href="#B21-energies-11-00674" class="html-bibr">21</a>].</p>
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<p>DTG of hydrochar cauliflower, field garlic and potato/carrot mixture (220 °C, 10% dry mass, 4 h). (Modified from [<a href="#B21-energies-11-00674" class="html-bibr">21</a>]).</p>
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<p>DTG of hydrochar from hydroxymethylfurfural.</p>
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<p>FTIR spectra of hydrochars from straw, cellulose, and lignin. HTC conditions: 220 °C, 10% DM (Dry Mater), 4 h reaction time (Modified from [<a href="#B21-energies-11-00674" class="html-bibr">21</a>]).</p>
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<p>IR spectra of lignin and its hydrochar (10% DM, 220 °C, 4 h). (Modified from [<a href="#B21-energies-11-00674" class="html-bibr">21</a>]).</p>
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<p>IR spectra of cellulose and its hydrochar (10% DM, 220 °C, and 2 h). (Modified from [<a href="#B21-energies-11-00674" class="html-bibr">21</a>]).</p>
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<p>IR spectra of cellulose and its hydrochar (20% DM, 250 °C, and 2 h). (Modified from [<a href="#B21-energies-11-00674" class="html-bibr">21</a>]).</p>
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<p>DTG of thermal treated hydrochars of beech wood and straw at 490 and 700 °C, respectively. (Modified from [<a href="#B21-energies-11-00674" class="html-bibr">21</a>]).</p>
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<p>FTIR spectra of thermal post-treated hydrochar from beech wood and straw. Post-treatment temperatures were 490 and 700 °C. For comparison, the spectrum of a not post-treated hydrochar from lignin is added (HTC: 220 °C, 4 h, 10% DM). (Modified from [<a href="#B21-energies-11-00674" class="html-bibr">21</a>]).</p>
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13 pages, 4658 KiB  
Article
A Three-Coil Inductively Power Transfer System with Constant Voltage Output
by Ruikun Mai, Youyuan Zhang, Ruimin Dai, Yang Chen and Zhengyou He
Energies 2018, 11(3), 673; https://doi.org/10.3390/en11030673 - 16 Mar 2018
Cited by 10 | Viewed by 4633
Abstract
For a traditional 2-coil system outputting constant voltage (CV), the transfer efficiency decreases drastically as transfer distance increases. To solve this problem, this essay proposes a 3-coil system which could achieve CV output and Zero Phase Angle (ZPA) conditions with specific parameter values. [...] Read more.
For a traditional 2-coil system outputting constant voltage (CV), the transfer efficiency decreases drastically as transfer distance increases. To solve this problem, this essay proposes a 3-coil system which could achieve CV output and Zero Phase Angle (ZPA) conditions with specific parameter values. This 3-coil system could partly relief transfer efficiency fall at a long transfer distance, without any complicated controls. In order to achieve CV and ZPA condition, this essay devises the parameter values based on the decoupling 3-coil model, and a prototype is designed accordingly to verify these characteristics. With 10 cm transfer distance, output voltage deviation is 5.5% as the load varies from 12 Ω to 200 Ω, proving that the output voltage almost keeps constant with load change. Furthermore, with software simulation, a comparison experiment between the proposed 3-coil system and a Series-Inductor-Capacitor-Inductor (S-LCL) compensated 2-coil system is built to verify the efficiency improvement. The transfer distance change leads to the differentiation of voltage gain for both 2-coil and 3-coil systems. So, the input voltage for both systems and the compensated capacitor in receiver loop of the 3-coil system are manipulated for keeping 60 V output voltage on the 12 Ω load. With distance increasing from 10 cm to 20 cm, transfer efficiency varies from 92.61 to 48.9% for the 2-coil system, and from 92.89 to 84.26% for the 3-coil system, effectively proving the efficiency improvement. The experiment and simulation results prove the effectiveness of the proposed method. Full article
(This article belongs to the Special Issue Wireless Power Transfer 2018)
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<p>Configuration of the proposed series-series compensated 3-coil IPT (inductive power transfer) system.</p>
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<p>Resonant tank in (<b>a</b>) equivalent circuits model (<b>b</b>) 3D module in Maxwell.</p>
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<p>Decoupled circuit of resonant tank for the proposed 3-coil system.</p>
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<p>S-LCL compensated 2-coil system from [<a href="#B22-energies-11-00673" class="html-bibr">22</a>].</p>
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<p>(<b>a</b>) Prototype of the 3-coil system (<b>b</b>) Resonant tank of the 3-coil system.</p>
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<p>Output voltage and efficiency respectively versus load resistance for the 3-coil system.</p>
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<p>Experimental waveforms of <span class="html-italic">V<sub>in</sub></span>, <span class="html-italic">I<sub>in</sub></span>, <span class="html-italic">V<sub>B</sub></span>, <span class="html-italic">I<sub>B</sub></span> at (<b>a</b>) <math display="inline"> <semantics> <mrow> <mtext> </mtext> <msub> <mi>R</mi> <mi>L</mi> </msub> <mo>=</mo> <mn>12</mn> <mo> </mo> <mi mathvariant="sans-serif">Ω</mi> </mrow> </semantics> </math> (<b>b</b>) <math display="inline"> <semantics> <mrow> <msub> <mi>R</mi> <mi>L</mi> </msub> <mo>=</mo> <mn>120</mn> <mo> </mo> <mi mathvariant="sans-serif">Ω</mi> </mrow> </semantics> </math>.</p>
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<p>Experimental waves of <span class="html-italic">V<sub>in</sub></span>, <span class="html-italic">I<sub>in</sub></span>, <span class="html-italic">V<sub>DS</sub></span>, <span class="html-italic">V<sub>GS</sub></span>.</p>
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<p>The transient waveforms of <span class="html-italic">V<sub>in</sub></span>, <span class="html-italic">I<sub>in</sub></span>, <span class="html-italic">V<sub>B</sub></span>, <span class="html-italic">I<sub>B</sub></span> when the load switches between 12 Ω and 50 Ω.</p>
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<p>Mutual-inductance Versus Transfer distance.</p>
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<p>Current Versus Transfer Distance for 2-coil System.</p>
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<p>Current Versus Transfer Distance for 3-coil System.</p>
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<p>Mutual-inductance Versus Transfer Distance.</p>
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13 pages, 4252 KiB  
Article
Solar Cell Capacitance Determination Based on an RLC Resonant Circuit
by Petru Adrian Cotfas, Daniel Tudor Cotfas, Paul Nicolae Borza, Dezso Sera and Remus Teodorescu
Energies 2018, 11(3), 672; https://doi.org/10.3390/en11030672 - 16 Mar 2018
Cited by 21 | Viewed by 9539
Abstract
The capacitance is one of the key dynamic parameters of solar cells, which can provide essential information regarding the quality and health state of the cell. However, the measurement of this parameter is not a trivial task, as it typically requires high accuracy [...] Read more.
The capacitance is one of the key dynamic parameters of solar cells, which can provide essential information regarding the quality and health state of the cell. However, the measurement of this parameter is not a trivial task, as it typically requires high accuracy instruments using, e.g., electrical impedance spectroscopy (IS). This paper introduces a simple and effective method to determine the electric capacitance of the solar cells. An RLC (Resistor Inductance Capacitor) circuit is formed by using an inductor as a load for the solar cell. The capacitance of the solar cell is found by measuring the frequency of the damped oscillation that occurs at the moment of connecting the inductor to the solar cell. The study is performed through simulation based on National Instruments (NI) Multisim application as SPICE simulation software and through experimental capacitance measurements of a monocrystalline silicon commercial solar cell and a photovoltaic panel using the proposed method. The results were validated using impedance spectroscopy. The differences between the capacitance values obtained by the two methods are of 1% for the solar cells and of 9.6% for the PV panel. The irradiance level effect upon the solar cell capacitance was studied obtaining an increase in the capacitance in function of the irradiance. By connecting different inductors to the solar cell, the frequency effect upon the solar cell capacitance was studied noticing a very small decrease in the capacitance with the frequency. Additionally, the temperature effect over the solar cell capacitance was studied achieving an increase in capacitance with temperature. Full article
(This article belongs to the Special Issue PV System Design and Performance)
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<p>The DC equivalent electrical circuit of a solar cell.</p>
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<p>The AC equivalent electrical circuit of a solar cell.</p>
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<p>The electrical circuit obtained connecting an inductance coil to a solar cell.</p>
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<p>The electrical circuit used to study the AC solar cell parameters.</p>
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<p>The voltage and current waveforms obtained in simulation (<span class="html-italic">C<sub>p</sub></span> = 0.433 μF, <span class="html-italic">R<sub>sh</sub></span> = 777 Ω, <span class="html-italic">L</span><sub>1</sub> = 566 μH, <span class="html-italic">R<sub>s</sub></span> = 143 mΩ, <span class="html-italic">R</span><sub>1</sub> = 100 mΩ).</p>
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<p>The simulated I-V characteristic obtained with the <span class="html-italic">L</span><sub>1</sub> as variable load.</p>
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<p>The dependence of the SC capacitance function of the frequency.</p>
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<p>SC capacitance obtained through IS and RLC methods.</p>
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<p>The variation of the solar cell capacitance depending on temperature.</p>
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16 pages, 5169 KiB  
Article
Numerical Study on Heat Transfer to an Arc Absorber Designed for a Waste Heat Recovery System around a Cement Kiln
by Mojtaba Mirhosseini, Alireza Rezaniakolaei and Lasse Rosendahl
Energies 2018, 11(3), 671; https://doi.org/10.3390/en11030671 - 16 Mar 2018
Cited by 15 | Viewed by 4129
Abstract
A numerical study on combined free convection, forced convection, and radiation heat transfers from an industrial isothermal rotating cylinder (cement kiln) is carried out in this work. The investigation is done by the study of two-dimensional (2D) incompressible turbulent flow around the kiln [...] Read more.
A numerical study on combined free convection, forced convection, and radiation heat transfers from an industrial isothermal rotating cylinder (cement kiln) is carried out in this work. The investigation is done by the study of two-dimensional (2D) incompressible turbulent flow around the kiln under steady- and unsteady-state solutions. The results of this study show that the average Reynolds and Rayleigh numbers around the cylindrical kiln are 647,812.1 and 1.75986 × 1011, respectively. A heat absorber is specifically designed around the kiln, according to the available space around the kiln, in a sample cement factory. The study investigates the effect of an added absorber on the heat transfer features, for both constant heat flux and constant temperature, on the kiln. The temperature distribution along the absorber circumference is obtained for designing an efficient thermoelectric waste heat recovery system as a future study. It is observed that the contribution of the radiative heat transfer is significant in the total heat transferred from the kiln to the absorber. Full article
(This article belongs to the Section I: Energy Fundamentals and Conversion)
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Graphical abstract
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<p>Schematic view of the two-dimensional (2D) computational domain (<span class="html-italic">U<sub>∞</sub></span>: free stream velocity, <span class="html-italic">T<sub>∞</sub></span>: ambient temperature, D: kiln diameter, x, y: Cartesian coordinate system).</p>
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<p>(<b>a</b>) Aalborg Portland cement kiln, and schematic view of the kiln and absorber; (<b>b</b>) temperature profile along the kiln’s external surface.</p>
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<p>(<b>a</b>) Meshed study domain in the ANSYS meshing tool; (<b>b</b>) grid pattern near the kiln and absorber wall.</p>
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<p>Contours of temperature, velocity, pressure, eddy viscosity, and velocity streamlines around the kiln in the presence of the absorber.</p>
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<p>Contours of temperature, velocity, pressure, eddy viscosity, and velocity streamlines around the kiln in the presence of the absorber.</p>
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<p>Time-averaged local convective, radiative, and total heat flux around the kiln surface while temperature of the kiln is constant.</p>
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<p>Temperature distribution along the outer surface of the absorber while temperature of the kiln is constant.</p>
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<p>Cross-sectional contour of temperature of the absorber body (unsteady solution).</p>
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<p>Local radiative heat flux, convective heat flux, and temperature distribution on the kiln while heat flux on the kiln is constant.</p>
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<p>Temperature distribution along the outer surface of the absorber while heat flux on the kiln is constant.</p>
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16 pages, 9762 KiB  
Article
Comparison of the Net Work Output between Stirling and Ericsson Cycles
by Rui F. Costa and Brendan D. MacDonald
Energies 2018, 11(3), 670; https://doi.org/10.3390/en11030670 - 16 Mar 2018
Cited by 5 | Viewed by 5814
Abstract
In this paper, we compare Stirling and Ericsson cycles to determine which engine produces greater net work output for various situations. Both cycles are for external heat engines that utilize regenerators, where the difference is the nature of the regeneration process, which is [...] Read more.
In this paper, we compare Stirling and Ericsson cycles to determine which engine produces greater net work output for various situations. Both cycles are for external heat engines that utilize regenerators, where the difference is the nature of the regeneration process, which is constant volume for Stirling and constant pressure for Ericsson. This difference alters the performance characteristics of the two engines drastically, and our comparison reveals which one produces greater net work output based on the thermodynamic parameters. The net work output equations are derived and analysed for three different scenarios: (i) equal mass and temperature limits; (ii) equal mass and pressure or volume; and (iii) equal temperature and pressure or volume limits. The comparison is performed by calculating when both cycles produce equal net work output and then analysing which one produces greater net work output based on how the parameters are varied. In general, the results demonstrate that Stirling engines produce more net work output at higher pressures and lower volumes, and Ericsson engines produce more net work output at lower pressures and higher volumes. For certain scenarios, threshold values are calculated to illustrate precisely when one cycle produces more net work output than the other. This paper can be used to inform the design of the engines and to determine when a Stirling or Ericsson engine should be selected for a particular application. Full article
(This article belongs to the Section I: Energy Fundamentals and Conversion)
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<p><span class="html-italic">P</span>-<span class="html-italic">V</span> diagram of the Stirling cycle and <span class="html-italic">T</span>-<span class="html-italic">s</span> diagram (inset).</p>
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<p><span class="html-italic">P</span>-<span class="html-italic">V</span> diagram of the Ericsson cycle and <span class="html-italic">T</span>-<span class="html-italic">s</span> diagram (inset).</p>
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<p>(<b>a</b>) Pressure ratio relationship and (<b>b</b>) volume ratio relationship between Stirling and Ericsson cycles, where the solid lines indicate equal net work output for equivalent mass and temperature limits.</p>
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<p><span class="html-italic">P</span>-<span class="html-italic">V</span> diagrams of Stirling and Ericsson cycles for fixed mass and temperature limits with (<b>a</b>) fixed maximum pressure and volume and (<b>b</b>) fixed minimum pressure and volume.</p>
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<p>(<b>a</b>) High temperature relationship between Stirling and Ericsson cycles for fixed pressure ratios and <math display="inline"> <semantics> <mrow> <msub> <mi>T</mi> <mi>L</mi> </msub> <mo>=</mo> <mn>25</mn> <msup> <mspace width="0.166667em"/> <mo>∘</mo> </msup> </mrow> </semantics> </math>C, with solid lines denoting equal net work output; and (<b>b</b>) <span class="html-italic">P</span>-<span class="html-italic">V</span> diagrams of Stirling and Ericsson cycles for fixed mass, pressure limits and low temperature for equal net work output.</p>
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<p>(<b>a</b>) High temperature relationship between Stirling and Ericsson cycles for fixed volume ratios and <math display="inline"> <semantics> <mrow> <msub> <mi>T</mi> <mi>L</mi> </msub> <mo>=</mo> <mn>25</mn> <msup> <mspace width="0.166667em"/> <mo>∘</mo> </msup> </mrow> </semantics> </math>C, with solid lines denoting equal net work output; (<b>b</b>) <span class="html-italic">P</span>-<span class="html-italic">V</span> diagrams of Stirling and Ericsson cycles for fixed mass, volume limits and low temperature for equal net work output.</p>
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<p>(<b>a</b>) Relationship between pressure and temperature ratio for equal net work output and <span class="html-italic">P</span>-<span class="html-italic">V</span> diagram of Stirling and Ericsson cycles for fixed temperature and pressure limits and maximum volume for equal net work output (inset); (<b>b</b>) non-dimensional net work as a function of pressure ratio and temperature ratio (inset).</p>
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<p>(<b>a</b>) Relationship between volume and temperature ratio for equal net work output and <span class="html-italic">P</span>-<span class="html-italic">V</span> diagram of Stirling and Ericsson cycles for fixed temperature and volume limits and maximum pressure for equal net work output (inset); (<b>b</b>) non-dimensional net work as a function of volume ratio and temperature ratio (inset).</p>
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23 pages, 2463 KiB  
Article
Comparative Study of Energy Performance between Chip and Inlet Temperature-Aware Workload Allocation in Air-Cooled Data Center
by Yan Bai, Lijun Gu and Xiao Qi
Energies 2018, 11(3), 669; https://doi.org/10.3390/en11030669 - 16 Mar 2018
Cited by 15 | Viewed by 4001
Abstract
Improving the energy efficiency of data center has become a research focus in recent years. Previous works commonly adopted the inlet temperature constraint to optimize the thermal environment in the data center. However, the inlet temperature-aware method cannot prevent the servers from over-cooling. [...] Read more.
Improving the energy efficiency of data center has become a research focus in recent years. Previous works commonly adopted the inlet temperature constraint to optimize the thermal environment in the data center. However, the inlet temperature-aware method cannot prevent the servers from over-cooling. To cope with this issue, we propose a thermal-aware workload allocation strategy with respect to the chip temperature constraint. In this paper, we conducted a comparative evaluation of the performance between the chip and inlet temperature-aware workload allocation strategies. The workload allocation strategies adopt a POD-based heat recirculation model to characterize the thermal environment in data center. The contribution of the temperature-dependent leakage power to server power consumption is also considered. We adopted a sample data center under constant-flow and variable-flow cooling air supply to evaluate the performance of these two different workload allocation strategies. The comparison results show that the chip temperature-aware workload allocation strategy prevents the servers from over-cooling and significantly improves the energy efficiency of data center, especially for the case of variable-flow cooling air supply. Full article
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<p>Typical air cooling system in data center.</p>
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<p>Schematic of thermal cross-interference in data center.</p>
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<p>Schematic diagram of POD method for cross-interference coefficient prediction.</p>
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<p>Data center layout used in our study.</p>
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<p>Energy distribution of POD basis.</p>
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<p>(<b>a</b>) POD reconstruction of cross-interference matrix for <math display="inline"> <semantics> <mrow> <msub> <mi>V</mi> <mrow> <mi>s</mi> <mi>u</mi> <mi>p</mi> </mrow> </msub> </mrow> </semantics> </math> = 8.19 m<sup>3</sup>/s. (<b>b</b>) Reconstruction error of cross-interference matrix for <math display="inline"> <semantics> <mrow> <msub> <mi>V</mi> <mrow> <mi>s</mi> <mi>u</mi> <mi>p</mi> </mrow> </msub> </mrow> </semantics> </math> = 8.19 m<sup>3</sup>/s. (<b>c</b>) POD reconstruction of cross-interference matrix for <math display="inline"> <semantics> <mrow> <msub> <mi>V</mi> <mrow> <mi>s</mi> <mi>u</mi> <mi>p</mi> </mrow> </msub> </mrow> </semantics> </math> = 10.71 m<sup>3</sup>/s. (<b>d</b>) Reconstruction error of cross-interference matrix for <math display="inline"> <semantics> <mrow> <msub> <mi>V</mi> <mrow> <mi>s</mi> <mi>u</mi> <mi>p</mi> </mrow> </msub> </mrow> </semantics> </math> = 10.71 m<sup>3</sup>/s. (<b>e</b>) POD reconstruction of cross-interference matrix for <math display="inline"> <semantics> <mrow> <msub> <mi>V</mi> <mrow> <mi>s</mi> <mi>u</mi> <mi>p</mi> </mrow> </msub> </mrow> </semantics> </math> = 13.23 m<sup>3</sup>/s. (<b>f</b>) Reconstruction error of cross-interference matrix for <math display="inline"> <semantics> <mrow> <msub> <mi>V</mi> <mrow> <mi>s</mi> <mi>u</mi> <mi>p</mi> </mrow> </msub> </mrow> </semantics> </math> = 13.23 m<sup>3</sup>/s.</p>
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<p>Chip temperature distributions achieved by different strategies under constant-flow cooling air supply. (<b>a</b>) Chip temperature-aware workload allocation, 30% data center utilization. (<b>b</b>) Inlet temperature-aware workload allocation, 30% data center utilization. (<b>c</b>) Chip temperature-aware workload allocation, 50% data center utilization. (<b>d</b>) Inlet temperature-aware workload allocation, 50% data center utilization. (<b>e</b>) Chip temperature-aware workload allocation, 70% data center utilization. (<b>f</b>) Inlet temperature-aware workload allocation, 70% data center utilization. (<b>g</b>) Chip temperature-aware workload allocation, 90% data center utilization. (<b>h</b>) Inlet temperature-aware workload allocation, 90% data center utilization.</p>
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<p>Chip temperature distributions achieved by different strategies under constant-flow cooling air supply. (<b>a</b>) Chip temperature-aware workload allocation, 30% data center utilization. (<b>b</b>) Inlet temperature-aware workload allocation, 30% data center utilization. (<b>c</b>) Chip temperature-aware workload allocation, 50% data center utilization. (<b>d</b>) Inlet temperature-aware workload allocation, 50% data center utilization. (<b>e</b>) Chip temperature-aware workload allocation, 70% data center utilization. (<b>f</b>) Inlet temperature-aware workload allocation, 70% data center utilization. (<b>g</b>) Chip temperature-aware workload allocation, 90% data center utilization. (<b>h</b>) Inlet temperature-aware workload allocation, 90% data center utilization.</p>
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<p>Power consumption of data center under constant-flow cooling air supply.</p>
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<p>Power consumption and cooling parameters of data center achieved by inlet temperature-aware workload allocation strategy. (<b>a</b>) Power consumption under different data center utilization. (<b>b</b>) Cooling parameters under different data center utilization.</p>
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<p>Power consumption and cooling parameters of data center achieved by inlet temperature-aware workload allocation strategy. (<b>a</b>) Power consumption under different data center utilization. (<b>b</b>) Cooling parameters under different data center utilization.</p>
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<p>Workload distributions achieved by inlet temperature-aware workload allocation strategy. (<b>a</b>) Constant-flow cooling air supply, 30% data center utilization. (<b>b</b>) Variable-flow cooling air supply, 30% data center utilization. (<b>c</b>) Constant-flow cooling air supply, 50% data center utilization. (<b>d</b>) Variable-flow cooling air supply, 50% data center utilization. (<b>e</b>) Constant-flow cooling air supply, 70% data center utilization. (<b>f</b>) Variable-flow cooling air supply, 70% data center utilization. (<b>g</b>) Constant-flow cooling air supply, 90% data center utilization. (<b>h</b>) Variable-flow cooling air supply, 90% data center utilization.</p>
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<p>Workload distributions achieved by inlet temperature-aware workload allocation strategy. (<b>a</b>) Constant-flow cooling air supply, 30% data center utilization. (<b>b</b>) Variable-flow cooling air supply, 30% data center utilization. (<b>c</b>) Constant-flow cooling air supply, 50% data center utilization. (<b>d</b>) Variable-flow cooling air supply, 50% data center utilization. (<b>e</b>) Constant-flow cooling air supply, 70% data center utilization. (<b>f</b>) Variable-flow cooling air supply, 70% data center utilization. (<b>g</b>) Constant-flow cooling air supply, 90% data center utilization. (<b>h</b>) Variable-flow cooling air supply, 90% data center utilization.</p>
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<p>Power consumption and cooling parameters of data center achieved by chip temperature-aware workload allocation strategy. (<b>a</b>) Power consumption under different data center utilization. (<b>b</b>) Cooling parameters under different data center utilization.</p>
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<p>Workload distributions achieved by chip temperature-aware workload allocation strategy. (<b>a</b>) Constant-flow cooling air supply, 30% data center utilization. (<b>b</b>) Variable-flow cooling air supply, 30% data center utilization. (<b>c</b>) Constant-flow cooling air supply, 50% data center utilization. (<b>d</b>) Variable-flow cooling air supply, 50% data center utilization. (<b>e</b>) Constant-flow cooling air supply, 70% data center utilization. (<b>f</b>) Variable-flow cooling air supply, 70% data center utilization. (<b>g</b>) Constant-flow cooling air supply, 90% data center utilization. (<b>h</b>) Variable-flow cooling air supply, 90% data center utilization.</p>
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<p>Total power consumption of data center for the case study.</p>
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<p>Power Usage Effectiveness for case study.</p>
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22 pages, 966 KiB  
Article
Availability of Ambient RF Energy in d-Dimensional Wireless Networks
by Hongxing Xia, Yongzhao Li, Hailin Zhang and Balasubramaniam Natarajan
Energies 2018, 11(3), 668; https://doi.org/10.3390/en11030668 - 15 Mar 2018
Cited by 1 | Viewed by 2962
Abstract
Radio frequency (RF) enabled energy harvesting has garnered increasingly broad applications in energy-constrained wireless networks. In this context, the actual available energy is constrained by the harvesting threshold of RF harvesters. In this paper, we first propose two new metrics, effective energy harvesting [...] Read more.
Radio frequency (RF) enabled energy harvesting has garnered increasingly broad applications in energy-constrained wireless networks. In this context, the actual available energy is constrained by the harvesting threshold of RF harvesters. In this paper, we first propose two new metrics, effective energy harvesting probability (EEHP) and spatial mean harvestable energy (SMHE) to characterize the availability of ambient RF energy. Assuming that the transmitters are spatially distributed according to a d-dimensional homogeneous Poisson point process (HPPP), we derive the distributions of the ambient RF energy for networks, from the perspective of information receivers, with and without interference control (IC). The corresponding EEHP and SMHE are given in integral forms for the case with IC and inverse Laplace transform form for the case without IC, respectively. For a special case where the dimension to path loss ratio equals 0.5, closed-form exact/approximate expressions for EEHP and SMHE are derived. Analytical results are validated by Monte Carlo simulations. Numerical results with distinct network parameters indicate that the harvesting threshold always has a significant effect on the EEHP, while the impact on SMHE can be ignored as the transmitter density increases. The general unified framework considered in this paper expands the applicability of the derived results to arbitrary dimensional networks. Full article
(This article belongs to the Section F: Electrical Engineering)
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<p>Spatial model of <span class="html-italic">d</span>-dimensional networks for <math display="inline"> <semantics> <mrow> <mi>d</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mspace width="4pt"/> <mi>and</mi> <mspace width="4pt"/> <mn>3</mn> </mrow> </semantics> </math>.</p>
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<p>Effective energy earvesting probability for LSWN-IC with <math display="inline"> <semantics> <mrow> <msub> <mi>λ</mi> <mi>t</mi> </msub> <mo>=</mo> <mn>0.1</mn> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>4</mn> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <msub> <mi>η</mi> <mi>a</mi> </msub> <mo>=</mo> <mn>1</mn> </mrow> </semantics> </math> and <math display="inline"> <semantics> <msub> <mi>P</mi> <mrow> <mi>e</mi> <mi>t</mi> </mrow> </msub> </semantics> </math> = 30 dBm.</p>
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<p>Effective energy harvesting probability for LSWN-IC with <math display="inline"> <semantics> <mrow> <msub> <mi>λ</mi> <mi>t</mi> </msub> <mo>=</mo> <mn>0.0001</mn> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>4</mn> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <msub> <mi>η</mi> <mi>a</mi> </msub> <mo>=</mo> <mn>1</mn> </mrow> </semantics> </math> and <math display="inline"> <semantics> <msub> <mi>P</mi> <mrow> <mi>e</mi> <mi>t</mi> </mrow> </msub> </semantics> </math> = 30 dBm.</p>
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<p>Effective energy harvesting probability for LSWN-noIC with <math display="inline"> <semantics> <mrow> <msub> <mi>λ</mi> <mi>t</mi> </msub> <mo>=</mo> <mn>0.1</mn> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>4</mn> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <msub> <mi>η</mi> <mi>a</mi> </msub> <mo>=</mo> <mn>1</mn> </mrow> </semantics> </math> and <math display="inline"> <semantics> <msub> <mi>P</mi> <mrow> <mi>e</mi> <mi>t</mi> </mrow> </msub> </semantics> </math> = 30 dBm.</p>
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<p>Effective energy harvesting probability for LSWN-noIC with <math display="inline"> <semantics> <mrow> <msub> <mi>λ</mi> <mi>t</mi> </msub> <mo>=</mo> <mn>0.0001</mn> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>4</mn> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <msub> <mi>η</mi> <mi>a</mi> </msub> <mo>=</mo> <mn>1</mn> </mrow> </semantics> </math> and <math display="inline"> <semantics> <msub> <mi>P</mi> <mrow> <mi>e</mi> <mi>t</mi> </mrow> </msub> </semantics> </math> = 30 dBm.</p>
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<p>Spatial mean harvestable energy for LSWN-IC with <math display="inline"> <semantics> <mrow> <msub> <mi>λ</mi> <mi>t</mi> </msub> <mo>=</mo> <mn>0.1</mn> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>4</mn> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <msub> <mi>η</mi> <mi>a</mi> </msub> <mo>=</mo> <mn>1</mn> </mrow> </semantics> </math> and <math display="inline"> <semantics> <msub> <mi>P</mi> <mrow> <mi>e</mi> <mi>t</mi> </mrow> </msub> </semantics> </math> = 30 dBm.</p>
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<p>Spatial mean harvestable energy for LSWN-IC with <math display="inline"> <semantics> <mrow> <msub> <mi>λ</mi> <mi>t</mi> </msub> <mo>=</mo> <mn>0.0001</mn> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>4</mn> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <msub> <mi>η</mi> <mi>a</mi> </msub> <mo>=</mo> <mn>1</mn> </mrow> </semantics> </math> and <math display="inline"> <semantics> <msub> <mi>P</mi> <mrow> <mi>e</mi> <mi>t</mi> </mrow> </msub> </semantics> </math> = 30 dBm.</p>
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<p>Spatial mean harvestable energy for LSWN-noIC with <math display="inline"> <semantics> <mrow> <msub> <mi>λ</mi> <mi>t</mi> </msub> <mo>=</mo> <mn>0.1</mn> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>4</mn> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <msub> <mi>η</mi> <mi>a</mi> </msub> <mo>=</mo> <mn>1</mn> </mrow> </semantics> </math> and <math display="inline"> <semantics> <msub> <mi>P</mi> <mrow> <mi>e</mi> <mi>t</mi> </mrow> </msub> </semantics> </math> = 30 dBm.</p>
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<p>Spatial mean harvestable energy for LSWN-noIC with <math display="inline"> <semantics> <mrow> <msub> <mi>λ</mi> <mi>t</mi> </msub> <mo>=</mo> <mn>0.0001</mn> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>4</mn> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <msub> <mi>η</mi> <mi>a</mi> </msub> <mo>=</mo> <mn>1</mn> </mrow> </semantics> </math> and <math display="inline"> <semantics> <msub> <mi>P</mi> <mrow> <mi>e</mi> <mi>t</mi> </mrow> </msub> </semantics> </math> = 30 dBm.</p>
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<p>Effective energy harvesting probability versus transmitter density with <math display="inline"> <semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>4</mn> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <mi>d</mi> <mo>=</mo> <mn>2</mn> </mrow> </semantics> </math> and <math display="inline"> <semantics> <msub> <mi>P</mi> <mrow> <mi>e</mi> <mi>t</mi> </mrow> </msub> </semantics> </math> = 30 dBm.</p>
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<p>Effective energy harvesting probability versus transmitting power with <math display="inline"> <semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>4</mn> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <mi>d</mi> <mo>=</mo> <mn>2</mn> </mrow> </semantics> </math> and <math display="inline"> <semantics> <mrow> <msub> <mi>λ</mi> <mi>t</mi> </msub> <mo>=</mo> <mn>0.001</mn> </mrow> </semantics> </math>.</p>
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<p>Spatial mean harvestable energy versus transmitter density with <math display="inline"> <semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>4</mn> <mo>,</mo> <mi>d</mi> <mo>=</mo> <mn>2</mn> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <msub> <mi>η</mi> <mi>a</mi> </msub> <mo>=</mo> <mn>1</mn> </mrow> </semantics> </math> and <math display="inline"> <semantics> <msub> <mi>P</mi> <mrow> <mi>e</mi> <mi>t</mi> </mrow> </msub> </semantics> </math> = 30 dBm.</p>
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<p>Spatial mean harvestable energy versus transmitter density with <math display="inline"> <semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>4</mn> <mo>,</mo> <mi>d</mi> <mo>=</mo> <mn>2</mn> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <msub> <mi>η</mi> <mi>a</mi> </msub> <mo>=</mo> <mn>1</mn> </mrow> </semantics> </math> and <math display="inline"> <semantics> <mrow> <msub> <mi>λ</mi> <mi>t</mi> </msub> <mo>=</mo> <mn>0.01</mn> </mrow> </semantics> </math>.</p>
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15 pages, 2222 KiB  
Article
Building Automation and Control Systems and Electrical Distribution Grids: A Study on the Effects of Loads Control Logics on Power Losses and Peaks
by Salvatore Favuzza, Mariano Giuseppe Ippolito, Fabio Massaro, Rossano Musca, Eleonora Riva Sanseverino, Giuseppe Schillaci and Gaetano Zizzo
Energies 2018, 11(3), 667; https://doi.org/10.3390/en11030667 - 15 Mar 2018
Cited by 11 | Viewed by 4262
Abstract
Growing home comfort is causing increasing energy consumption in residential buildings and a consequent stress in urban medium and low voltage distribution networks. Therefore, distribution system operators are obliged to manage problems related to the reliability of the electricity system and, above all, [...] Read more.
Growing home comfort is causing increasing energy consumption in residential buildings and a consequent stress in urban medium and low voltage distribution networks. Therefore, distribution system operators are obliged to manage problems related to the reliability of the electricity system and, above all, they must consider investments for enhancing the electrical infrastructure. The purpose of this paper is to assess how the reduction of building electricity consumption and the modification of the building load profile, due to load automation, combined with suitable load control programs, can improve network reliability and distribution efficiency. This paper proposes an extensive study on this issue, considering various operating scenarios with four load control programs with different purposes, the presence/absence of local generation connected to the buildings and different external thermal conditions. The study also highlights how different climatic conditions can influence the effects of the load control logics. Full article
(This article belongs to the Section F: Electrical Engineering)
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<p>Daily power profiles for an apartment, modified by the action of the control logics.</p>
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<p>Test-Network.</p>
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<p>Turin: daily power profile at a primary substation for Scenarios S.0 to S.4.</p>
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<p>Rome: daily power profile at a primary substation for Scenarios S.0 to S.4.</p>
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<p>Rome: daily power profile at a primary substation for Scenarios S.0 to S.4.</p>
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<p>Palermo: daily power profile at a primary substation for Scenarios S.0 to S.4.</p>
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22 pages, 4490 KiB  
Article
Research on Optimal Wind Power Penetration Ratio and the Effects of a Wind-Thermal-Bundled System under the Constraint of Rotor Angle Transient Stability
by Ming Ding, Yan Zhang, Pingping Han, Yuying Bao and Haitian Zhang
Energies 2018, 11(3), 666; https://doi.org/10.3390/en11030666 - 15 Mar 2018
Cited by 6 | Viewed by 3562
Abstract
Large-scale wind-thermal-bundled power that is transmitted by mixed ultra-high voltage direct current (UHVDC)/ultra-high voltage alternating current (UHVAC) systems has become crucial for large-scale wind farms in China. Equations describing the electromagnetic power characteristics under short circuits for UHVAC lines and UHVDC blocks are [...] Read more.
Large-scale wind-thermal-bundled power that is transmitted by mixed ultra-high voltage direct current (UHVDC)/ultra-high voltage alternating current (UHVAC) systems has become crucial for large-scale wind farms in China. Equations describing the electromagnetic power characteristics under short circuits for UHVAC lines and UHVDC blocks are derived based on an analysis of the external characteristics of a doubly fed wind farm and UHVDC systems. The effect of wind power penetration ratio on rotor angle transient stability is analysed, and the optimal wind power penetration ratio under the constraint of rotor angle transient stability is determined. The effects of system parameters, such as the UHVDC transmission capacity and the reactance of UHVAC lines on the optimal wind power penetration ratio are discussed. The trend of rotor angle stability varies from a monotonic deterioration to concave, and the optimal wind power penetration ratio increases from 0 to 30% under an UHVDC block when the reactance of UHVAC lines increases from 0.005 to 0.02. The optimal wind power penetration ratio under a short circuit increases from 40% to 60% when the reactance of UHVAC lines decreases from 0.02 to 0.006 and decreases from 40% to 30% when the capacity of UHVDC decreases from 3200 MW to 1600 MW. The analysis is verified by simulating an actual system in China’s Northwest Power Grid. Full article
(This article belongs to the Section F: Electrical Engineering)
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<p>Diagram of the single-ended power transmission system: (<b>a</b>) Structural schematic diagram; and (<b>b</b>) Equivalent circuit diagram.</p>
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<p>Diagram of a typical wind-thermal-bundled system during normal operation: (<b>a</b>) Structural schematic diagram; and (<b>b</b>) Equivalent circuit diagram.</p>
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<p>Transient response characteristics of a wind farm: (<b>a</b>) Voltage of wind farm integration point; and, (<b>b</b>) Active and reactive power of wind farm.</p>
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<p>Transient response characteristics of ultra-high voltage alternatingcurrent (UHVDC): (<b>a</b>) <span class="html-italic">U<sub>D</sub></span> and <span class="html-italic">P<sub>D</sub></span>; (<b>b</b>) Reactive power exchange between rectifier and system.</p>
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<p>Electromagnetic power characteristics of thermal power units at different wind power penetration ratios.</p>
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<p>Equivalent circuit diagram of a typical wind-thermal-bundled system after an UHVDC block.</p>
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<p>Simulation results of a typical wind-thermal-bundled system under an UHVDC block: (<b>a</b>) <span class="html-italic">U<sub>D</sub></span> and active power of UHVAC lines; (<b>b</b>) <span class="html-italic">P<sub>w</sub></span> and <span class="html-italic">Q<sub>w</sub></span>.</p>
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<p>Influence of wind power penetration ratio on acceleration and deceleration area after an UHVDC block.</p>
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<p>Equivalent circuit diagram of a typical wind-thermal-bundled system: (<b>a</b>) During a short circuit; and (<b>b</b>) After a short circuit.</p>
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<p>Influence of wind power penetration ratio on acceleration and deceleration area after a short circuit of UHVAC lines: (<b>a</b>) 0 <span class="html-italic">&lt; k ≤ n</span>; (<b>b</b>) <span class="html-italic">n ≤ k &lt;</span> 1.</p>
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<p>Curve of rotor angle under an UHVDC block of a typical wind-thermal-bundled system when <span class="html-italic">P<sub>D</sub></span> is 3200 MW and <span class="html-italic">x<sub>L</sub></span> is 0.02: (<b>a</b>) <span class="html-italic">k ≤</span> 30%; (<b>b</b>) <span class="html-italic">k ≥</span> 30%.</p>
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<p>Maximum rotor angle difference of a typical wind-thermal-bundled system under an UHVDC block when <span class="html-italic">P<sub>D</sub></span> is 3200 MW and <span class="html-italic">x<sub>L</sub></span> is 0.02.</p>
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<p>Simulation results under an UHVDC block of a typical wind-thermal-bundled system when <span class="html-italic">P<sub>D</sub></span> is 3200 MW and <span class="html-italic">x<sub>L</sub></span> is 0.005: (<b>a</b>) Curves of rotor angle; (<b>b</b>) Maximum rotor angle difference.</p>
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<p>Maximum rotor angle difference of a typical wind-thermal-bundled system under an UHVDC block when <span class="html-italic">P<sub>D</sub></span> is 2400 MW and <span class="html-italic">x<sub>L</sub></span> is 0.02.</p>
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<p>Curve of rotor angle under a short-circuit of a typical wind-thermal-bundled system when <span class="html-italic">P<sub>D</sub></span> is 3200 MW and <span class="html-italic">x<sub>L</sub></span> is 0.02: (<b>a</b>) <span class="html-italic">k ≤</span> 40%; and (<b>b</b>) <span class="html-italic">k ≥</span> 40%.</p>
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<p>Structural schematic diagram of an actual wind-thermal-bundled system in China’s Northwest Power Grid.</p>
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<p>Simulation results under an UHVDC monopolar block of an actual wind-thermal-bundled system: (<b>a</b>) Curve of rotor angle; and (<b>b</b>) Maximum rotor angle difference.</p>
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<p>Simulation results under an UHVDC block of an actual system when <span class="html-italic">x<sub>L</sub></span> is decreased by (thyristor-controlled series compensation) TCSC: (<b>a</b>) Curve of rotor angle; and (<b>b</b>) Maximum rotor angle difference.</p>
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<p>Curve of rotor angle under a short circuit of an actual wind-thermal-bundled system.</p>
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<p>The structure diagram of the control system of CIGRE HVDC test model.</p>
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24 pages, 6515 KiB  
Article
A New Analytical Wake Model for Yawed Wind Turbines
by Guo-Wei Qian and Takeshi Ishihara
Energies 2018, 11(3), 665; https://doi.org/10.3390/en11030665 - 15 Mar 2018
Cited by 105 | Viewed by 10170
Abstract
A new analytical wake model for wind turbines, considering ambient turbulence intensity, thrust coefficient and yaw angle effects, is proposed from numerical and analytical studies. First, eight simulations by the Reynolds Stress Model are conducted for different thrust coefficients, yaw angles and ambient [...] Read more.
A new analytical wake model for wind turbines, considering ambient turbulence intensity, thrust coefficient and yaw angle effects, is proposed from numerical and analytical studies. First, eight simulations by the Reynolds Stress Model are conducted for different thrust coefficients, yaw angles and ambient turbulence intensities. The wake deflection, mean velocity and turbulence intensity in the wakes are systematically investigated. A new wake deflection model is then proposed to analytically predict the wake center trajectory in the yawed condition. Finally, the effects of yaw angle are incorporated in the Gaussian-based wake model. The wake deflection, velocity deficit and added turbulence intensity in the wake predicted by the proposed model show good agreement with the numerical results. The model parameters are determined as the function of ambient turbulence intensity and thrust coefficient, which enables the model to have good applicability under various conditions. Full article
(This article belongs to the Collection Wind Turbines)
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<p>Schematic of the ADM-R model for the yawed rotor: (<b>a</b>) isometric view, (<b>b</b>) top view.</p>
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<p>Velocities and forces acting on a cross-sectional blade element.</p>
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<p>Schematic view of the computational domain.</p>
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<p>Vertical profiles in the simulated neutral atmospheric boundary layers without wind turbines: (<b>a</b>,<b>c</b>) for normalized mean velocity; (<b>b</b>,<b>d</b>) for turbulence intensity.</p>
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<p>Wake characteristics in the horizontal <span class="html-italic">x</span>-<span class="html-italic">y</span> plane at hub height under non-yawed conditions: (<b>a</b>,<b>c</b>) for normalized mean velocity; (<b>b</b>,<b>d</b>) for turbulence intensity.</p>
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<p>Contours of normalized mean velocity <math display="inline"> <semantics> <mrow> <mi>U</mi> <mo>/</mo> <msub> <mi>U</mi> <mi>h</mi> </msub> </mrow> </semantics> </math> and wake deflections in the horizontal <span class="html-italic">x</span>-<span class="html-italic">y</span> plane at the hub height. Solid lines represent the wind turbine rotors. Dashed lines and open circles indicate the wake boundaries and wake center trajectories, respectively.</p>
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<p>Contours of turbulence intensity and wake deflections in the horizontal <span class="html-italic">x</span>-<span class="html-italic">y</span> plane at the hub height. Solid lines represent the wind turbine rotors. Dashed lines denote the position of peak values of turbulence intensity and the midpoint of the two peaks are indicated by the open circles. The wake center trajectories obtained from the mean velocity contours are plotted by red dotted lines.</p>
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<p>Schematic of the momentum conservation-based model for the wake deflection. Black dotted lines downstream the turbine represent the wake boundaries and the part overlapped by the red dashed lines are used to establish the control volume.</p>
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<p>Comparison between wake deflection models and the experiment results.</p>
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<p>Schematic of the Gaussian-based wake model in yawed condition: (<b>a</b>) mean velocity (<b>b</b>) turbulence intensity.</p>
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<p>Validation for predicted wake deflections in yawed conditions.</p>
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<p>Validation for the predicted mean velocity under the yawed conditions.</p>
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<p>Validation for the predicted turbulence intensity in the yawed conditions.</p>
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30 pages, 899 KiB  
Review
Algal Biomass from Wastewater and Flue Gases as a Source of Bioenergy
by Sandra Lage, Zivan Gojkovic, Christiane Funk and Francesco G. Gentili
Energies 2018, 11(3), 664; https://doi.org/10.3390/en11030664 - 15 Mar 2018
Cited by 74 | Viewed by 9658
Abstract
Algae are without doubt the most productive photosynthetic organisms on Earth; they are highly efficient in converting CO2 and nutrients into biomass. These abilities can be exploited by culturing microalgae from wastewater and flue gases for effective wastewater reclamation. Algae are known [...] Read more.
Algae are without doubt the most productive photosynthetic organisms on Earth; they are highly efficient in converting CO2 and nutrients into biomass. These abilities can be exploited by culturing microalgae from wastewater and flue gases for effective wastewater reclamation. Algae are known to remove nitrogen and phosphorus as well as several organic contaminants including pharmaceuticals from wastewater. Biomass production can even be enhanced by the addition of CO2 originating from flue gases. The algal biomass can then be used as a raw material to produce bioenergy; depending on its composition, various types of biofuels such as biodiesel, biogas, bioethanol, biobutanol or biohydrogen can be obtained. However, algal biomass generated in wastewater and flue gases also contains contaminants which, if not degraded, will end up in the ashes. In this review, the current knowledge on algal biomass production in wastewater and flue gases is summarized; special focus is given to the algal capacity to remove contaminants from wastewater and flue gases, and the consequences when converting this biomass into different types of biofuels. Full article
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<p>Algal biomass conversion pathways (modified from [<a href="#B84-energies-11-00664" class="html-bibr">84</a>]).</p>
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25 pages, 5550 KiB  
Article
A New Exact Mathematical Approach for Studying Bifurcation in DCM Operated dc-dc Switching Converters
by Mircea Gurbina, Aurel Ciresan, Dan Lascu, Septimiu Lica and Ioana-Monica Pop-Calimanu
Energies 2018, 11(3), 663; https://doi.org/10.3390/en11030663 - 15 Mar 2018
Cited by 5 | Viewed by 6778
Abstract
A bifurcation study for dc-dc converters operated in DCM is performed using an accurate method. When applying classical techniques significant difficulties are encountered in the calculations. For example, using the averaging method the validity of the result is limited to half the switching [...] Read more.
A bifurcation study for dc-dc converters operated in DCM is performed using an accurate method. When applying classical techniques significant difficulties are encountered in the calculations. For example, using the averaging method the validity of the result is limited to half the switching frequency and higher order effects are neglected Another approach is to perform a Taylor expansion of the state transition matrices. However, this is somehow also an averaging but the fact that the Taylor series is truncated leads to unacceptable inaccuracy. A new mathematical technique for discontinuous conduction mode (DCM) analysis of dc-dc switching converters is proposed in order to predict bifurcation and chaos. The proposed technique is based on exact calculation of the state transition matrices and of the Jacobian thus providing higher accuracy of the results compared to other previously reported approaches. Beside the fact the new technique allows for exact diagnosis of instability, it is also highly general, in the sense that it can be applied to any dc-dc DCM operated converter employing any type of control. The good agreement between theoretical, simulation and experimental results, with an error lower than 0.94%, confirms the validity of the proposed method. Full article
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<p>Transistor switching function, inductor voltage and inductor current in a DCM operated dc-dc converter.</p>
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<p>Proportional voltage-mode controlled boost converter.</p>
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<p>Flow chart of the Matlab™ program for determining the critical bifurcation parameter value.</p>
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<p>Caspoc™ schematic for the proportional voltage-mode controlled boost converter.</p>
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<p>The simulated bifurcation diagram of the boost DCM converter employing proportional voltage-mode control.</p>
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<p>Magnified image around the bifurcation point. Clearly bifurcation appears at <span class="html-italic">k</span> = 1.159. The table below provides the instantaneous values of the simulated waveforms, together with their minimum and maximum values.</p>
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<p>(<b>a</b>) The inductor current waveform for <span class="html-italic">k</span> = 1.1. The horizontal dotted line denotes the zero level and the solid line the inductor current. (<b>b</b>) Phase portrait for stable operation, <span class="html-italic">k</span> = 1.1. (<b>c</b>) Inductor current for parameter <span class="html-italic">k</span> = 1.2, higher than bifurcation threshold value of 1.1589. Unstable operation with period doubling (period 2 subharmonic) is obvious.</p>
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<p>(<b>a</b>) The inductor current waveform for <span class="html-italic">k</span> = 1.1. The horizontal dotted line denotes the zero level and the solid line the inductor current. (<b>b</b>) Phase portrait for stable operation, <span class="html-italic">k</span> = 1.1. (<b>c</b>) Inductor current for parameter <span class="html-italic">k</span> = 1.2, higher than bifurcation threshold value of 1.1589. Unstable operation with period doubling (period 2 subharmonic) is obvious.</p>
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<p>Phase portrait for <span class="html-italic">k</span> = 1.2, higer than 1.1589, confirming period doubling.</p>
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<p>Schematic of the experimental boost DCM converter employing proportional voltage-mode control.</p>
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<p>Set signal and reset signals of IC<span class="html-italic">4</span>, inductor current and output voltage waveform (this up to down order) for <span class="html-italic">k</span> = 1.08. Stable operation can be remarked.</p>
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<p>Phase portrait for <span class="html-italic">k</span> = 1.08, confirming stable operation.</p>
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<p>Inductor current and output voltage waveforms for <span class="html-italic">k</span> = 1.17. Notice that the peak inductor current values start to slightly differ in two consecutive periods and the period of the inductor current doubles compared to stable operation.</p>
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<p>Inductor current and output voltage waveforms for <span class="html-italic">k</span> = 1.20. Bifurcation with period 2 operation is evident.</p>
Full article ">Figure 14
<p>Phase portrait for <span class="html-italic">k</span> = 1.20, clearly revealing period 2 operation.</p>
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<p>Chaotic operation of the converter corresponding to <span class="html-italic">k</span> = 1.50.</p>
Full article ">Figure 16
<p>The simulated bifurcation diagram of the boost DCM converter: <span class="html-italic">V<sub>out</sub> = f</span>(<span class="html-italic">V<sub>g</sub></span>).</p>
Full article ">Figure 17
<p>Phase portrait for <span class="html-italic">V<sub>g</sub> =</span> 16.50 V, confirming stable operation.</p>
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<p>Phase portrait for <span class="html-italic">V<sub>g</sub></span> = 17.20, clearly revealing period 2 operation.</p>
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<p>Chaotic operation of the converter corresponding to <span class="html-italic">V<sub>g</sub> =</span> 17.80 V.</p>
Full article ">
31 pages, 14908 KiB  
Article
On-Line Detection of Coil Inter-Turn Short Circuit Faults in Dual-Redundancy Permanent Magnet Synchronous Motors
by Yiguang Chen, Xuemin Chen and Yonghuan Shen
Energies 2018, 11(3), 662; https://doi.org/10.3390/en11030662 - 15 Mar 2018
Cited by 12 | Viewed by 5024
Abstract
In the aerospace and military fields, with high reliability requirements, the dual-redundancy permanent magnet synchronous motor (DRPMSM) with weak thermal coupling and no electromagnetic coupling is needed. A common fault in the DRPMSM is the inter-turn short circuit fault (ISCF). However, research on [...] Read more.
In the aerospace and military fields, with high reliability requirements, the dual-redundancy permanent magnet synchronous motor (DRPMSM) with weak thermal coupling and no electromagnetic coupling is needed. A common fault in the DRPMSM is the inter-turn short circuit fault (ISCF). However, research on how to diagnose ISCF and the set of faulty windings in the DRPMSM is lacking. In this paper, the structure of the DRPMSM is analyzed and mathematical models of the motor under normal and faulty conditions are established. Then an on-line ISCF detection scheme, which depends on the running modes of the DRPMSM and the average values for the difference of the d-axis voltages between two sets of windings in the latest 20 sampling periods, is proposed. The main contributions of this paper are to analyze the calculation for the inductance of each part of the stator windings and propose the on-line diagnosis method of the ISCF under various operating conditions. The simulation and experimental results show that the proposed method can quickly and effectively diagnose ISCF and determine the set of faulty windings of the DRPMSM. Full article
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Figure 1

Figure 1
<p>The cross-sectional view of the DRPMSM.</p>
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<p>The star graph of the fundamental EMF and the diagram of phase separation: (<b>a</b>) The star graph of the fundamental EMF; (<b>b</b>) The diagram of phase separation.</p>
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<p>Stator windings outspread diagram of the DRPMSM.</p>
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<p>The control system block diagram of the DRPMSM.</p>
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<p>The MMF of the C2 phase winding under normal conditions: (<b>a</b>) The stator winding outspread diagram; (<b>b</b>) The distribution diagram of the MMF generated by <math display="inline"> <semantics> <mrow> <msub> <mi>i</mi> <mrow> <mi mathvariant="normal">C</mi> <mn>2</mn> </mrow> </msub> </mrow> </semantics> </math>.</p>
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<p>The MMF of the C2 phase winding with ISCF: (<b>a</b>) The stator winding outspread diagram of the DRPMSM; (<b>b</b>) The distribution diagram of the MMF generated by <math display="inline"> <semantics> <mrow> <msub> <mi>i</mi> <mi mathvariant="normal">s</mi> </msub> </mrow> </semantics> </math>; (<b>c</b>) The distribution diagram of the MMF generated by <math display="inline"> <semantics> <mrow> <msub> <mi>i</mi> <mrow> <mi mathvariant="normal">C</mi> <mn>2</mn> </mrow> </msub> </mrow> </semantics> </math>.</p>
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<p>The shape of the stator slots.</p>
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<p>The equivalent circuit diagram of the second set of windings.</p>
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<p>The simulation models of an ISCF in the DRPMSM: (<b>a</b>) The simulation model of the overall control system for the motor; (<b>b</b>) The sub-model of the vector control system; (<b>c</b>) The sub-model of the DRPMSM; (<b>d</b>) The sub-model of the set of faulty windings.</p>
Full article ">Figure 9 Cont.
<p>The simulation models of an ISCF in the DRPMSM: (<b>a</b>) The simulation model of the overall control system for the motor; (<b>b</b>) The sub-model of the vector control system; (<b>c</b>) The sub-model of the DRPMSM; (<b>d</b>) The sub-model of the set of faulty windings.</p>
Full article ">Figure 9 Cont.
<p>The simulation models of an ISCF in the DRPMSM: (<b>a</b>) The simulation model of the overall control system for the motor; (<b>b</b>) The sub-model of the vector control system; (<b>c</b>) The sub-model of the DRPMSM; (<b>d</b>) The sub-model of the set of faulty windings.</p>
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<p>The simulation results (rated load, <math display="inline"> <semantics> <mrow> <msup> <mi>n</mi> <mo>∗</mo> </msup> <mo>=</mo> <mn>900</mn> <mtext> </mtext> <mi mathvariant="normal">r</mi> <mo>/</mo> <mi>min</mi> </mrow> </semantics> </math>): (<b>a</b>) The three phase currents of two sets of windings; (<b>b</b>) The electromagnetic torque; (<b>c</b>) The <span class="html-italic">d</span>-axis voltages of two sets of windings; (<b>d</b>) The difference of the <span class="html-italic">d</span>-axis voltages between the set of faulty windings and the set of normal windings; (<b>e</b>) The average values for the difference of the <span class="html-italic">d</span>-axis voltages in the latest 20 sampling periods.</p>
Full article ">Figure 10 Cont.
<p>The simulation results (rated load, <math display="inline"> <semantics> <mrow> <msup> <mi>n</mi> <mo>∗</mo> </msup> <mo>=</mo> <mn>900</mn> <mtext> </mtext> <mi mathvariant="normal">r</mi> <mo>/</mo> <mi>min</mi> </mrow> </semantics> </math>): (<b>a</b>) The three phase currents of two sets of windings; (<b>b</b>) The electromagnetic torque; (<b>c</b>) The <span class="html-italic">d</span>-axis voltages of two sets of windings; (<b>d</b>) The difference of the <span class="html-italic">d</span>-axis voltages between the set of faulty windings and the set of normal windings; (<b>e</b>) The average values for the difference of the <span class="html-italic">d</span>-axis voltages in the latest 20 sampling periods.</p>
Full article ">Figure 11
<p>The simulation results with a coil ISCF under various operation conditions: (<b>a</b>) The reference speed and the actual speed; (<b>b</b>) The reference load torque and the electromagnetic torque; (<b>c</b>) The <span class="html-italic">d</span>-axis voltages of two sets of windings; (<b>d</b>) <math display="inline"> <semantics> <mrow> <mo>Δ</mo> <msub> <mi>u</mi> <mi>d</mi> </msub> </mrow> </semantics> </math>; (<b>e</b>) <math display="inline"> <semantics> <mrow> <mo>Δ</mo> <msubsup> <mi>u</mi> <mrow> <mi>d</mi> <mi>av</mi> </mrow> <mrow> <mn>20</mn> </mrow> </msubsup> </mrow> </semantics> </math>.</p>
Full article ">Figure 11 Cont.
<p>The simulation results with a coil ISCF under various operation conditions: (<b>a</b>) The reference speed and the actual speed; (<b>b</b>) The reference load torque and the electromagnetic torque; (<b>c</b>) The <span class="html-italic">d</span>-axis voltages of two sets of windings; (<b>d</b>) <math display="inline"> <semantics> <mrow> <mo>Δ</mo> <msub> <mi>u</mi> <mi>d</mi> </msub> </mrow> </semantics> </math>; (<b>e</b>) <math display="inline"> <semantics> <mrow> <mo>Δ</mo> <msubsup> <mi>u</mi> <mrow> <mi>d</mi> <mi>av</mi> </mrow> <mrow> <mn>20</mn> </mrow> </msubsup> </mrow> </semantics> </math>.</p>
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<p>The variation diagram of <math display="inline"> <semantics> <mrow> <mo>Δ</mo> <msub> <mi>u</mi> <mrow> <mi>d</mi> <mi>av</mi> </mrow> </msub> </mrow> </semantics> </math> with the percentage for the number of short-circuited turns and the electromagnetic torque.</p>
Full article ">Figure 13
<p>The variation diagram of <math display="inline"> <semantics> <mrow> <mo>Δ</mo> <msub> <mi>u</mi> <mrow> <mi>d</mi> <mi>av</mi> </mrow> </msub> </mrow> </semantics> </math> with the electromagnetic torque and the speed.</p>
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<p>The variation diagram of <math display="inline"> <semantics> <mrow> <mo>Δ</mo> <msub> <mi>u</mi> <mrow> <mi>d</mi> <mi>av</mi> </mrow> </msub> </mrow> </semantics> </math> with the percentage for the number of short-circuited turns and the speed.</p>
Full article ">Figure 15
<p>The variation diagram of <math display="inline"> <semantics> <mrow> <mo>Δ</mo> <msub> <mi>u</mi> <mrow> <mi>d</mi> <mi>av</mi> </mrow> </msub> </mrow> </semantics> </math> with the external contact resistance and the percentage for the number of short-circuited turns.</p>
Full article ">Figure 16
<p>The variation diagram of <math display="inline"> <semantics> <mrow> <mo>Δ</mo> <msub> <mi>u</mi> <mrow> <mi>d</mi> <mi>av</mi> </mrow> </msub> </mrow> </semantics> </math> with the external contact resistance and the electromagnetic torque.</p>
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<p>The logic block diagram of the fault diagnosis and redundancy controller.</p>
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<p>The experimental system diagram of the DRPMSM.</p>
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<p>The current waveforms of the second set of windings: (<b>a</b>) Three phase currents under normal conditions; (<b>b</b>) Three phase currents under faulty conditions.</p>
Full article ">Figure 19 Cont.
<p>The current waveforms of the second set of windings: (<b>a</b>) Three phase currents under normal conditions; (<b>b</b>) Three phase currents under faulty conditions.</p>
Full article ">Figure 20
<p>The waveforms about the <span class="html-italic">d</span>-axis voltage under normal conditions: (<b>a</b>) The <span class="html-italic">d</span>-axis voltages of two sets of windings; (<b>b</b>) <math display="inline"> <semantics> <mrow> <mo>Δ</mo> <msub> <mi>u</mi> <mi>d</mi> </msub> </mrow> </semantics> </math>; (<b>c</b>) <math display="inline"> <semantics> <mrow> <mo>Δ</mo> <msubsup> <mi>u</mi> <mrow> <mi>d</mi> <mi>av</mi> </mrow> <mrow> <mn>20</mn> </mrow> </msubsup> </mrow> </semantics> </math>.</p>
Full article ">Figure 20 Cont.
<p>The waveforms about the <span class="html-italic">d</span>-axis voltage under normal conditions: (<b>a</b>) The <span class="html-italic">d</span>-axis voltages of two sets of windings; (<b>b</b>) <math display="inline"> <semantics> <mrow> <mo>Δ</mo> <msub> <mi>u</mi> <mi>d</mi> </msub> </mrow> </semantics> </math>; (<b>c</b>) <math display="inline"> <semantics> <mrow> <mo>Δ</mo> <msubsup> <mi>u</mi> <mrow> <mi>d</mi> <mi>av</mi> </mrow> <mrow> <mn>20</mn> </mrow> </msubsup> </mrow> </semantics> </math>.</p>
Full article ">Figure 21
<p>The waveforms about the <span class="html-italic">d</span>-axis voltage under faulty conditions: (<b>a</b>) The <span class="html-italic">d</span>-axis voltages of two sets of windings; (<b>b</b>) <math display="inline"> <semantics> <mrow> <mo>Δ</mo> <msub> <mi>u</mi> <mi>d</mi> </msub> </mrow> </semantics> </math>; (<b>c</b>) <math display="inline"> <semantics> <mrow> <mo>Δ</mo> <msubsup> <mi>u</mi> <mrow> <mi>d</mi> <mi>av</mi> </mrow> <mrow> <mn>20</mn> </mrow> </msubsup> </mrow> </semantics> </math>.</p>
Full article ">Figure 22
<p>The variation curves of <math display="inline"> <semantics> <mrow> <mo>Δ</mo> <msub> <mi>u</mi> <mrow> <mi>d</mi> <mi>av</mi> </mrow> </msub> </mrow> </semantics> </math> with the number of short-circuited turns and the electromagnetic torque.</p>
Full article ">Figure 23
<p>The variation curves of <math display="inline"> <semantics> <mrow> <mo>Δ</mo> <msub> <mi>u</mi> <mrow> <mi>d</mi> <mi>av</mi> </mrow> </msub> </mrow> </semantics> </math> with the speed and the electromagnetic torque.</p>
Full article ">Figure 24
<p>The variation curves of <math display="inline"> <semantics> <mrow> <mo>Δ</mo> <msub> <mi>u</mi> <mrow> <mi>d</mi> <mi>av</mi> </mrow> </msub> </mrow> </semantics> </math> with the speed and the number of short-circuited turns.</p>
Full article ">Figure 25
<p>The variation curves of <math display="inline"> <semantics> <mrow> <mo>Δ</mo> <msub> <mi>u</mi> <mrow> <mi>d</mi> <mi>av</mi> </mrow> </msub> </mrow> </semantics> </math> with the number of short-circuited turns and the external contact resistance.</p>
Full article ">Figure 26
<p>The variation curves of <math display="inline"> <semantics> <mrow> <mo>Δ</mo> <msub> <mi>u</mi> <mrow> <mi>d</mi> <mi>av</mi> </mrow> </msub> </mrow> </semantics> </math> with the electromagnetic torque and the external contact resistance.</p>
Full article ">
22 pages, 18250 KiB  
Article
Visual Investigation of the Occurrence Characteristics of Multi-Type Formation Water in a Fracture–Cavity Carbonate Gas Reservoir
by Lu Wang, Shenglai Yang, Xian Peng, Hui Deng, Yi Liao, Yicheng Liu, Wei Xu and Youjun Yan
Energies 2018, 11(3), 661; https://doi.org/10.3390/en11030661 - 15 Mar 2018
Cited by 24 | Viewed by 4286
Abstract
It is difficult to investigate the formation process and occurrence states of water in multi-type reservoirs, due to the strong heterogeneity and complex microstructure of the fracture–cavity carbonate gas reservoirs. To date, there is no systematic study on the occurrence characteristics of multi-type [...] Read more.
It is difficult to investigate the formation process and occurrence states of water in multi-type reservoirs, due to the strong heterogeneity and complex microstructure of the fracture–cavity carbonate gas reservoirs. To date, there is no systematic study on the occurrence characteristics of multi-type formation water, especially through visual observation experiments. In this paper, a new creation method for visual micromodels based on CT scan images and microelectronic photolithography techniques was described. Subsequently, a gas–drive–water visual experiment was conducted to intuitively study the formation mechanism and the occurrence states of formation water. Then, the ImageJ gray analysis method was utilized to quantitatively investigate the gas-water saturation and the proportion of residual water film. Finally, the occurrence characteristics of formation water and its effects on gas seepage flow were comprehensively analyzed. Visual experimental results showed that: the migration processes of natural gas in different types of reservoirs are different; the water in multiple media consists of native movable water and residual water, and residual water is composed of secondary movable water and irreducible water; the residual water mainly occurs in different locations of different reservoirs with the forms of “water film”, “water mass”, “water column” and “water droplets”; the main influencing factors are capillary force, surface tension, displacement pressure and channel connectivity. Quantitative results reflect that the saturation of movable water and residual water are the parameters related directly to reservoir physical properties, pore structure and displacement pressure—the smaller the size of flow channel, the larger the space occupied by water film; the thickness proportion of water film is increasing exponentially with the channel size; the thickness proportion of water film decreases as the increase of displacement pressure; the thickness proportion of water film is essentially constant when the displacement pressure increases to a certain extent. The conducted visual investigation not only improves our intuitive understanding of the occurrence characteristics of formation water, but also provides a theoretical basis for the efficient development of fracture-cavity gas reservoirs. Full article
(This article belongs to the Section L: Energy Sources)
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Figure 1
<p>Photographs of cores. (<b>a</b>) 201400270147; (<b>b</b>) 201400830059; (<b>c</b>) 201400830021.</p>
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<p>Optimized CT scan images: (<b>a</b>) Fracture-type; (<b>b</b>) Cavity-type; (<b>c</b>) Fracture–cavity-type.</p>
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<p>Schematic of the mask frame and typical flow direction.</p>
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<p>Base images of three type cores based on CT scan images: (<b>a</b>) Fracture-type; (<b>b</b>) Cavity-type; (<b>c</b>) Fracture–cavity-type.</p>
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<p>Visual micromodel fabrication process: (<b>a</b>) pretreatment (pre-baking and cleaning); (<b>b</b>) priming HMDS; (<b>c</b>) photoresist spin coating; (<b>d</b>) DUV exposure; (<b>e</b>) developing; (<b>f</b>) etching by HF acid gases; (<b>g</b>) cleaning by piranha solution; (<b>h</b>) bounding; and (<b>i</b>) heating.</p>
Full article ">Figure 6
<p>Fabricated visual micromodels based on the microelectronic photolithography technique: (<b>a</b>) Fracture-type; (<b>b</b>) Cavity-type; (<b>c</b>) Fracture–cavity-type.</p>
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<p>Schematic diagram of the two-dimensional visual simulation experiment.</p>
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<p>Appearance of the two-dimensional visual micromodel holder: (<b>a</b>) Overall structure; (<b>b</b>) Top structure.</p>
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<p>Schematic of quantitative characterization of images based on ImageJ gray analysis. (<b>a</b>) Gas-water distribution image after pretreatment; (<b>b</b>) Area occupied by voids; (<b>c</b>) Area occupied by gas.</p>
Full article ">Figure 10
<p>Gas-drive-water process of the fracture-type micromodel: (<b>a</b>) Initial stage; (<b>b</b>) Second stage; (<b>c</b>) Third stage; (<b>d</b>) Final stage.</p>
Full article ">Figure 11
<p>Gas-drive-water process of the cavity-type micromodel: (<b>a</b>) Initial stage; (<b>b</b>) Second stage; (<b>c</b>) Third stage; (<b>d</b>) Final stage.</p>
Full article ">Figure 12
<p>Gas-drive-water process of the fracture–cavity-type micromodel: (<b>a</b>) Initial stage; (<b>b</b>) Second stage; (<b>c</b>) Third stage; (<b>d</b>) Final stage.</p>
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<p>Classification of formation water in fracture-cavity carbonate reservoirs.</p>
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<p>Occurrence states of residual water after gas-drive-water visual experiments: (<b>a</b>) Residual water film on the fracture walls; (<b>b</b>) Residual water mass at the bottom of the cavity; (<b>c</b>) Residual water column formed by cutoff; (<b>d</b>) Residual water droplet at the dead end and blind corner.</p>
Full article ">Figure 15
<p>Water saturation versus displacement differential pressure based on the ImageJ gray analysis of the three visual micromodels. (<b>a</b>) Fracture type; (<b>b</b>) Cavity type; (<b>c</b>) Fracture-cavity type.</p>
Full article ">Figure 15 Cont.
<p>Water saturation versus displacement differential pressure based on the ImageJ gray analysis of the three visual micromodels. (<b>a</b>) Fracture type; (<b>b</b>) Cavity type; (<b>c</b>) Fracture-cavity type.</p>
Full article ">Figure 16
<p>Proportion of residual water film in different size channels based on the ImageJ gray analysis method.</p>
Full article ">
21 pages, 1953 KiB  
Article
Multi-Model Prediction for Demand Forecast in Water Distribution Networks
by Rodrigo Lopez Farias, Vicenç Puig, Hector Rodriguez Rangel and Juan J. Flores
Energies 2018, 11(3), 660; https://doi.org/10.3390/en11030660 - 15 Mar 2018
Cited by 25 | Viewed by 4864
Abstract
This paper presents a multi-model predictor called Qualitative Multi-Model Predictor Plus (QMMP+) for demand forecast in water distribution networks. QMMP+ is based on the decomposition of the quantitative and qualitative information of the time-series. The quantitative component (i.e., the daily consumption prediction) is [...] Read more.
This paper presents a multi-model predictor called Qualitative Multi-Model Predictor Plus (QMMP+) for demand forecast in water distribution networks. QMMP+ is based on the decomposition of the quantitative and qualitative information of the time-series. The quantitative component (i.e., the daily consumption prediction) is forecasted and the pattern mode estimated using a Nearest Neighbor (NN) classifier and a Calendar. The patterns are updated via a simple Moving Average scheme. The NN classifier and the Calendar are executed simultaneously every period and the most suited model for prediction is selected using a probabilistic approach. The proposed solution for water demand forecast is compared against Radial Basis Function Artificial Neural Networks (RBF-ANN), the statistical Autoregressive Integrated Moving Average (ARIMA), and Double Seasonal Holt-Winters (DSHW) approaches, providing the best results when applied to real demand of the Barcelona Water Distribution Network. QMMP+ has demonstrated that the special modelling treatment of water consumption patterns improves the forecasting accuracy. Full article
(This article belongs to the Special Issue Smart Water Networks in Urban Environments)
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Figure 1

Figure 1
<p>Qualitative-quantitative multi-model architecture.</p>
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<p>Hourly prediction of water consumption using a sliding window of width <span class="html-italic">h</span>.</p>
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<p>Nearest Neighbor Rule with the current observations.</p>
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<p>Euclidean distance of <math display="inline"> <semantics> <msup> <mi mathvariant="bold">Y</mi> <mo>′</mo> </msup> </semantics> </math> along the time compared with <math display="inline"> <semantics> <msub> <mi>P</mi> <mn>1</mn> </msub> </semantics> </math> and <math display="inline"> <semantics> <msub> <mi>P</mi> <mn>2</mn> </msub> </semantics> </math>.</p>
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<p>Barcelona drinking water transport network.</p>
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<p>Silhouette coefficient obtained by running <span class="html-italic">k</span>-Means with different <math display="inline"> <semantics> <mrow> <mi>k</mi> <mo>∈</mo> <mrow> <mn>1</mn> <mo>,</mo> <mo>⋯</mo> <mo>,</mo> <mn>7</mn> </mrow> </mrow> </semantics> </math>, for each of the seven water distribution sectors.</p>
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<p>Initial patterns <math display="inline"> <semantics> <mi mathvariant="bold">P</mi> </semantics> </math> of sectors 1 to 4.</p>
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<p>Initial patterns <math display="inline"> <semantics> <mi mathvariant="bold">P</mi> </semantics> </math> of sectors 5 to 7.</p>
Full article ">
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