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Energies, Volume 17, Issue 18 (September-2 2024) – 230 articles

Cover Story (view full-size image): To lessen the environmental and health impacts of agriculture, innovative powertrains are needed, especially for diesel tractors that emit significant pollution. Fuel-cell systems are a promising alternative due to their low emissions, quick refueling, and efficiency. A comprehensive life cycle assessment is essential to evaluate their sustainability. This article compares the life cycle impacts of diesel and fuel-cell hybrid tractors. This study uses LCA methodology and Ecoinvent 3.0 data, analyzing 10 impact categories. Fuel-cell tractors significantly reduce some impacts, decreasing human toxicity by over 92%, but they only reduce fossil fuel scarcity by 4.55% due to gray hydrogen use. Overall, the climate change impact is reduced by over 34%. Using green hydrogen from solar energy further reduces the impacts of climate change and fossil fuel depletion but worsens other impacts. View this paper
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20 pages, 21501 KiB  
Article
The Influence of Reduced Frequency on H-VAWT Aerodynamic Performance and Flow Field Near Blades
by Nianxi Yue, Congxin Yang and Shoutu Li
Energies 2024, 17(18), 4760; https://doi.org/10.3390/en17184760 - 23 Sep 2024
Viewed by 711
Abstract
Studies demonstrate that the reduced frequency k is influenced by the incoming wind speed U0 and the rotor speed n. As a dimensionless parameter, k characterizes the stability of the flow field, which is a critical factor affecting the performance of [...] Read more.
Studies demonstrate that the reduced frequency k is influenced by the incoming wind speed U0 and the rotor speed n. As a dimensionless parameter, k characterizes the stability of the flow field, which is a critical factor affecting the performance of vertical-axis wind turbines (VAWTs). This paper investigates the impact of k on the performance of straight-blade vertical-axis wind turbines (H-VAWT). The findings indicate that 0.05 is the critical value of k. The same k results in a similar flow field structure, yet the performance changes vary with different U0. A decrease in n or an increase in U0 leads to an increase in the average value and fluctuation of k, which subsequently reduces the rotor rotation torque Cm and decreases the maximum wind energy utilization rate Cpmax. This reduction in Cpmax weakens the stability of the flow field. Additionally, the high-speed area of the blade’s trailing edge velocity trajectory at θ=0°, θ=120°, and θ=240° expands with increasing range. Velocity dissipation in the high-speed area of the trailing edge affects the stability of the flow field within the rotor. Full article
(This article belongs to the Section A3: Wind, Wave and Tidal Energy)
►▼ Show Figures

Figure 1

Figure 1
<p>Numerical model of the H-VAWT.</p> Full article ">Figure 2
<p>Top view of H-VAWT wind rotor.</p> Full article ">Figure 3
<p>Grid division within the computational domain. (<b>a</b>) Overall view of the domain. (<b>b</b>) Interface grids. (<b>c</b>) Grids around the airfoil. (<b>d</b>) Boundary grids (The purple color means Stationary domain and the orange color means Rotating domain).</p> Full article ">Figure 4
<p>Schematic diagram of boundary conditions. (<b>a</b>) Top view. (<b>b</b>) Side view.</p> Full article ">Figure 5
<p>Verification of the results.</p> Full article ">Figure 6
<p>Grid independence test.</p> Full article ">Figure 7
<p>Variation of <math display="inline"><semantics> <mrow> <mi>k</mi> </mrow> </semantics></math> with azimuth angle <math display="inline"><semantics> <mrow> <mi>θ</mi> </mrow> </semantics></math>. (<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mn>0</mn> </mrow> </msub> </mrow> </semantics></math> = 11.4 m/s. (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mn>0</mn> </mrow> </msub> </mrow> </semantics></math> = 12.1 m/s. (<b>c</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mn>0</mn> </mrow> </msub> </mrow> </semantics></math> = 13.3 m/s. (<b>d</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mn>0</mn> </mrow> </msub> </mrow> </semantics></math> = 16.0 m/s.</p> Full article ">Figure 8
<p>Correlations between different parameters.</p> Full article ">Figure 9
<p>Variation of moment coefficient <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>C</mi> </mrow> <mrow> <mi>m</mi> </mrow> </msub> </mrow> </semantics></math> with azimuth angle <math display="inline"><semantics> <mrow> <mi>θ</mi> </mrow> </semantics></math>. (<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mn>0</mn> </mrow> </msub> </mrow> </semantics></math> = 11.4 m/s. (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mn>0</mn> </mrow> </msub> </mrow> </semantics></math> = 1 2.1 m/s. (<b>c</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mn>0</mn> </mrow> </msub> </mrow> </semantics></math> = 13.3 m/s. (<b>d</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mn>0</mn> </mrow> </msub> </mrow> </semantics></math> = 16.0 m/s.</p> Full article ">Figure 9 Cont.
<p>Variation of moment coefficient <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>C</mi> </mrow> <mrow> <mi>m</mi> </mrow> </msub> </mrow> </semantics></math> with azimuth angle <math display="inline"><semantics> <mrow> <mi>θ</mi> </mrow> </semantics></math>. (<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mn>0</mn> </mrow> </msub> </mrow> </semantics></math> = 11.4 m/s. (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mn>0</mn> </mrow> </msub> </mrow> </semantics></math> = 1 2.1 m/s. (<b>c</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mn>0</mn> </mrow> </msub> </mrow> </semantics></math> = 13.3 m/s. (<b>d</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mn>0</mn> </mrow> </msub> </mrow> </semantics></math> = 16.0 m/s.</p> Full article ">Figure 10
<p>Variations of wind energy utilization rate <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>C</mi> </mrow> <mrow> <mi>P</mi> </mrow> </msub> </mrow> </semantics></math> with azimuth angle <math display="inline"><semantics> <mrow> <mi>θ</mi> </mrow> </semantics></math>. (<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mn>0</mn> </mrow> </msub> </mrow> </semantics></math> = 11.4 m/s. (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mn>0</mn> </mrow> </msub> </mrow> </semantics></math> = 12.1 m/s. (<b>c</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mn>0</mn> </mrow> </msub> </mrow> </semantics></math> = 13.3 m/s. (<b>d</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mn>0</mn> </mrow> </msub> </mrow> </semantics></math> = 16.0 m/s.</p> Full article ">Figure 10 Cont.
<p>Variations of wind energy utilization rate <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>C</mi> </mrow> <mrow> <mi>P</mi> </mrow> </msub> </mrow> </semantics></math> with azimuth angle <math display="inline"><semantics> <mrow> <mi>θ</mi> </mrow> </semantics></math>. (<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mn>0</mn> </mrow> </msub> </mrow> </semantics></math> = 11.4 m/s. (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mn>0</mn> </mrow> </msub> </mrow> </semantics></math> = 12.1 m/s. (<b>c</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mn>0</mn> </mrow> </msub> </mrow> </semantics></math> = 13.3 m/s. (<b>d</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>U</mi> </mrow> <mrow> <mn>0</mn> </mrow> </msub> </mrow> </semantics></math> = 16.0 m/s.</p> Full article ">Figure 11
<p>Correlation between. <math display="inline"><semantics> <mrow> <mi>k</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>C</mi> </mrow> <mrow> <mi>m</mi> </mrow> </msub> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>C</mi> </mrow> <mrow> <mi>P</mi> </mrow> </msub> </mrow> </semantics></math>.</p> Full article ">Figure 12
<p>Variations in the flow field around blades with <span class="html-italic">k</span> at <math display="inline"><semantics> <mrow> <mi>θ</mi> <mo>=</mo> <mn>0</mn> <mo>°</mo> </mrow> </semantics></math>.</p> Full article ">Figure 13
<p>Changes in the flow field around blades with <span class="html-italic">k</span> at <math display="inline"><semantics> <mrow> <mi>θ</mi> <mo>=</mo> <mn>120</mn> <mo>°</mo> </mrow> </semantics></math>.</p> Full article ">Figure 14
<p>Changes in the flow field around blades with <span class="html-italic">k</span> at <math display="inline"><semantics> <mrow> <mi>θ</mi> <mo>=</mo> <mn>240</mn> <mo>°</mo> </mrow> </semantics></math>.</p> Full article ">Figure 15
<p>Changes in the pressure around blades with <span class="html-italic">k</span> at <math display="inline"><semantics> <mrow> <mi>θ</mi> <mo>=</mo> <mn>0</mn> <mo>°</mo> </mrow> </semantics></math>.</p> Full article ">Figure 16
<p>Changes in the pressure around blades with <span class="html-italic">k</span> at <math display="inline"><semantics> <mrow> <mi>θ</mi> <mo>=</mo> <mn>120</mn> <mo>°</mo> </mrow> </semantics></math>.</p> Full article ">Figure 17
<p>Changes in the pressure around blades with <span class="html-italic">k</span> at <math display="inline"><semantics> <mrow> <mi>θ</mi> <mo>=</mo> <mn>240</mn> <mo>°</mo> </mrow> </semantics></math>.</p> Full article ">
16 pages, 14074 KiB  
Article
Experimental Investigation of Large-Scale Vertically Coated Tubes for Enhanced Air–Steam Condensation Heat Transfer
by Zengqiao Chen, Keyuan Zhang and Naihua Wang
Energies 2024, 17(18), 4759; https://doi.org/10.3390/en17184759 - 23 Sep 2024
Viewed by 620
Abstract
Non-condensable gas plays a significant role in steam condensation, primarily by reducing heat transfer efficiency. Enhanced condensation heat transfer in the presence of non-condensable gas is crucial for improving thermal efficiency, reducing energy consumption, and lowering costs. However, experimental studies on applying coatings [...] Read more.
Non-condensable gas plays a significant role in steam condensation, primarily by reducing heat transfer efficiency. Enhanced condensation heat transfer in the presence of non-condensable gas is crucial for improving thermal efficiency, reducing energy consumption, and lowering costs. However, experimental studies on applying coatings to enhance condensation heat transfer in large-scale vertical outer tubes with non-condensable gas are scarce. This study investigates the condensation heat transfer performance of vertical stainless steel- and brass-coated tubes compared to their bare counterparts at different air concentrations (0.4, 0.3, 0.15, and 0.08). All tubes have an outer diameter of 19 mm and an effective length of 1080 mm. Visualizations reveal that condensate flow rates as high as 0.5 m/s on bare tubes cause significant disturbances to the diffusion layer. At various air concentrations, the maximum condensation heat transfer coefficient of the coated stainless steel tube exhibited increases of 22.2%, 11.9%, 4.2%, and 19.6% compared with the uncoated stainless steel tube. Similarly, the maximum condensation heat transfer coefficient for the coated brass tube showed significant increases of 58.9%, 53.5%, 68.0%, and 70.7% compared with the uncoated brass tube. Notably, the enhancement effect on heat transfer performance is more pronounced when the same type of modified surface is applied to the brass tube compared with the stainless steel tube. Full article
(This article belongs to the Section J1: Heat and Mass Transfer)
►▼ Show Figures

Figure 1

Figure 1
<p>Schematic diagram of NCG–steam condensation process.</p> Full article ">Figure 2
<p>Experimental setup for condensation heat transfer. (<b>a</b>) Schematic diagram of the experimental apparatus. (<b>b</b>) Schematic and physical representation of the test section. (The red line represents the condenser tube and the green line represents the spatial location in the vessel where the thermocouples that measure the bulk temperature are located.)</p> Full article ">Figure 2 Cont.
<p>Experimental setup for condensation heat transfer. (<b>a</b>) Schematic diagram of the experimental apparatus. (<b>b</b>) Schematic and physical representation of the test section. (The red line represents the condenser tube and the green line represents the spatial location in the vessel where the thermocouples that measure the bulk temperature are located.)</p> Full article ">Figure 3
<p>Heat transfer coefficient <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>h</mi> </mrow> <mrow> <mi mathvariant="normal">a</mi> <mi mathvariant="normal">v</mi> <mi mathvariant="normal">e</mi> </mrow> </msub> </mrow> </semantics></math>–subcooling <math display="inline"><semantics> <mrow> <msub> <mrow> <mo>∆</mo> <mi>T</mi> </mrow> <mrow> <mi mathvariant="normal">s</mi> <mi mathvariant="normal">u</mi> <mi mathvariant="normal">b</mi> </mrow> </msub> </mrow> </semantics></math> curves for bare and coated stainless steel tubes at the air concentrations of <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>W</mi> </mrow> <mrow> <mi mathvariant="normal">a</mi> </mrow> </msub> </mrow> </semantics></math> = 0.4 (<b>a</b>), <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>W</mi> </mrow> <mrow> <mi mathvariant="normal">a</mi> </mrow> </msub> </mrow> </semantics></math> = 0.3 (<b>b</b>), <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>W</mi> </mrow> <mrow> <mi mathvariant="normal">a</mi> </mrow> </msub> </mrow> </semantics></math> = 0.15 (<b>c</b>), and <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>W</mi> </mrow> <mrow> <mi mathvariant="normal">a</mi> </mrow> </msub> </mrow> </semantics></math> = 0.08 (<b>d</b>).</p> Full article ">Figure 4
<p>Heat flux <span class="html-italic">q</span>–subcooling <math display="inline"><semantics> <mrow> <msub> <mrow> <mo>∆</mo> <mi>T</mi> </mrow> <mrow> <mi mathvariant="normal">s</mi> <mi mathvariant="normal">u</mi> <mi mathvariant="normal">b</mi> </mrow> </msub> </mrow> </semantics></math> curves for bare and coated stainless steel tubes at the air concentrations of <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>W</mi> </mrow> <mrow> <mi mathvariant="normal">a</mi> </mrow> </msub> </mrow> </semantics></math> = 0.4 (<b>a</b>), <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>W</mi> </mrow> <mrow> <mi mathvariant="normal">a</mi> </mrow> </msub> </mrow> </semantics></math> = 0.3 (<b>b</b>), <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>W</mi> </mrow> <mrow> <mi mathvariant="normal">a</mi> </mrow> </msub> </mrow> </semantics></math> = 0.15 (<b>c</b>), and <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>W</mi> </mrow> <mrow> <mi mathvariant="normal">a</mi> </mrow> </msub> </mrow> </semantics></math> = 0.08 (<b>d</b>).</p> Full article ">Figure 5
<p>Dynamic behavior of condensate on the surface of bare and coated stainless steel tubes. (<b>a</b>) Bare tube, NCG = 0.3, <math display="inline"><semantics> <mrow> <msub> <mrow> <mo>∆</mo> <mi>T</mi> </mrow> <mrow> <mi mathvariant="normal">s</mi> <mi mathvariant="normal">u</mi> <mi mathvariant="normal">b</mi> </mrow> </msub> </mrow> </semantics></math> = ~14 K, (<b>b</b>) coated tube, NCG = 0.3, <math display="inline"><semantics> <mrow> <msub> <mrow> <mo>∆</mo> <mi>T</mi> </mrow> <mrow> <mi mathvariant="normal">s</mi> <mi mathvariant="normal">u</mi> <mi mathvariant="normal">b</mi> </mrow> </msub> </mrow> </semantics></math> = ~14 K, and (<b>c</b>) coated tube, NCG = 0.08, <math display="inline"><semantics> <mrow> <msub> <mrow> <mo>∆</mo> <mi>T</mi> </mrow> <mrow> <mi mathvariant="normal">s</mi> <mi mathvariant="normal">u</mi> <mi mathvariant="normal">b</mi> </mrow> </msub> </mrow> </semantics></math> = ~10 K.</p> Full article ">Figure 5 Cont.
<p>Dynamic behavior of condensate on the surface of bare and coated stainless steel tubes. (<b>a</b>) Bare tube, NCG = 0.3, <math display="inline"><semantics> <mrow> <msub> <mrow> <mo>∆</mo> <mi>T</mi> </mrow> <mrow> <mi mathvariant="normal">s</mi> <mi mathvariant="normal">u</mi> <mi mathvariant="normal">b</mi> </mrow> </msub> </mrow> </semantics></math> = ~14 K, (<b>b</b>) coated tube, NCG = 0.3, <math display="inline"><semantics> <mrow> <msub> <mrow> <mo>∆</mo> <mi>T</mi> </mrow> <mrow> <mi mathvariant="normal">s</mi> <mi mathvariant="normal">u</mi> <mi mathvariant="normal">b</mi> </mrow> </msub> </mrow> </semantics></math> = ~14 K, and (<b>c</b>) coated tube, NCG = 0.08, <math display="inline"><semantics> <mrow> <msub> <mrow> <mo>∆</mo> <mi>T</mi> </mrow> <mrow> <mi mathvariant="normal">s</mi> <mi mathvariant="normal">u</mi> <mi mathvariant="normal">b</mi> </mrow> </msub> </mrow> </semantics></math> = ~10 K.</p> Full article ">Figure 6
<p>Heat transfer coefficient <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>h</mi> </mrow> <mrow> <mi mathvariant="normal">a</mi> <mi mathvariant="normal">v</mi> <mi mathvariant="normal">e</mi> </mrow> </msub> </mrow> </semantics></math>–wall subcooling <math display="inline"><semantics> <mrow> <msub> <mrow> <mo>∆</mo> <mi>T</mi> </mrow> <mrow> <mi mathvariant="normal">s</mi> <mi mathvariant="normal">u</mi> <mi mathvariant="normal">b</mi> </mrow> </msub> </mrow> </semantics></math> curves for bare and coated brass tubes at the air concentrations of <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>W</mi> </mrow> <mrow> <mi mathvariant="normal">a</mi> </mrow> </msub> </mrow> </semantics></math> = 0.4 (<b>a</b>), <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>W</mi> </mrow> <mrow> <mi mathvariant="normal">a</mi> </mrow> </msub> </mrow> </semantics></math> = 0.3 (<b>b</b>), <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>W</mi> </mrow> <mrow> <mi mathvariant="normal">a</mi> </mrow> </msub> </mrow> </semantics></math> = 0.15 (<b>c</b>), and <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>W</mi> </mrow> <mrow> <mi mathvariant="normal">a</mi> </mrow> </msub> </mrow> </semantics></math> = 0.08 (<b>d</b>).</p> Full article ">Figure 7
<p>Heat flux <span class="html-italic">q</span>–wall subcooling <math display="inline"><semantics> <mrow> <msub> <mrow> <mo>∆</mo> <mi>T</mi> </mrow> <mrow> <mi mathvariant="normal">s</mi> <mi mathvariant="normal">u</mi> <mi mathvariant="normal">b</mi> </mrow> </msub> </mrow> </semantics></math> curves for bare and coated brass tubes at the air concentrations of <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>W</mi> </mrow> <mrow> <mi mathvariant="normal">a</mi> </mrow> </msub> </mrow> </semantics></math> = 0.4 (<b>a</b>), <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>W</mi> </mrow> <mrow> <mi mathvariant="normal">a</mi> </mrow> </msub> </mrow> </semantics></math> = 0.3 (<b>b</b>), <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>W</mi> </mrow> <mrow> <mi mathvariant="normal">a</mi> </mrow> </msub> </mrow> </semantics></math> = 0.15 (<b>c</b>), and <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>W</mi> </mrow> <mrow> <mi mathvariant="normal">a</mi> </mrow> </msub> </mrow> </semantics></math> = 0.08 (<b>d</b>).</p> Full article ">Figure 8
<p>Dynamic behavior of condensate on the surface of bare and coated brass tubes at NCG = 0.3, <math display="inline"><semantics> <mrow> <msub> <mrow> <mo>∆</mo> <mi>T</mi> </mrow> <mrow> <mi mathvariant="normal">s</mi> <mi mathvariant="normal">u</mi> <mi mathvariant="normal">b</mi> </mrow> </msub> </mrow> </semantics></math> = ~12 K for the (<b>a</b>) bare tube and (<b>b</b>) coated tube.</p> Full article ">
18 pages, 1222 KiB  
Article
Computational Optimization for CdS/CIGS/GaAs Layered Solar Cell Architecture
by Satyam Bhatti, Habib Ullah Manzoor, Ahmed Zoha and Rami Ghannam
Energies 2024, 17(18), 4758; https://doi.org/10.3390/en17184758 - 23 Sep 2024
Viewed by 779
Abstract
Multi-junction solar cells are vital in developing reliable, green, sustainable solar cells. Consequently, the computational optimization of solar cell architecture has the potential to profoundly expedite the process of discovering high-efficiency solar cells. Copper indium gallium selenide (CIGS)-based solar cells exhibit substantial performance [...] Read more.
Multi-junction solar cells are vital in developing reliable, green, sustainable solar cells. Consequently, the computational optimization of solar cell architecture has the potential to profoundly expedite the process of discovering high-efficiency solar cells. Copper indium gallium selenide (CIGS)-based solar cells exhibit substantial performance compared to those utilizing cadmium sulfide (CdS). Likewise, CIGS-based devices are more efficient according to their device performance, environmentally benign nature, and thus, reduced cost. Therefore, the paper introduces an optimization process of three-layered n-CdS/p-CIGS/p-GaAs (NPP)) solar cell architecture based on thickness and carrier charge density. An in-depth investigation of the numerical analysis for homojunction PPN-junction with the ’GaAs’ layer structure along with n-ZnO front contact was simulated using the Solar Cells Capacitance Simulator (SCAPS-1D) software. Subsequently, various computational optimization techniques for evaluating the effect of the thickness and the carrier density on the performance of the PPN layer on solar cell architecture were examined. The electronic characteristics by adding the GaAs layer on the top of the conventional (PN) junction further led to optimized values of the power conversion efficiency (PCE), open-circuit voltage (VOC), fill factor (FF), and short-circuit current density (JSC) of the solar cell. Lastly, the paper concludes by highlighting the most promising results of our study, showcasing the impact of adding the GaAs layer. Hence, using the optimized values from the analysis, thickness of 5 (μm) and carrier density of 1×1020 (1/cm) resulted in the maximum PCE, VOC, FF, and JSC of 45.7%, 1.16 V, 89.52%, and 43.88 (mA/m2), respectively, for the proposed solar cell architecture. The outcomes of the study aim to pave the path for highly efficient, optimized, and robust multi-junction solar cells. Full article
(This article belongs to the Special Issue Advances in High-Performance Perovskite Solar Cells)
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<p>Figure represents the baseline structure of the proposed multi-junction solar cell architecture, with simple three n-ZnO/n-CdS/p-CIGS, where the ZnO layer acts as a window/buffer layer. The solar light in the structure is incident from the right contact (front) using the simulation tool SCAPS-1D. The baseline values for the given solar cell architecture are set as mentioned in the simulation setup, whilst the thickness and current density (doping) values for the baseline are set as 0.5 μm and <math display="inline"><semantics> <mrow> <mn>1</mn> <mo>×</mo> <msup> <mn>10</mn> <mn>10</mn> </msup> </mrow> </semantics></math> (1/<math display="inline"><semantics> <msup> <mi>cm</mi> <mn>3</mn> </msup> </semantics></math>), respectively.</p> Full article ">Figure 2
<p>Figure represents the optimization technique used to evaluate the most efficient value of the thickness for the p-CIGS and n-CdS semiconductor materials using the heatmap confusion matrix. In addition, it shows the (<b>a</b>) efficiency, <math display="inline"><semantics> <mi>η</mi> </semantics></math> (%), (<b>b</b>) fill factor, FF (%), (<b>c</b>) current density (mA/cm<sup>2</sup>), (<b>d</b>) open-circuit voltage (<math display="inline"><semantics> <msub> <mi>V</mi> <mrow> <mi>O</mi> <mi>C</mi> </mrow> </msub> </semantics></math>).</p> Full article ">Figure 3
<p>IV and PV characteristics after the thickness optimization and quantum efficiency (%) curve.</p> Full article ">Figure 4
<p>Figure represents an optimization of the carrier charge density, also known as doping concentration (1/cm) for the n-ZnO/p-CdS/p-CIGS multi-junction solar cell architecture incorporating the heatmap confusion matrix. Like the thickness optimization, doping optimization values are as follows: (<b>a</b>) efficiency, <math display="inline"><semantics> <mi>η</mi> </semantics></math> (%), (<b>b</b>) fill factor, FF (%), (<b>c</b>) current density (mA/cm<sup>2</sup>), (<b>d</b>) open-circuit voltage (<math display="inline"><semantics> <msub> <mi>V</mi> <mrow> <mi>O</mi> <mi>C</mi> </mrow> </msub> </semantics></math>).</p> Full article ">Figure 5
<p>IV and PV characteristics after density optimization optimization and quantum efficiency (%) curve.</p> Full article ">Figure 6
<p>Figure represents the introduction of the p-GaAs layer to the proposed baseline multi-junction solar cell architecture. The GaAs layer is added at the bottom of the p-CIGS layer next to the left contact (back) of the proposed solar cell architecture for analyzing the electrical and electronic performance in the real-world environment with the help of the SCAPS-1D simulation tool. Moreover, the light in this scenario is incident from the right contact (front) to achieve the maximum possible efficiency of the proposed solar cell architecture.</p> Full article ">Figure 7
<p>Figure represents the output results for the simulations performed by introducing the GaAs layer to the proposed baseline solar cell architecture. Herein, the optimization for the thickness and the charge current density (doping concentration) is evaluated simultaneously using the same heatmap confusion matrix. Accordingly, electrical characteristics are presented as (<b>a</b>) efficiency, <math display="inline"><semantics> <mi>η</mi> </semantics></math> (%), (<b>b</b>) fill factor, FF (%), (<b>c</b>) current density (mA/cm<sup>2</sup>), (<b>d</b>) open-circuit voltage (<math display="inline"><semantics> <msub> <mi>V</mi> <mrow> <mi>O</mi> <mi>C</mi> </mrow> </msub> </semantics></math>).</p> Full article ">Figure 8
<p>IV and PV characteristics after adding GaAs to Cds/CIGS and quantum efficiency (%) curve.</p> Full article ">Figure 9
<p>Figure represents a critical comparison of IV characteristics of the proposed baseline multi-junction solar cell (n-ZnO/p-CdS/p-CIGS) architecture after the thickness optimization (blue). The orange curve shows the results after the optimization of the doping concentration of the proposed solar cell. Lastly, the green curve shows the results of the optimization of both thickness and doping concentration after introducing the GaAs layer to the proposed solar cell architecture.</p> Full article ">Figure 10
<p>Figure represents the comparison of PV characteristics of the proposed baseline multi-junction solar cell (CdS/CIGS/GaAs) architecture after the thickness optimization (blue). The orange curve shows the results after the optimization of the doping concentration of the proposed solar cell and the green curve shows the results of the optimization of both thickness and doping concentration after introducing the GaAs layer to the solar cell.</p> Full article ">Figure 11
<p>Figure showcases the results of the comparison of quantum efficiency (QE) for the proposed solar cell (n-ZnO/p-CdS/p-CIGS) architecture after the thickness optimization (blue). Accordingly, the orange curve shows the results after the optimization of the doping concentration of the proposed solar cell, and the grey curve depicts the results of the optimization of both thickness and doping concentration after introducing the GaAs layer to the solar cell.</p> Full article ">
1 pages, 121 KiB  
Correction
Correction: Shi et al. Parameter Estimation Method for Virtual Power Plant Frequency Response Model Based on SLP. Energies 2024, 17, 3124
by Zheng Shi, Haixiao Zhu, Haibo Zhao, Peng Wang, Yan Liang, Kaikai Wang, Jie Chen, Xiaoming Zheng and Hongli Liu
Energies 2024, 17(18), 4757; https://doi.org/10.3390/en17184757 - 23 Sep 2024
Viewed by 387
Abstract
In the original publication [...] Full article
(This article belongs to the Special Issue Optimal Planning and Operation in RES-Rich Power Systems under Electricity and Carbon Emission Market Environment)
23 pages, 1526 KiB  
Review
Overview of the Recent Findings in the Perovskite-Type Structures Used for Solar Cells and Hydrogen Storage
by Meng-Hsueh Kuo, Neda Neykova and Ivo Stachiv
Energies 2024, 17(18), 4755; https://doi.org/10.3390/en17184755 - 23 Sep 2024
Viewed by 1512
Abstract
Perovskite-type structures have unique crystal architecture and chemical composition, which make them highly attractive for the design of solar cells. For instance, perovskite-based solar cells have been shown to perform better than silicon cells, capable of adsorbing a wide range of light wavelengths, [...] Read more.
Perovskite-type structures have unique crystal architecture and chemical composition, which make them highly attractive for the design of solar cells. For instance, perovskite-based solar cells have been shown to perform better than silicon cells, capable of adsorbing a wide range of light wavelengths, and they can be relatively easily manufactured at a low cost. Importantly, the perovskite-based structures can also adsorb a significant amount of hydrogen atoms into their own structure; therefore, perovskite holds promise in the solid-state storage of hydrogen. It is widely expected by the scientific community that the controlled adsorption/desorption of the hydrogen atoms into/from perovskite-based structures can help to overcome the main hydrogen storage issues such as a low volumetric density and the safety concerns (i.e., the hydrogen embrittlement affects strongly the mechanical properties of metals and, as such, the storage or transport of the gaseous hydrogen in the vessels is, especially for large vessel volumes, challenging). The purpose of this review is to provide an updated overview of the recent results and studies focusing on the perovskite materials used for both solar cells and hydrogen storage applications. Particular attention is given to (i) the preparation and the achievable efficiency and stability of the perovskite solar cells and (ii) the structural, thermodynamic, and storage properties of perovskite hydrides and oxides. We show that the perovskite materials can not only reach the efficiency above current Si-based solar cells but also, due to good stability and reasonable price, can be preferable in the solid-state storage of hydrogen. Then, the future trends and directions in the research and application of perovskite in both solar cells and hydrogen storage are also highlighted. Full article
(This article belongs to the Special Issue Advanced Materials and Technologies for Hydrogen Evolution)
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<p>Crystal structure of hybrid organic-inorganic perovskite materials.</p> Full article ">Figure 2
<p>Band diagram and main process and PSC: (1) Absorption of photon and free charges generation; (2) Charge transport; (3) Charge extraction. Here, HTL stands for the hole transport layer, FTO for fluorine-doped tin oxide, and ETL is the electron transport layer. Results are reproduced from [<a href="#B109-energies-17-04755" class="html-bibr">109</a>] with permission from Elsevier.</p> Full article ">Figure 3
<p>Four device configurations of PSCs: mesoscopic structure, planar structure, triple mesoscopic structure, and tandem structure with lower-bandgap subcell. Here, TCO is the transparent conductive oxide, and ETL is the electron transport layer. Present results are reproduced from [<a href="#B112-energies-17-04755" class="html-bibr">112</a>] with permission from AAAS.</p> Full article ">Figure 4
<p>The crystal structure of MgXH<sub>3</sub> for X = Co, X = Cu, X = Ni.</p> Full article ">Figure 5
<p>The crystal structures of AeSiH<sub>3</sub> for Ae = K, Ae = Li, Ae = Na, Ae = Mg.</p> Full article ">
18 pages, 4744 KiB  
Article
Heat Transfer Enhancement in a 3D-Printed Compact Heat Exchanger
by Marcin Kruzel, Tadeusz Bohdal and Krzysztof Dutkowski
Energies 2024, 17(18), 4754; https://doi.org/10.3390/en17184754 - 23 Sep 2024
Viewed by 994
Abstract
The study describes experimental data on thermal tests during the condensation of HFE7100 refrigerant in a compact heat exchanger. The heat exchanger was manufactured using the additive 3D printing in metal. The material is AISI 316L steel. MPCM slurry was used as the [...] Read more.
The study describes experimental data on thermal tests during the condensation of HFE7100 refrigerant in a compact heat exchanger. The heat exchanger was manufactured using the additive 3D printing in metal. The material is AISI 316L steel. MPCM slurry was used as the heat exchanger coolant, and water was used as the reference medium. The refrigerant was condensed on a bundle of circular tubes made of steel with an internal/external diameter of di/de = 2/3 mm, while a mixture of water and phase change materials as the coolant flowed through the channels. Few studies consider the heat exchange in condensation using phase change materials; furthermore, there is also a lack of description of heat exchange in small-sized exchangers printed from metal. Most papers deal with computer research, including flow simulations of heat exchange. The study describes the process of heat exchange enhancement using the phase transition of coolant. Experimental data for the mPCM slurry coolant flow was compared to the data of pure water flow as a reference liquid. The tests were carried out under the following thermal and flow conditions: G = 10–450 [kg m−² s−1], q = 2000–25,000 [W m−²], and ts = 30–40 [°C]. The conducted research provided many quantities describing the heat exchange in compact heat exchangers, including heat exchanger heat power, heat exchange coefficient, and heat exchange coefficients for working media. Based on these factors, the thermal performance of the heat exchanger was described. External characteristics include the value of the thermal power and the heat exchange coefficient as a function of the mass flow density of the working medium and the average logarithmic temperature difference. The performance of the heat exchanger was presented as the dependencies of the heat exchange coefficients on the mass flux density and the heat flux density on the heat exchange surface. The thickness of the refrigerant’s condensate film was also determined. Furthermore, a model was proposed to determine the heat exchange coefficient value for the condensing HFE7100 refrigerant on the outer surface of a bundle of smooth tubes inside a compact heat exchanger. According to experimental data, the calculation results were in good agreement with each other, with a range of 25%. These data can be used to design mini condensers that are widely used in practice. Full article
(This article belongs to the Special Issue Modeling, Optimization and Experimental Investigation of Heat Exchangers for Power Plants)
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<p>Heat exchanger design with dimensions.</p> Full article ">Figure 2
<p>Overall view of the 3D-printed heat exchanger.</p> Full article ">Figure 3
<p>Pictorial scheme of the test stand: 1, 3D heat exchanger; 2, heating fluid mass flow meter; 3, mPCM fluid storage vessel; 4, plate heat exchanger; 5, cooling fluid mass flow meter; 6, differential pressure sensor; 7, pump; 8, cooling unit; 9, data recorder; 10, heated refrigerant storage vessel; 11, autotransformer.</p> Full article ">Figure 4
<p>The influence of coolant temperature on the specific enthalpy of the base liquid (pure water) and the slurry containing 5% and 10% mPCM.</p> Full article ">Figure 5
<p>The heat exchanger thermal power (heat flux) dependency on the coolant temperature at the heat exchanger outlet.</p> Full article ">Figure 6
<p>The dependency of the OHTC on the coolant temperature at the exit of the HX.</p> Full article ">Figure 7
<p>Comparison of experimental and theoretical [<a href="#B32-energies-17-04754" class="html-bibr">32</a>] data on the dependence of (<b>a</b>) the HTC of the refrigerant on the heat flux density; (<b>b</b>) the HTC of the refrigerant on the <span class="html-italic">t<sub>s</sub></span> − <span class="html-italic">t<sub>w</sub></span> temperature difference.</p> Full article ">Figure 7 Cont.
<p>Comparison of experimental and theoretical [<a href="#B32-energies-17-04754" class="html-bibr">32</a>] data on the dependence of (<b>a</b>) the HTC of the refrigerant on the heat flux density; (<b>b</b>) the HTC of the refrigerant on the <span class="html-italic">t<sub>s</sub></span> − <span class="html-italic">t<sub>w</sub></span> temperature difference.</p> Full article ">Figure 8
<p>The dependence of the HTC on the refrigerant condensate thickness.</p> Full article ">Figure 9
<p>The dependency of the HTC on the condensate velocity.</p> Full article ">Figure 10
<p>Comparison of the Nusselt number values for the results of experimental studies with the results of calculations according to dependence (7).</p> Full article ">Figure 11
<p>Mean absolute percentage error distribution for Nusselt number.</p> Full article ">
21 pages, 862 KiB  
Article
Predicting UK Domestic Electricity and Gas Consumption between Differing Demographic Household Compositions
by Gregory Sewell, Stephanie Gauthier, Patrick James and Sebastian Stein
Energies 2024, 17(18), 4753; https://doi.org/10.3390/en17184753 - 23 Sep 2024
Cited by 1 | Viewed by 544
Abstract
This paper examines the influence of building characteristics, occupant demographics and behaviour on gas and electricity consumption, differentiating between family groups; homes with children; homes with elderly; and homes without either. Both regression and Lasso regression analyses are used to analyse data from [...] Read more.
This paper examines the influence of building characteristics, occupant demographics and behaviour on gas and electricity consumption, differentiating between family groups; homes with children; homes with elderly; and homes without either. Both regression and Lasso regression analyses are used to analyse data from a 2019 UK-based survey of 4358homes (n = 1576 with children, n = 436 with elderly, n = 2330 without either). Three models (building, occupants, behaviour) were tested against electricity and gas consumption for each group. Results indicated that homes without children or elderly consumed the least energy. Property Type emerged as the strongest predictor in the Building Model (except for homes with elderly), while Current Energy Efficiency was less significant, particularly for homes with elderly occupants. Homeownership and number of occupants were the most influential factors in the Occupants Model, though this pattern did not hold for homes with elderly. Many occupant and behaviour variables are often considered ‘unregulated energy’ in calculations such as SAP and are thus typically disregarded. However, this study found these variables to be significant, especially as national standards improve. The findings suggest that incorporating occupant behaviour into energy modelling could help reduce the energy performance gap. Full article
(This article belongs to the Section B1: Energy and Climate Change)
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<p>Data analysis framework.</p> Full article ">Figure 2
<p>Annual Electricity (on the <b>left</b>) and Annual Gas consumption (on the <b>right</b>) for the three household groups.</p> Full article ">Figure 3
<p>All household groups, variable ‘home ownership’ (Yes/No) for electricity consumption (on the <b>left</b>) and gas consumption (on the <b>right</b>).</p> Full article ">Figure 4
<p>Variable ‘Wall construction’ vs. Gas consumption in kWh/annual for ‘Group A—With Children’ (<b>top</b>), ‘Group B—With Elderly’ (<b>middle</b>) and ‘Group C—With Neither’ (<b>bottom</b>).</p> Full article ">Figure 5
<p>Variable ‘current energy efficiency’(EPC rating) vs. electricity consumption (on the left) and vs. gas consumption (on the right), for ‘Group A—With Children’(red line), ‘Group B—With Elderly’ (green line) and ‘Group C—With Neither’ (blue line).</p> Full article ">
38 pages, 3457 KiB  
Review
Review and Assessment of Decarbonized Future Electricity Markets
by Ali Darudi and Hannes Weigt
Energies 2024, 17(18), 4752; https://doi.org/10.3390/en17184752 - 23 Sep 2024
Viewed by 522
Abstract
The electricity sector plays a key role in achieving zero emissions targets. The required transition will lead to substantial changes in the supply, demand, and distribution of electricity, as well as in stakeholder roles. Future market designs may change substantially to accommodate these [...] Read more.
The electricity sector plays a key role in achieving zero emissions targets. The required transition will lead to substantial changes in the supply, demand, and distribution of electricity, as well as in stakeholder roles. Future market designs may change substantially to accommodate these changes, address challenges, and take advantage of new opportunities. This paper reviews the characteristics of future carbon-neutral electricity systems and electricity market design options. To provide a guiding framework for the literature review, we transfer the complexity of electricity systems into a three-layer structure: Firstly, we analyze papers that rely on techno-economic modeling of the physical electricity system. As a case study, we analyze various studies focusing on a decarbonized European electricity system in 2050. Secondly, we review papers that investigate the economic behavior and effects of self-interest-seeking stakeholders such as producers, network operators, and consumers. Finally, we review papers focusing on policy and market design questions that guide policymakers in achieving a target physical asset combination while considering the behavior of stakeholders. We highlight common trends and disagreements in the literature, review the main drivers of future markets, and finally provide a mapping between those drivers, challenges, and opportunities. The review concludes that the most promising next step toward a fully comprehensive assessment approach is to combine existing approaches across topical and disciplinary boundaries. Full article
(This article belongs to the Special Issue Energy Transition and Energy Future: Bridging Insights into Renewable Energy and Environmental Sustainability)
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<p>Countries that adopted net-zero targets. (Source: Net-Zero Tracker, World Resources Institute, at <a href="http://www.climatewatchdata.org/net-zero-tracker" target="_blank">www.climatewatchdata.org/net-zero-tracker</a>, accessed on 10 January 2024).</p> Full article ">Figure 2
<p>Three-layer representation of the electricity sector (DR: demand response, RES: renewable energy sources, EV: electric vehicles, HP: heat pumps, ICT: information and communication technology).</p> Full article ">Figure 3
<p>Technology generation shares of the European decarbonization studies.</p> Full article ">Figure 4
<p>Total generation of the European decarbonization studies.</p> Full article ">Figure 5
<p>Transport demand of electric vehicles in a working day (Friday) and a weekend (Saturday) for a week in 2040 from [<a href="#B20-energies-17-04752" class="html-bibr">20</a>] and derived BEV charging profile in instant charging and smart charging approaches.</p> Full article ">Figure 6
<p>Common assumptions and insights of the investigated future electricity system literature.</p> Full article ">Figure 7
<p>Relations between drivers, challenges, and alterations in future electricity systems.</p> Full article ">
18 pages, 26928 KiB  
Article
Seismic Prediction Method of Shale Reservoir Brittleness Index Based on the BP Neural Network for Improving Shale Gas Extraction Efficiency
by Xuejuan Zhang, Haiyan She, Lei Zhang, Ruolin Li, Jiayang Feng, Ruhao Liu and Xinrui Wang
Energies 2024, 17(18), 4751; https://doi.org/10.3390/en17184751 - 23 Sep 2024
Viewed by 473
Abstract
The current seismic prediction methods of the shale brittleness index are all based on the pre-stack seismic inversion of elastic parameters, and the elastic parameters are transformed by Rickman and other simple linear mathematical relationship formulas. In order to address the low accuracy [...] Read more.
The current seismic prediction methods of the shale brittleness index are all based on the pre-stack seismic inversion of elastic parameters, and the elastic parameters are transformed by Rickman and other simple linear mathematical relationship formulas. In order to address the low accuracy of the seismic prediction results for the brittleness index, this study proposes a method for predicting the brittleness index of shale reservoirs based on an error backpropagation neural network (BP neural network). The continuous static rock elastic parameters were calculated by fitting the triaxial test data with well logging data, and the static elastic parameters with good correlation with the brittleness index of shale minerals were selected as the sample data of the BP neural network model. A dataset of 1970 data points, characterized by Young’s modulus, Poisson’s ratio, shear modulus, and the mineral brittleness index, was constructed. A total of 367 sets of data points from well Z4 were randomly retained as model validation data, and 1603 sets of data points from the other three wells were divided into model training data and test data at a ratio of 7:3. The calculation accuracy of the model with different numbers of nodes was analyzed and the key parameters of the BP neural network structure such as the number of input layers, the number of output layers, the number of hidden layers, and the number of neurons were determined. The gradient descent method was used to determine the weight and bias of the model parameters with the smallest error, the BP neural network model was trained, and the stability of the brittleness index prediction model of the BP neural network was verified by posterior data. After obtaining Young’s modulus, Poisson’s ratio, and shear modulus through pre-stack seismic inversion, the BP neural network model established in this study was used to predict the brittleness index distribution of the target layer in the study area. Compared with the conventional Rickman method, the prediction coincidence rate is 69.16%, and the prediction coincidence rate between the prediction results and the real value is 95.79%, which is 26.63% higher. The BP neural network method proposed in this paper provides a reliable new method for seismic prediction of the shale reservoir brittleness index, which has important practical significance for clarifying the shale gas development scheme and improving shale gas exploitation efficiency. Full article
(This article belongs to the Section H: Geo-Energy)
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<p>Schematic diagram of BP neural network structure.</p> Full article ">Figure 2
<p>Flowchart of the research process in this study.</p> Full article ">Figure 3
<p>Dynamic and static Young’s modulus fitting and data normalization: (<b>a</b>) fitting analysis plot; (<b>b</b>) plot of normalized results.</p> Full article ">Figure 4
<p>Brittleness index curves obtained by the mineral method: (<b>a</b>) Z1 well brittleness curves; (<b>b</b>) Z2 well brittleness curves; (<b>c</b>) Z3 well brittleness curves; (<b>d</b>) Z4 well brittleness curves.</p> Full article ">Figure 5
<p>Data processing result of well Z1.</p> Full article ">Figure 6
<p>Correlation analysis of elastic parameters: (<b>a</b>) brittleness index and Young’s modulus intersection graph; (<b>b</b>) brittleness index and Poisson’s ratio intersection graph; (<b>c</b>) brittleness index and shear modulus intersection graph; (<b>d</b>) brittleness index and bulk modulus intersection graph; (<b>e</b>) brittleness index and Lame constants intersection graph.</p> Full article ">Figure 7
<p>Model absolute error analysis with different numbers of hidden nodes.</p> Full article ">Figure 8
<p>Correlation analysis between model training data, and test data: (<b>a</b>) correlation analysis between predicted and actual brittleness index of training data; (<b>b</b>) correlation analysis between predicted and actual brittleness index of test data.</p> Full article ">Figure 9
<p>Validation analysis of BP neural network model effectiveness: (<b>a</b>) correlation analysis between predicted and real values of brittleness index for Z4 well; (<b>b</b>) comparison of predicted and real values of brittleness index for Z4 well.</p> Full article ">Figure 10
<p>Optimization of pre-stack seismic data and fine calibration of well seismic data: (<b>a</b>) CDP gathers; (<b>b</b>) Super CDP gathers; (<b>c</b>) AVA gathers; (<b>d</b>) Z1 well synthetic seismic record.</p> Full article ">Figure 11
<p>The plane diagrams of pre-stack simultaneous inversion of elastic parameters data volumes: (<b>a</b>) the plane diagrams of Young’s modulus data volume; (<b>b</b>) the plane diagrams of Poisson’s ratio data volume; (<b>c</b>) the plane diagrams of shear modulus data volume.</p> Full article ">Figure 12
<p>The plane diagram of the predicted brittleness index of the BP neural network method.</p> Full article ">Figure 13
<p>Comparison plane diagram of the predicted brittleness index results for the target layer in the research area: (<b>a</b>) interpolation distribution plane diagram of the actual brittleness index; (<b>b</b>) brittleness index prediction plane distribution diagram of BP neural network method; (<b>c</b>) brittleness index prediction plane distribution diagram of Rickman method.</p> Full article ">Figure 14
<p>Comparison between predicted values and measured values of shale brittleness index seismic prediction based on BP neural network and Rickman method.</p> Full article ">Figure 15
<p>Comparative analysis of the brittleness index prediction results: (<b>a</b>) scatter plot of the predicted and true values of the brittleness index; (<b>b</b>) scatter plot of the relative errors of the brittleness index based on the BP neural network and the Rickman method.</p> Full article ">
20 pages, 4894 KiB  
Article
Optimization and Modification of Bacterial Cellulose Membrane from Coconut Juice Residues and Its Application in Carbon Dioxide Removal for Biogas Separation
by Wipawee Dechapanya, Kamontip Wongsuwan, Jonathon Huw Lewis and Attaso Khamwichit
Energies 2024, 17(18), 4750; https://doi.org/10.3390/en17184750 - 23 Sep 2024
Viewed by 817
Abstract
Driven by environmental and economic considerations, this study explores the viability of utilizing coconut juice residues (CJRs), a byproduct from coconut milk production, as a carbon source for bacterial cellulose (BC) synthesis in the form of a versatile bio-membrane. This work investigates the [...] Read more.
Driven by environmental and economic considerations, this study explores the viability of utilizing coconut juice residues (CJRs), a byproduct from coconut milk production, as a carbon source for bacterial cellulose (BC) synthesis in the form of a versatile bio-membrane. This work investigates the use of optimization modeling as a tool to find the optimal conditions for BC cultivation in consideration of waste minimization and resource sustainability. Optimization efforts focused on three parameters, including pH (4–6), cultivation temperature (20–30 °C), and time (6–10 days) using Design Expert (DE) V.13. The maximum yield of 9.31% (g/g) was achieved when the cultivation took place at the optimal conditions (pH 6, 30 °C, and 8 days). This approach aligns with circular economy principles, contributing to sustainable resource management and environmental impact reduction. The experimental and predicted optimal conditions from DE V.13 were in good agreement, validating the study’s outcomes. The predictive model gave the correlations of the optimal conditions in response to the highest yield and maximum eco-efficiency. The use of prediction modeling resulted in a useful tool for forecasting and obtaining guidelines that can assist other researchers in calculating optimal conditions for a desired yield. Acetylation of the BC resulted in cellulose acetate (CA) membranes. The CA membrane exhibited the potential to separate CO2 from a CH4/CO2 mixed gas with a CO2 selectivity of 1.315 in a membrane separation. The promising gas separation results could be further explored to be utilized in biogas purification applications. Full article
(This article belongs to the Special Issue Bio-Waste to Energy and Added Value Products – Challenges and Opportunities)
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<p>Schematic of synthesis of BC and CA membranes from CJRs, the cultivation mechanism pathways by <span class="html-italic">A. xylinum</span> were adapted from Khami et al. [<a href="#B24-energies-17-04750" class="html-bibr">24</a>].</p> Full article ">Figure 2
<p>Membrane separation unit.</p> Full article ">Figure 3
<p>(<b>a</b>) wet BC; (<b>b</b>) dry BC; (<b>c</b>) dry CA.</p> Full article ">Figure 4
<p>FTIR analysis of the BC and CA membrane [<a href="#B52-energies-17-04750" class="html-bibr">52</a>].</p> Full article ">Figure 5
<p>SEM results of the BC membrane sample with the highest yield of (<b>a</b>) surface, (<b>b</b>) cross-section at 10 k× magnification, (<b>c</b>) cross-section at 30 k× magnification.</p> Full article ">Figure 6
<p>SEM results of the BC membrane sample with the lowest yield of (<b>a</b>) surface, (<b>b</b>) cross-section at 10 k× magnification, (<b>c</b>) cross-section at 30 k× magnification</p> Full article ">Figure 7
<p>Normal percent probability of residuals versus externally studentized residuals of (<b>a</b>) dry BC yields and (<b>b</b>) eco-efficiency.</p> Full article ">Figure 8
<p>Response surface plot demonstrates the interaction effect of (<b>a</b>) pH and days; (<b>b</b>) pH and temperature; (<b>c</b>) days and temperature for dry yield BC.</p> Full article ">Figure 9
<p>Response surface plot demonstrates the interaction effect of (<b>a</b>) pH and days; (<b>b</b>) pH and temperature; (<b>c</b>) days and temperature for eco-efficiency.</p> Full article ">Figure 10
<p>Desirability ramps of dry BC yield and eco-efficiency.</p> Full article ">
12 pages, 10284 KiB  
Article
Research on Solid-State Linear Transformer Driver Power Source Driving Atmospheric Pressure Plasma Jet Treatment of Epoxy Resin
by Xiangnan Cao, Guiying Song, Yikai Chen and Haowei Chen
Energies 2024, 17(18), 4749; https://doi.org/10.3390/en17184749 - 23 Sep 2024
Viewed by 435
Abstract
The Solid-State Linear Transformer Driver (SSLTD) is a nanosecond pulse power source characterized by its fast rise time and adjustable output waveform. It can generate uniform and stable atmospheric plasma jets, which is suitable for material surface modification. In this study, a 15-stage [...] Read more.
The Solid-State Linear Transformer Driver (SSLTD) is a nanosecond pulse power source characterized by its fast rise time and adjustable output waveform. It can generate uniform and stable atmospheric plasma jets, which is suitable for material surface modification. In this study, a 15-stage SSLTD was designed and assembled, which can produce a stable nanosecond pulse voltage up to 15 times the amplitude of the charging voltage at high frequencies, with a rise time of approximately 10 ns. This device can be used to generate stable atmospheric pressure Ar plasma jets with an electron density in the range of 1015~1016 cm−3 and gas temperatures close to room temperature. After the modification treatment by the plasma jets, the content of the C=O groups on the surface of the epoxy resin significantly increased in the wavelength range of 1720~1740 cm−1, and its flashover resistance was noticeably enhanced. The optimal comprehensive modification effect was achieved at a charging voltage of 600 V, pulse width of 50 ns, and pulse frequency in the range of 800~1000 Hz. Full article
(This article belongs to the Section F: Electrical Engineering)
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<p>Principles and physical diagram of SSLTD: (<b>a</b>) equivalent circuit of SSLTD; (<b>b</b>) cross-sectional structure of the SSLTD module; (<b>c</b>) single SSLTD module; (<b>d</b>) pulse power generator with 15 SSLTD modules.</p> Full article ">Figure 2
<p>Schematic diagram of plasma jet modification experiment driven by the SSLTD.</p> Full article ">Figure 3
<p>Performance testing of SSLTD: (<b>a</b>) different charging voltages; (<b>b</b>) different pulse widths of the control signal; (<b>c</b>) different pulse frequencies of the control signal.</p> Full article ">Figure 4
<p>Images of plasma jets under different experimental conditions: (<b>a</b>) pulse width 50 ns, pulse frequency 1000 Hz, charging voltage 600~800 V; (<b>b</b>) pulse width 50 ns, charging voltage 600 V, pulse frequency 200~1000 Hz; (<b>c</b>) charging voltage 600 V, pulse frequency 1000 Hz, pulse width 50~100 ns.</p> Full article ">Figure 5
<p>Emission spectra of the plasma jet under different experimental conditions: (<b>a</b>) emission spectra of the plasma jet in the wavelength range of 193~980 nm; (<b>b</b>) Ar emission spectra with varying charging voltages; (<b>c</b>) Ar emission spectra with varying pulse frequencies; (<b>d</b>) Ar emission spectra with varying pulse widths.</p> Full article ">Figure 5 Cont.
<p>Emission spectra of the plasma jet under different experimental conditions: (<b>a</b>) emission spectra of the plasma jet in the wavelength range of 193~980 nm; (<b>b</b>) Ar emission spectra with varying charging voltages; (<b>c</b>) Ar emission spectra with varying pulse frequencies; (<b>d</b>) Ar emission spectra with varying pulse widths.</p> Full article ">Figure 6
<p>FTIR detection results of epoxy resin: (<b>a</b>) 600~800 V; (<b>b</b>) 200~1000 Hz; (<b>c</b>) 50~100 ns.</p> Full article ">Figure 7
<p>Comparison of modified areas of epoxy resin.</p> Full article ">Figure 8
<p>Flashover voltage Weibull distribution of epoxy resin: (<b>a</b>) 600~800 V; (<b>b</b>) 200~1000 Hz; (<b>c</b>) 50~100 ns.</p> Full article ">
25 pages, 4418 KiB  
Article
Two-Stage Optimal Configuration Strategy of Distributed Synchronous Condensers at the Sending End of Large-Scale Wind Power Generation Bases
by Lang Zhao, Zhidong Wang, Yizheng Li, Xueying Wang, Zhiyun Hu and Yunpeng Xiao
Energies 2024, 17(18), 4748; https://doi.org/10.3390/en17184748 - 23 Sep 2024
Viewed by 423
Abstract
The transmission end of large-scale wind power generation bases faces challenges such as high AC-DC coupling strength, low system inertia, and weak voltage support capabilities. Deploying distributed synchronous condensers (SCs) within and around wind farms can effectively provide transient reactive power support, enhance [...] Read more.
The transmission end of large-scale wind power generation bases faces challenges such as high AC-DC coupling strength, low system inertia, and weak voltage support capabilities. Deploying distributed synchronous condensers (SCs) within and around wind farms can effectively provide transient reactive power support, enhance grid system inertia at the transmission end, and improve dynamic frequency support capabilities. However, the high investment and maintenance costs of SCs hinder their large-scale deployment, necessitating the investigation of optimal SC configuration strategies at critical nodes in the transmission grid. Initially, a node inertia model was developed to identify weaknesses in dynamic frequency support, and a critical inertia constraint based on node frequency stability was proposed. Subsequently, a multi-timescale reactive power response model was formulated to quantify the impact on short-circuit ratio improvement and transient overvoltage suppression. Finally, a two-stage optimal configuration strategy for distributed SCs at the transmission end was proposed, considering dynamic frequency support and transient voltage stability. In the first stage, the optimal SC configuration aimed to maximize system inertia improvement per unit investment to meet dynamic frequency support requirements. In the second stage, the configuration results from the first stage were adjusted by incorporating constraints for enhancing the multiple renewable short-circuit ratio (MRSCR) and suppressing transient overvoltage. The proposed model was validated using the feeder grid of a large energy base in western China. The results demonstrate that the optimal configuration scheme effectively suppressed transient overvoltage at the generator end and significantly enhanced the system’s dynamic frequency support strength. Full article
(This article belongs to the Section A3: Wind, Wave and Tidal Energy)
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<p>Sending-end equivalent network of large energy base convergence area.</p> Full article ">Figure 2
<p>Simplified AC equivalent circuit after condenser access.</p> Full article ">Figure 3
<p>Solving process for the two-stage optimal configuration model of distributed SCs in Matlab software (version 2021a).</p> Full article ">Figure 4
<p>Wiring diagram for sending-end grid of large energy bases in China.</p> Full article ">Figure 5
<p>Coupling relationship between economic cost and node inertia enhancement.</p> Full article ">Figure 6
<p>Comparison of node inertia improvement before and after distributed regulator configuration. (<b>a</b>) Before the distributed SC configuration. (<b>b</b>) After the distributed SC configuration.</p> Full article ">Figure 7
<p>Frequency comparison before and after distributed SC configuration.</p> Full article ">Figure 8
<p>Voltage curves of nodes 31 and 36 before and after correction of the distributed SC. (<b>a</b>) The voltage curves of node31 before and after correction of the distributed SC. (<b>b</b>) The voltage curves of node36 before and after correction of the distributed SC.</p> Full article ">Figure 8 Cont.
<p>Voltage curves of nodes 31 and 36 before and after correction of the distributed SC. (<b>a</b>) The voltage curves of node31 before and after correction of the distributed SC. (<b>b</b>) The voltage curves of node36 before and after correction of the distributed SC.</p> Full article ">Figure 9
<p>Comparison of 42 and 45 transient voltage waveforms. (<b>a</b>) Transient voltage waveform at node 42. (<b>b</b>) Transient voltage waveform at node 45.</p> Full article ">Figure 9 Cont.
<p>Comparison of 42 and 45 transient voltage waveforms. (<b>a</b>) Transient voltage waveform at node 42. (<b>b</b>) Transient voltage waveform at node 45.</p> Full article ">Figure 10
<p>DC system reactive power output and demand.</p> Full article ">
29 pages, 41304 KiB  
Article
Study on Fast Temporal Prediction Method of Flame Propagation Velocity in Methane Gas Deflagration Experiment Based on Neural Network
by Xueqi Wang, Boqiao Wang, Kuai Yu, Wenbin Zhu, Jinnan Zhang and Bin Zhang
Energies 2024, 17(18), 4747; https://doi.org/10.3390/en17184747 - 23 Sep 2024
Viewed by 451
Abstract
To address the challenges of high experimental costs, complexity, and time consumption associated with pre-mixed combustible gas deflagration experiments under semi-open space obstacle conditions, a rapid temporal prediction method for flame propagation velocity based on Ranger-GRU neural networks is proposed. The deflagration experiment [...] Read more.
To address the challenges of high experimental costs, complexity, and time consumption associated with pre-mixed combustible gas deflagration experiments under semi-open space obstacle conditions, a rapid temporal prediction method for flame propagation velocity based on Ranger-GRU neural networks is proposed. The deflagration experiment data are employed as the training dataset for the neural network, with the coefficient of determination (R2) and mean squared error (MSE) used as evaluation metrics to assess the predictive performance of the network. First, 108 sets of pre-mixed methane gas deflagration experiments were conducted, varying obstacle parameters to investigate methane deflagration mechanisms under different conditions. The experimental results demonstrate that obstacle-to-ignition source distance, obstacle shape, obstacle length, obstacle quantity, and thick and fine wire mesh obstacles all significantly influence flame propagation velocity. Subsequently, the GRU neural network was trained, and different activation functions (Sigmoid, Relu, PReLU) and optimizers (Lookahead, RAdam, Adam, Ranger) were incorporated into the backpropagation updating process of the network. The training results show that the Ranger-GRU neural network based on the PReLU activation function achieves the highest mean R2 value of 0.96 and the lowest mean MSE value of 7.16759. Therefore, the Ranger-GRU neural network with PReLU activation function can be a viable rapid prediction method for flame propagation velocity in pre-mixed methane gas deflagration experiments under semi-open space obstacle conditions. Full article
(This article belongs to the Section I2: Energy and Combustion Science)
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<p>Experimental system.</p> Full article ">Figure 2
<p>Experimental equipment.</p> Full article ">Figure 3
<p>Flame diagram before and after grayscale.</p> Full article ">Figure 4
<p>GRU structure diagram.</p> Full article ">Figure 5
<p>Internal structure diagram of GRU neurons.</p> Full article ">Figure 6
<p>The structure of the proposed model.</p> Full article ">Figure 7
<p>Single obstacle peak flame velocity diagram: (<b>a</b>) Length 0.12 m. (<b>b</b>) Length 0.15 m.</p> Full article ">Figure 8
<p>The flame speed comparison diagram of rectangular obstacle with different ignition source distances.</p> Full article ">Figure 9
<p>The flame propagation velocity comparison diagram under different ignition source distance conditions with the same obstacle: (<b>a</b>) Length 0.12 m. (<b>b</b>) Length 0.15 m.</p> Full article ">Figure 10
<p>The flame speed comparison diagram of different shapes of obstacles with same ignition source distances.</p> Full article ">Figure 10 Cont.
<p>The flame speed comparison diagram of different shapes of obstacles with same ignition source distances.</p> Full article ">Figure 11
<p>Flame propagation velocity comparison diagram under same ignition source distance conditions with different shapes of the obstacle: (<b>a</b>) Length 0.12 m. (<b>b</b>) Length 0.15 m.</p> Full article ">Figure 12
<p>The peak flame velocity diagram for two obstacles condition: (<b>a</b>) Circular obstacle. (<b>b</b>) Rectangular obstacle. (<b>c</b>) Square obstacle.</p> Full article ">Figure 13
<p>The flame speed comparison diagram of different obstacle spacing with same obstacle shape and ignition source distances.</p> Full article ">Figure 14
<p>The flame propagation velocity comparison diagram under different distance between two obstacles condition.</p> Full article ">Figure 15
<p>The peak flame velocity diagram under conditions of the barbed wire obstacle and the circular obstacle.</p> Full article ">Figure 16
<p>The flame propagation velocity comparison diagram under conditions of the barbed wire obstacle and the circular obstacle.</p> Full article ">Figure 17
<p>The flame propagation velocity comparison diagram under conditions of coarse and fine barbed wire obstacles.</p> Full article ">Figure 18
<p>Comparison diagram of R<sup>2</sup> mean value for different numbers of neurons in the neural network.</p> Full article ">Figure 19
<p>Comparison diagram of R<sup>2</sup>, MSE, and predicted values for different neural networks on the test set.</p> Full article ">Figure 20
<p>Comparison diagram of different neural networks prediction trends.</p> Full article ">Figure 21
<p>Comparison diagram of R<sup>2</sup>, MSE, and predicted values for different activation functions on the test set.</p> Full article ">Figure 22
<p>Comparison diagram of different activation functions prediction trends.</p> Full article ">Figure 23
<p>Comparison diagram of R<sup>2</sup>, MSE, and predicted values for different optimizers on the test set.</p> Full article ">Figure 24
<p>Comparison diagram of different optimizers prediction trends.</p> Full article ">Figure 25
<p>Prediction trends of flame propagation velocity by Zhang et al. [<a href="#B24-energies-17-04747" class="html-bibr">24</a>].</p> Full article ">Figure 26
<p>Comparison diagram of R<sup>2</sup> and MSE error bar after deleting a category of input vectors.</p> Full article ">
19 pages, 3527 KiB  
Article
Do the Energy-Related Uncertainties Stimulate Renewable Energy Demand in Developed Economies? Fresh Evidence from the Role of Environmental Policy Stringency and Global Economic Policy Uncertainty
by Chafic Saliba
Energies 2024, 17(18), 4746; https://doi.org/10.3390/en17184746 - 23 Sep 2024
Viewed by 557
Abstract
Despite earlier research on green energy, there is still a significant gap in understanding how energy-related uncertainties affect renewable energy consumption (REN), especially in developed nations. Thus, this study explicitly looks into how the energy-related uncertainty index (EUI) can promote (or diminish) REN [...] Read more.
Despite earlier research on green energy, there is still a significant gap in understanding how energy-related uncertainties affect renewable energy consumption (REN), especially in developed nations. Thus, this study explicitly looks into how the energy-related uncertainty index (EUI) can promote (or diminish) REN in sixteen wealthy nations between 2000 and 2020. Furthermore, we attempt to specify the factors of REN and explore whether environmental policy stringency (EPS) and global economic policy uncertainty (GEPU) could help moderate (or intensify) the EUI-REN nexus. To achieve this, we employ different panel data methods. The results underscore that the EUI significantly impacts REN, denoting that higher uncertainties related to energy markets lead to promoting REN. Additionally, the (EUI × EPS) underlines that EPS has a favorable role in increasing the positive effect of the EUI on REN in sample developed countries while (EUI × GEPU) has a detrimental effect. Remarkably, the findings underline that the effect of the EUI on REN is more positive in high EPS countries and that the positive effect of the EUI is more moderate when GEPU is high. The findings also underscore that the development of the financial market, FDI, personal remittances, and EPS positively stimulate REN whereas CO2, total natural resources rents, economic activity, and GEPU have a detrimental impact. The results are robust, and authorities and policymakers are advised to implement a wide range of policy proposals to accomplish sustainable development goals (SDGs) 7 and 13. Full article
(This article belongs to the Special Issue Energy Economics, Finance and Policy Towards Sustainable Energy)
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<p>Global total energy supply by source (2021). Authors’ calculations (Source: <a href="https://www.iea.org/data-and-statistics/" target="_blank">https://www.iea.org/data-and-statistics/</a>, accessed on 1 June 2024).</p> Full article ">Figure 2
<p>Total RENE supply by developing and developed countries (2021); Authors’ calculations (Source: <a href="https://www.iea.org/data-and-statistics/" target="_blank">https://www.iea.org/data-and-statistics/</a>, accessed on 1 June 2024).</p> Full article ">Figure 3
<p>Variables’ Expected signs.</p> Full article ">Figure 4
<p>Scatterplot matrix.</p> Full article ">Figure 5
<p>Panel (<b>A</b>,<b>B</b>) Time series plot of Ln(REN), Ln(EUI), Ln(EPS), and Ln(GEPU) for the years 2000–2020.</p> Full article ">Figure 5 Cont.
<p>Panel (<b>A</b>,<b>B</b>) Time series plot of Ln(REN), Ln(EUI), Ln(EPS), and Ln(GEPU) for the years 2000–2020.</p> Full article ">Figure 6
<p>Panel (<b>A</b>): Average of Ln(REN) among the selected developed countries. Panel (<b>B</b>): Average of Ln(EUI) among the selected developed countries.</p> Full article ">
17 pages, 1735 KiB  
Article
Environmental and Social Life Cycle Assessment of Data Centre Heat Recovery Technologies Combined with Fuel Cells for Energy Generation
by Camila Andrea Puentes Bejarano, Javier Pérez Rodríguez, Juan Manuel de Andrés Almeida, David Hidalgo-Carvajal, Jonas Gustaffson, Jon Summers and Alberto Abánades
Energies 2024, 17(18), 4745; https://doi.org/10.3390/en17184745 - 23 Sep 2024
Viewed by 916
Abstract
The energy sector is essential in the transition to a more sustainable future, and renewable energies will play a key role in achieving this. It is also a sector in which the circular economy presents an opportunity for the utilisation of other resources [...] Read more.
The energy sector is essential in the transition to a more sustainable future, and renewable energies will play a key role in achieving this. It is also a sector in which the circular economy presents an opportunity for the utilisation of other resources and residual energy flows. This study examines the environmental and social performance of innovative energy technologies (which contribute to the circularity of resources) implemented in a demonstrator site in Luleå (Sweden). The demo-site collected excess heat from a data centre to cogenerate energy, combining the waste heat with fuel cells that use biogas derived from waste, meeting part of its electrical demand and supplying thermal energy to an existing district heating network. Following a cradle-to-gate approach, an environmental and a social life cycle assessment were developed to compare two scenarios: a baseline scenario reflecting current energy supply methods and the WEDISTRICT scenario, which considers the application of different renewable and circular technologies. The findings indicate that transitioning to renewable energy sources significantly reduces environmental impacts in seven of the eight assessed impact categories. Specifically, the study showed a 48% reduction in climate change impact per kWh generated. Additionally, the WEDISTRICT scenario, accounting for avoided burdens, prevented 0.21 kg CO2 eq per kWh auto-consumed. From the social perspective, the WEDISTRICT scenario demonstrated improvement in employment conditions within the worker and local community categories, product satisfaction within the society category, and fair competition within the value chain category. Projects like WEDISTRICT demonstrate the circularity options of the energy sector, the utilisation of resources and residual energy flows, and that these lead to environmental and social improvements throughout the entire life cycle, not just during the operation phase. Full article
(This article belongs to the Special Issue Circular Economy and Life Cycle Assessment Approach for Energy Systems)
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<p>Technologies implemented in the Luleå demonstrator. System boundaries definition of baseline and WEDISTRICT scenario.</p> Full article ">Figure 2
<p>Environmental impact results comparing the baseline scenario against the WEDISTRICT scenario (the baseline scenario impacts are equal to 100).</p> Full article ">Figure 3
<p>Environmental impact results comparing the baseline scenario against the WEDISTRICT scenario and the WEDISTRICT scenario with the avoided burdens included (the baseline scenario impacts are equal to 100).</p> Full article ">
19 pages, 8368 KiB  
Article
Effect of Ferrite Core Modification on Electromagnetic Force Considering Spatial Harmonics in an Induction Cooktop
by Sangjin Lee, Gyeonghwan Yun, Grace Firsta Lukman, Jang-Mok Kim, Tae-Hoon Kim and Cheewoo Lee
Energies 2024, 17(18), 4744; https://doi.org/10.3390/en17184744 - 23 Sep 2024
Viewed by 510
Abstract
This study investigates the influence of ferrite shape modifications on the performance and noise characteristics of an induction cooktop. The goal is to optimize the air gap dimensions between ferrites and cookware, enhancing efficiency while managing noise levels. Using finite element method (FEM) [...] Read more.
This study investigates the influence of ferrite shape modifications on the performance and noise characteristics of an induction cooktop. The goal is to optimize the air gap dimensions between ferrites and cookware, enhancing efficiency while managing noise levels. Using finite element method (FEM) simulations, we analyze the spatial distribution of magnetic forces and their harmonics. Eight ferrite shape models were examined, focusing on both outer and inner air gaps. Model #8 (reduced outer air gap) and Model #9 (reduced inner air gap) were experimentally validated. Noise measurements indicated that Model #8 reduced 120 Hz harmonic noise components, while Model #9 increased them due to enhanced excitation forces. Current measurements confirmed that Model #9 achieved higher efficiency, with RMS current reduced to 94.54% of the base model. The study reveals a trade-off between performance and noise: inner air gap reduction significantly boosts efficiency but raises noise levels, whereas outer air gap reduction offers balanced improvements. These findings provide insights for optimizing induction cooktop designs, aiming for quieter operation without compromising efficiency. Full article
(This article belongs to the Special Issue Design and Optimization of High-Speed Permanent Magnet Synchronous Machines)
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<p>Induction cooktop configuration.</p> Full article ">Figure 2
<p>Heat generation process in an induction cooktop.</p> Full article ">Figure 3
<p>(<b>a</b>) Simplified simulation model of an induction cooktop; (<b>b</b>) mesh structure of the simulation model.</p> Full article ">Figure 4
<p>(<b>a</b>) Ferrite core shape of the base model, (<b>b</b>) ohmic loss density distribution of the base model.</p> Full article ">Figure 5
<p>Air gap flux density: (<b>a</b>) large ferrite region, (<b>b</b>) small ferrite region, (<b>c</b>) non-ferrite region.</p> Full article ">Figure 6
<p>Ferrite core shapes (<b>a</b>) Model #1, (<b>b</b>) Model #2, (<b>c</b>) Model #3, (<b>d</b>) Model #4, (<b>e</b>) Model #5, (<b>f</b>) Model #6, (<b>g</b>) Model #7.</p> Full article ">Figure 7
<p>Ohmic loss density distribution according to ferrite core shape (<b>a</b>) Model #1, (<b>b</b>) Model #2, (<b>c</b>) Model #3, (<b>d</b>) Model #4, (<b>e</b>) Model #5, (<b>f</b>) Model #6, (<b>g</b>) Model #7.</p> Full article ">Figure 7 Cont.
<p>Ohmic loss density distribution according to ferrite core shape (<b>a</b>) Model #1, (<b>b</b>) Model #2, (<b>c</b>) Model #3, (<b>d</b>) Model #4, (<b>e</b>) Model #5, (<b>f</b>) Model #6, (<b>g</b>) Model #7.</p> Full article ">Figure 8
<p>Forces in the air gap of the induction cooktop.</p> Full article ">Figure 9
<p>Force generation in the induction cooktop.</p> Full article ">Figure 10
<p>Vibration modes of a circular membrane with fixed edges.</p> Full article ">Figure 11
<p>Experimental setup for measuring current and noise.</p> Full article ">Figure 12
<p>FFT analysis of noise from the base model.</p> Full article ">Figure 13
<p>Actual current waveform.</p> Full article ">Figure 14
<p>Process of harmonic analysis using magnetic flux density in the air gap.</p> Full article ">Figure 15
<p>Spatial harmonic analysis of the base model.</p> Full article ">Figure 16
<p>Comparison of spatial harmonics for different ferrite shapes: (<b>a</b>) Base vs. Model #1, (<b>b</b>) Base vs. Model #2, 3, (<b>c</b>) Base vs. Model #4, 5, (<b>d</b>) Base vs. Model #6, 7.</p> Full article ">Figure 17
<p>Components for securing the coil and ferrite in the base model.</p> Full article ">Figure 18
<p>New ferrite model: (<b>a</b>) modeling of Model #8, (<b>b</b>) modeling of Model #9, (<b>c</b>) prototype of Model #8, (<b>d</b>) prototype of Model #9.</p> Full article ">Figure 19
<p>Noise comparison: (<b>a</b>) Base Model vs. Model #8, (<b>b</b>) Base Model vs. Model #9.</p> Full article ">Figure 20
<p>Current measurement: (<b>a</b>) Model #8, (<b>b</b>) Model #9.</p> Full article ">
13 pages, 1909 KiB  
Article
A Single-Phase Ground Fault Line Selection Method in Active Distribution Networks Based on Transformer Grounding Mode Modification
by Shi Su, Qingyang Xie, Pengfei Ma, Yuan Li, Fahui Chen, Jing Zhang, Botong Li and Changqi Wang
Energies 2024, 17(18), 4743; https://doi.org/10.3390/en17184743 - 23 Sep 2024
Viewed by 518
Abstract
Reliable fault line selection technology is crucial for preventing fault range expansion and ensuring the reliable operation of distribution networks. Modern distribution systems with neutral earthing via arc extinguishing coil face challenges during single-phase ground faults due to indistinct fault characteristics and system [...] Read more.
Reliable fault line selection technology is crucial for preventing fault range expansion and ensuring the reliable operation of distribution networks. Modern distribution systems with neutral earthing via arc extinguishing coil face challenges during single-phase ground faults due to indistinct fault characteristics and system sequence networks influenced by the grounding methods on the distributed generation side. These factors increase the difficulty of fault line selection. By analyzing the differences between the zero-sequence currents of feeder lines and neutral lines in active distribution networks with neutral earthing via arc extinguishing coil, a method for single-phase ground fault line selection has been proposed in this paper. This method involves switching from a neutral point ungrounded mode to a low-resistance neutral grounding mode using distributed generation grid-connected transformers under permanent fault conditions. Criteria based on the differences in zero-sequence current ratios before and after the grounding mode switch are established. Simulation validation using the Power Systems Computer Aided Design (PSCAD) platform has been conducted. The proposed method demonstrates strong tolerance to transition resistance, simple extraction of fault characteristic signals, and accurate fault line selection results. Full article
(This article belongs to the Section F3: Power Electronics)
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<p>Diagram of a 10 kV DNs with DGs.</p> Full article ">Figure 2
<p>Zero-sequence equivalent circuit for a SPGF in a 10 kV DNs.</p> Full article ">Figure 3
<p>Diagram of the modified 10 kV DNs.</p> Full article ">Figure 4
<p>Zero-sequence equivalent circuit for a SPGF in the 10 kV DNs after modification.</p> Full article ">Figure 5
<p>Flowchart of the proposed FLS method.</p> Full article ">Figure 6
<p>Metallic single-phase ground fault on modified line3 (<b>a</b>) Zero-sequence currents of each line; (<b>b</b>) Ratio of zero-sequence currents between feeders and neutral point.</p> Full article ">Figure 7
<p>Metallic single-phase ground fault on non-modified line4 (<b>a</b>) Zero-sequence currents of each line; (<b>b</b>) Ratio of zero-sequence currents between feeders and neutral point.</p> Full article ">Figure 8
<p>Metallic single-phase ground fault on bus (<b>a</b>) Zero-sequence currents of each line; (<b>b</b>) Ratio of zero-sequence currents between feeders and neutral point.</p> Full article ">
24 pages, 5081 KiB  
Article
A 24-Step Short-Term Power Load Forecasting Model Utilizing KOA-BiTCN-BiGRU-Attentions
by Mingshen Xu, Wanli Liu, Shijie Wang, Jingjia Tian, Peng Wu and Congjiu Xie
Energies 2024, 17(18), 4742; https://doi.org/10.3390/en17184742 - 23 Sep 2024
Viewed by 741
Abstract
With the global objectives of achieving a “carbon peak” and “carbon neutrality” along with the implementation of carbon reduction policies, China’s industrial structure has undergone significant adjustments, resulting in constraints on high-energy consumption and high-emission industries while promoting the rapid growth of green [...] Read more.
With the global objectives of achieving a “carbon peak” and “carbon neutrality” along with the implementation of carbon reduction policies, China’s industrial structure has undergone significant adjustments, resulting in constraints on high-energy consumption and high-emission industries while promoting the rapid growth of green industries. Consequently, these changes have led to an increasingly complex power system structure and presented new challenges for electricity demand forecasting. To address this issue, this study proposes a 24-step multivariate time series short-term load forecasting algorithm model based on KNN data imputation and BiTCN bidirectional temporal convolutional networks combined with BiGRU bidirectional gated recurrent units and attention mechanism. The Kepler adaptive optimization algorithm (KOA) is employed for hyperparameter optimization to effectively enhance prediction accuracy. Furthermore, using real load data from a wind farm in Xinjiang as an example, this paper predicts the electricity load from 1 January to 30 December in 2019. Experimental results demonstrate that our comprehensive short-term load forecasting model exhibits lower prediction errors and superior performance compared to traditional methods, thus holding great value for practical applications. Full article
(This article belongs to the Section F: Electrical Engineering)
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<p>Possible positions in (<b>a</b>) 2-dimension and (<b>b</b>) 3-dimension.</p> Full article ">Figure 2
<p>Flow chart KOA optimization algorithm.</p> Full article ">Figure 3
<p>Diagram of the BiGRU structure.</p> Full article ">Figure 4
<p>BiTCN module structure.</p> Full article ">Figure 5
<p>Attention mechanism.</p> Full article ">Figure 6
<p>Structure of KOA-BiGRU-BiTCN-attention model.</p> Full article ">Figure 7
<p>Imputation of missing values for the KNN model.</p> Full article ">Figure 8
<p>Correlation matrix and scatter plots.</p> Full article ">Figure 9
<p>Visualization of the performance of ablation experiments visualization.</p> Full article ">Figure 10
<p>Visualization of prediction results of different optimization algorithms.</p> Full article ">Figure 11
<p>Visualization of comparison of classical algorithm predictions.</p> Full article ">Figure 12
<p>Visualization of performance improvement.</p> Full article ">
23 pages, 5406 KiB  
Article
A Transient Damping Improvement Strategy for Enhancing Grid-Connected Active Power Response Performance of VSG
by Yan Xia, Yang Chen, Renzhao Chen, Ke Li, Huizhu Li, Jinhui Shi and Yiqiang Yang
Energies 2024, 17(18), 4741; https://doi.org/10.3390/en17184741 - 23 Sep 2024
Viewed by 511
Abstract
The given power and grid frequency disturbances can cause transient oscillations and steady-state deviations in the output power of a virtual synchronous generator (VSG), which can be effectively addressed by adding transient damping. However, this approach may result in significant power overshoot. This [...] Read more.
The given power and grid frequency disturbances can cause transient oscillations and steady-state deviations in the output power of a virtual synchronous generator (VSG), which can be effectively addressed by adding transient damping. However, this approach may result in significant power overshoot. This article proposes an improved VSG control strategy based on transient electromagnetic power feedback compensation and a small-signal model reduction scheme. Firstly, the grid-connected active closed-loop small-signal models of typical VSG control and transient damping VSG control are established, respectively. The transient oscillation suppression mechanism of active power is revealed through root locus and frequency response analyses, and the power overshoot characteristics of the two control strategies are analysed by combining them with the system of zero points. Secondly, the active transient feedback compensation method and the small-signal model reduction design method are introduced in detail. Finally, comparative analysis experiments are conducted using the Matlab/Simulink and hardware-in-the-loop experimental platform. It is verified that the proposed control strategy can suppress transient oscillations in active power, prevent steady-state deviations, and effectively mitigate the power overshoot problem of the system. Full article
(This article belongs to the Section F3: Power Electronics)
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<p>TVSG topology and its control methods.</p> Full article ">Figure 2
<p>Small-signal control block diagram of TVSG.</p> Full article ">Figure 3
<p>The trend of pole distribution in APL small-signal model of TVSG.</p> Full article ">Figure 4
<p>Transient response characteristics of TVSG under different control parameters: (<b>a</b>) The response curve of <span class="html-italic">G</span><sub>PP</sub>(<span class="html-italic">s</span>) (<span class="html-italic">D</span><sub>ω</sub> = 7.6); (<b>b</b>) The response curve of <span class="html-italic">G</span><sub>Pω</sub>(<span class="html-italic">s</span>) (<span class="html-italic">D</span><sub>ω</sub> = 7.6); (<b>c</b>) The response curve of <span class="html-italic">G</span><sub>PP</sub>(<span class="html-italic">s</span>) (<span class="html-italic">J</span> = 1.01); (<b>d</b>) The response curve of <span class="html-italic">G</span><sub>Pω</sub>(<span class="html-italic">s</span>) (<span class="html-italic">J</span> = 1.01).</p> Full article ">Figure 5
<p>Small-signal model control block diagram of TDP-VSG.</p> Full article ">Figure 6
<p>The trend of pole distribution in APL small-signal model of TDP-VSG.</p> Full article ">Figure 7
<p>Transient response characteristics of TDP-VSG under different control parameters: (<b>a</b>) The response curve of <span class="html-italic">G</span><sub>PD</sub>(<span class="html-italic">s</span>) (<span class="html-italic">T</span><sub>d</sub> = 0.5); (<b>b</b>) The response curve of <span class="html-italic">G</span><sub>ωD</sub>(<span class="html-italic">s</span>) (<span class="html-italic">T</span><sub>d</sub> = 0.5); (<b>c</b>) The response curve of <span class="html-italic">G</span><sub>PD</sub>(<span class="html-italic">s</span>) (<span class="html-italic">D</span><sub>s</sub> = 15); (<b>d</b>) The response curve of <span class="html-italic">G</span><sub>ωD</sub>(<span class="html-italic">s</span>) (<span class="html-italic">D</span><sub>s</sub> = 15).</p> Full article ">Figure 8
<p>Small-signal model control block diagram of APFBC-VSG.</p> Full article ">Figure 9
<p>The trend of pole distribution in APL small-signal model of APFBC-VSG: (<b>a</b>) <span class="html-italic">T</span><sub>FB</sub> = 0.004, <span class="html-italic">K</span><sub>FB</sub> increases from 0 to 20; (<b>b</b>) <span class="html-italic">K</span><sub>FB</sub> = 2, <span class="html-italic">T</span><sub>FB</sub> increases from 0 to 20.</p> Full article ">Figure 10
<p>Transient response characteristics of APFBC-VSG under different control parameters: (<b>a</b>) The response curve of <span class="html-italic">G</span><sub>PD</sub>(<span class="html-italic">s</span>) (<span class="html-italic">T</span><sub>FB</sub> = 0.006); (<b>b</b>) The response curve of <span class="html-italic">G</span><sub>ωD</sub>(<span class="html-italic">s</span>) (<span class="html-italic">T</span><sub>FB</sub> = 0.006); (<b>c</b>) The response curve of <span class="html-italic">G</span><sub>PD</sub>(<span class="html-italic">s</span>) (<span class="html-italic">K</span><sub>FB</sub> = 2); (<b>d</b>) The response curve of <span class="html-italic">G</span><sub>ωD</sub>(<span class="html-italic">s</span>) (<span class="html-italic">K</span><sub>FB</sub> = 2).</p> Full article ">Figure 11
<p>Step response characteristics of the system before and after order reduction: (<b>a</b>) the response curve with a change in <span class="html-italic">T</span><sub>FB</sub>; (<b>b</b>) the response curve with a change in <span class="html-italic">K</span><sub>FB</sub>.</p> Full article ">Figure 12
<p>Simulation results of fixed damping control under different <span class="html-italic">D</span><sub>ω</sub>.</p> Full article ">Figure 13
<p>Simulation results of TDP-VSG control under different transient damping coefficients.</p> Full article ">Figure 14
<p>Simulation results of APFBC-VSG control under different compensation coefficients.</p> Full article ">Figure 15
<p>Simulation results under different VSG control strategies: (<b>a</b>) active power output characteristic simulation curve; (<b>b</b>) frequency output characteristic simulation curve.</p> Full article ">Figure 16
<p>Simulation results of grid-connected output current under different VSG control strategies: (<b>a</b>) The simulation output three phase current of TVSG (<span class="html-italic">D</span><sub>ω</sub> = 0); (<b>b</b>) The simulation output three phase current of TVSG (<span class="html-italic">D</span><sub>ω</sub> = 20); (<b>c</b>) The simulation output three phase current of TDP-VSG (<span class="html-italic">D</span><sub>s</sub> = 30); (<b>d</b>) The simulation output three phase current of APFBC-VSG (<span class="html-italic">K</span><sub>FB</sub> = 15).</p> Full article ">Figure 17
<p>HIL hardware experiment platform.</p> Full article ">Figure 18
<p>Experimental results: (<b>a</b>) comparative experimental results of different control strategies under given power disturbance; (<b>b</b>) Comparative experimental results of different control strategies under grid frequency drop.</p> Full article ">
27 pages, 4788 KiB  
Article
Assessment of the Profitability of a Photovoltaic Installation Cooperating with Energy Storage Using an Example of a Medium-Sized Production Company
by Jerzy Mikulik and Mariusz Niekurzak
Energies 2024, 17(18), 4740; https://doi.org/10.3390/en17184740 - 23 Sep 2024
Viewed by 891
Abstract
This work aims to comprehensively analyze the cooperation of an electricity storage facility with an operating photovoltaic installation in a manufacturing company regarding the efficiency and effectiveness of the device and the economic profitability of the investment. This work aims to check the [...] Read more.
This work aims to comprehensively analyze the cooperation of an electricity storage facility with an operating photovoltaic installation in a manufacturing company regarding the efficiency and effectiveness of the device and the economic profitability of the investment. This work aims to check the benefits that can be brought by expanding the PV system with an electricity storage facility. Based on the real energy balance and the characteristics of electricity distribution in the company, profitability calculations were carried out reflecting the expected savings generated by using individual solutions. These methods allowed the authors to calculate the market value of the investment with the assumed boundary criteria and to determine the economic effectiveness of the investment. Additionally, the practical process of selecting an electricity storage facility was presented and key moments in the company’s energy report were analyzed, in which the use of a battery could bring results. Calculations showed that supplementing the described PV installation with an energy storage facility will increase the current level of self-consumption of PV energy by over 14%. The benefits translate into the final effect of energy storage operation, which brings additional annual savings for the company of approximately EUR 23,000 in the case of a weaker device and roughly EUR 40,000 in the case of a more powerful energy storage device. The proposed research could improve the planning of new industrial plants for photovoltaic installations, as well as the redesign of existing ones. Full article
(This article belongs to the Special Issue Development of Energy Harvesting Systems and Methods from Uncommon Sources)
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<p>Flowchart of the research methodology.</p> Full article ">Figure 2
<p>Exhibition of PV installation modules placed on the company’s roofs.</p> Full article ">Figure 3
<p>Monthly characteristics of active power consumption (from the network) in the enterprise.</p> Full article ">Figure 4
<p>Monthly production of electricity by the PV system over the entire period of operation.</p> Full article ">Figure 5
<p>Monthly electricity production of the PV system—comparison every year.</p> Full article ">Figure 6
<p>Electricity production by the PV installation in a given year and total production over the entire period of operation.</p> Full article ">Figure 7
<p>Total electricity consumption in the company.</p> Full article ">Figure 8
<p>Electricity delivered to the distribution network is produced by a PV installation.</p> Full article ">Figure 9
<p>Percentage distribution of electricity produced by PV.</p> Full article ">Figure 10
<p>Electricity balance—the entire period of operation of the PV system.</p> Full article ">Figure 11
<p>Electricity consumption and delivery to the network in 15 min cycles from 11 May to 17 May 2024.</p> Full article ">Figure 12
<p>Consumption, delivery, and production of electricity in 15 min cycles from 11 May to 14 May 2024.</p> Full article ">Figure 13
<p>Electricity balance in 15 min cycles on 14 May 2024.</p> Full article ">Figure 14
<p>Electricity balance in 15 min cycles on 17 May 2024.</p> Full article ">
19 pages, 15139 KiB  
Article
Ultra-Short-Term Photovoltaic Power Prediction by NRGA-BiLSTM Considering Seasonality and Periodicity of Data
by Hong Wu, Haipeng Liu, Huaiping Jin and Yanping He
Energies 2024, 17(18), 4739; https://doi.org/10.3390/en17184739 - 23 Sep 2024
Cited by 1 | Viewed by 829
Abstract
Photovoltaic (PV) power generation is highly stochastic and intermittent, which poses a challenge to the planning and operation of existing power systems. To enhance the accuracy of PV power prediction and ensure the safe operation of the power system, a novel approach based [...] Read more.
Photovoltaic (PV) power generation is highly stochastic and intermittent, which poses a challenge to the planning and operation of existing power systems. To enhance the accuracy of PV power prediction and ensure the safe operation of the power system, a novel approach based on seasonal division and a periodic attention mechanism (PAM) for PV power prediction is proposed. First, the dataset is divided into three components of trend, period, and residual under fuzzy c-means clustering (FCM) and the seasonal decomposition (SD) method according to four seasons. Three independent bidirectional long short-term memory (BiLTSM) networks are constructed for these subsequences. Then, the network is optimized using the improved Newton–Raphson genetic algorithm (NRGA), and the innovative PAM is added to focus on the periodic characteristics of the data. Finally, the results of each component are summarized to obtain the final prediction results. A case study of the Australian DKASC Alice Spring PV power plant dataset demonstrates the performance of the proposed approach. Compared with other paper models, the MAE, RMSE, and MAPE performance evaluation indexes show that the proposed approach has excellent performance in predicting output power accuracy and stability. Full article
(This article belongs to the Section A2: Solar Energy and Photovoltaic Systems)
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<p>Framework of the proposed model.</p> Full article ">Figure 2
<p>PV active power profile of DKASC.</p> Full article ">Figure 3
<p>Heat map of Pearson coefficient results.</p> Full article ">Figure 4
<p>Sequence of characteristics of different seasons after seasonal division (<b>a</b>) spring, (<b>b</b>) summer, (<b>c</b>) autumn, (<b>d</b>) winter.</p> Full article ">Figure 5
<p>The decomposition results of spring feature F0. (<b>a</b>) Trend component, (<b>b</b>) period component, (<b>c</b>) residual component.</p> Full article ">Figure 6
<p>Predictive effectiveness of each model under different algorithms.</p> Full article ">
23 pages, 2142 KiB  
Article
Identifying Critical Failures in PV Systems Based on PV Inverters’ Monitoring Unit: A Techno-Economic Analysis
by Filipe Monteiro, Eduardo Sarquis and Paulo Branco
Energies 2024, 17(18), 4738; https://doi.org/10.3390/en17184738 - 23 Sep 2024
Viewed by 817
Abstract
Recent advancements in power electronics have significantly improved photovoltaic (PV) inverters by equipping them with sophisticated monitoring capabilities. These enhancements provide economic advantages by facilitating swift failure detection and lowering monitoring costs. Educating users on the economic repercussions of undetected failures in specific [...] Read more.
Recent advancements in power electronics have significantly improved photovoltaic (PV) inverters by equipping them with sophisticated monitoring capabilities. These enhancements provide economic advantages by facilitating swift failure detection and lowering monitoring costs. Educating users on the economic repercussions of undetected failures in specific inverter monitoring systems is crucial. This paper introduces a novel metric, “Cost of Detection”, which assesses the financial impact of failures, considering the repair expenses and the “quality” of the monitoring system in place. The study analyzed fifteen inverter monitoring solutions, focusing on the variance in alerts generated by the manufacturers’ standard and extra monitoring features. Employing the Failure Mode and Effects Analysis (FMEA) method, alerts were prioritized based on their importance for two PV system scenarios: a low-power residential system (5 kWp) and a medium-power industrial/commercial system (100 kWp). Lisbon, Rome, and Berlin were chosen as the locations for these systems. The economic impact of system failures is evaluated annually for each capacity and city. Given the differing costs and annual yields, comparing their economic performance over time is essential. This comparison utilizes the Net Present Value (NPV), which estimates an investment’s worth by calculating the present value of all cash flows. The investment assessment includes only the costs of inverters and optimizers, excluding O&M expenses, licenses, and fees. Over five years, a higher NPV signifies a more economically advantageous solution. For residential systems, string inverters with optimizers have the highest NPV, surpassing those without optimizers by 17% across all three cities. The optimal monitoring solution in the industrial/commercial context was a string inverter with one optimizer for every two panels. Here, Rome emerged as the location with the most substantial NPV increase of 50%, followed by Berlin with 33% and Lisbon with 28%. Full article
(This article belongs to the Special Issue Advances in Photovoltaic Solar Energy II)
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<p>Percentage of PV inverters reporting AC-related failure alerts.</p> Full article ">Figure 2
<p>Percentage of inverters that report internal failure alerts.</p> Full article ">Figure 3
<p>Percentage of inverters that report DC-related failure alerts.</p> Full article ">Figure 4
<p>Flowchart describing the steps to execute the FMEA methodology.</p> Full article ">Figure 5
<p>Flowchart describing the steps to calculate the stoppage cost due to some particular failure in a PV system.</p> Full article ">Figure 6
<p>Diagram of the residential PV system.</p> Full article ">Figure 7
<p>Industrial/commercial PV system diagram.</p> Full article ">
31 pages, 3212 KiB  
Review
A Comprehensive Review of Energy-Efficient Techniques for UAV-Assisted Industrial Wireless Networks
by Yijia Zhang, Ruotong Zhao, Deepak Mishra and Derrick Wing Kwan Ng
Energies 2024, 17(18), 4737; https://doi.org/10.3390/en17184737 - 23 Sep 2024
Viewed by 818
Abstract
The rapid expansion of the Industrial Internet-of-Things (IIoT) has spurred significant research interest due to the growth of security-aware, vehicular, and time-sensitive applications. Unmanned aerial vehicles (UAVs) are widely deployed within wireless communication systems to establish rapid and reliable links between users and [...] Read more.
The rapid expansion of the Industrial Internet-of-Things (IIoT) has spurred significant research interest due to the growth of security-aware, vehicular, and time-sensitive applications. Unmanned aerial vehicles (UAVs) are widely deployed within wireless communication systems to establish rapid and reliable links between users and devices, attributed to their high flexibility and maneuverability. Leveraging UAVs provides a promising solution to enhance communication system performance and effectiveness while overcoming the unprecedented challenges of stringent spectrum limitations and demanding data traffic. However, due to the dramatic increase in the number of vehicles and devices in the industrial wireless networks and limitations on UAVs’ battery storage and computing resources, the adoption of energy-efficient techniques is essential to ensure sustainable system implementation and to prolong the lifetime of the network. This paper provides a comprehensive review of various disruptive methodologies for addressing energy-efficient issues in UAV-assisted industrial wireless networks. We begin by introducing the background of recent research areas from different aspects, including security-enhanced industrial networks, industrial vehicular networks, machine learning for industrial communications, and time-sensitive networks. Our review identifies key challenges from an energy efficiency perspective and evaluates relevant techniques, including resource allocation, UAV trajectory design and wireless power transfer (WPT), across various applications and scenarios. This paper thoroughly discusses the features, strengths, weaknesses, and potential of existing works. Finally, we highlight open research issues and gaps and present promising potential directions for future investigation. Full article
(This article belongs to the Topic Innovation and Inventions in Aerospace and UAV Applications)
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<p>The Overall Paper Structure.</p> Full article ">Figure 2
<p>Backscatter-aided V2I communication.</p> Full article ">Figure 3
<p>Energy-efficient UAV-assisted networks.</p> Full article ">Figure 4
<p>Energy efficient UAV-assisted industrial wireless networks.</p> Full article ">
24 pages, 747 KiB  
Article
Quantifying Agricultural Residues Biomass Resources and the Energy Potentials with Characterization of Their Nature and Ethiopian Case Consumption Inference
by Angesom Gebrezgabiher Tesfay, Asfafaw Haileselassie Tesfay and Muyiwa Samuel Adaramola
Energies 2024, 17(18), 4736; https://doi.org/10.3390/en17184736 - 23 Sep 2024
Viewed by 541
Abstract
As the Ethiopian energy demand urges for fuel options, it is essential to identify biomass fuels and estimate their energy potential. This study quantified the agricultural residues’ biomass resources and their energy potential. Further analyzed and characterized the potential nature through quantitative and [...] Read more.
As the Ethiopian energy demand urges for fuel options, it is essential to identify biomass fuels and estimate their energy potential. This study quantified the agricultural residues’ biomass resources and their energy potential. Further analyzed and characterized the potential nature through quantitative and qualitative methodologies with descriptive, comparative, explanatory, and exploratory studies. Five-year crop yield data of 27 crops were collected from the Central Statistical Agency of Ethiopia. Conversion factors into energy were surveyed from the literature. Subsequently, the residues available and their energy potentials were estimated. Mathematical and statistical analysis methods were considered in an Excel sheet. A new measure of natural potential capacity for energy was defined in two views (resource and application). Accordingly, their potential capacities were rated and prioritized comparatively. The gross energy potential of all the residues was estimated to be 494.7 PJ. With 30% collecting efficiency, it corresponds to the imported petroleum fuel in 2018. Five major crops contributed to 80% of this gross potential. Maize and sorghum presented the highest potential due to their superior yields and good natural potential capacities. They are also well distributed in all the regions. Cotton and maize’s natural potential capacities are the best in both views. Generally, commercial crops presented better capacities than the major cereal crops. However, major crops’ energy potentials dominated due to their yields. These resources need mobilization into modern and commercially accessible fuel forms that await intervention. Densified and carbonized forms of consumption in nearby industries and households are most viable for the Ethiopian case. Full article
(This article belongs to the Special Issue Biomass Resources to Bioenergy)
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<p>Shares of the crop’s energy potentials with the gross national potential.</p> Full article ">Figure 2
<p>Regional energy potentials and their share % to gross national potential.</p> Full article ">
17 pages, 4498 KiB  
Article
Performance Evaluation of Distance Relay Operation in Distribution Systems with Integrated Distributed Energy Resources
by David R. Garibello-Narváez, Eduardo Gómez-Luna and Juan C. Vasquez
Energies 2024, 17(18), 4735; https://doi.org/10.3390/en17184735 - 23 Sep 2024
Viewed by 528
Abstract
This article presents the evaluation of the performance of the distance relay (ANSI function 21) when integrating Distributed Energy Resources (DERs) in a Local Distribution System (LDS). The aim is to understand the impacts of and the necessary modifications required in the operation [...] Read more.
This article presents the evaluation of the performance of the distance relay (ANSI function 21) when integrating Distributed Energy Resources (DERs) in a Local Distribution System (LDS). The aim is to understand the impacts of and the necessary modifications required in the operation of distance relays, considering different levels of DER aggregation, and identifying any threshold levels before issues arise. To achieve this, first, a comprehensive review was carried out to analyze the impacts generated in the protection systems. Second, by using the DigSilent Power Factory software, the implementation of the distance relay using a IEEE 13 Node Test Feeder was validated. The aggregation of the three fundamental types of DG, synchronous machines, solar panels, and wind turbines, was evaluated. The threshold at which distributed generation power injection begins to compromise distance protection performance was identified. This study compares the outcomes of using mho and quadrilateral protection schemes. Full article
(This article belongs to the Special Issue Planning, Operation and Control of Microgrids: 2nd Edition)
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<p>Protection blinding due to distributed generation integration.</p> Full article ">Figure 2
<p>False tripping of the protection due to the integration of distributed generation.</p> Full article ">Figure 3
<p>Blowing of the fuse due to transient faults caused by constant short-circuit current contribution from DG.</p> Full article ">Figure 4
<p>IEEE 13 node test feeder study case with distributed generation.</p> Full article ">Figure 5
<p>(<b>a</b>) mho characteristic for relay G; (<b>b</b>) mho characteristic for relay J.</p> Full article ">Figure 6
<p>(<b>a</b>) Quadrilateral characteristic for relay G; (<b>b</b>) quadrilateral characteristic for relay J.</p> Full article ">Figure 7
<p>(<b>a</b>) Error propagation in impedance measurement with integration of SG in relay with mho characteristic; (<b>b</b>) error propagation in impedance measurement with integration of SG in relay with quadrilateral characteristic.</p> Full article ">Figure 8
<p>(<b>a</b>) Error propagation in impedance measurement with integration of solar PV generator in relay with quadrilateral characteristic; (<b>b</b>) error propagation in impedance measurement with integration of solar PV generator in relay with mho characteristic.</p> Full article ">Figure 9
<p>(<b>a</b>) Error propagation in impedance measurement with integration of wind turbines in relay with mho characteristic; (<b>b</b>) error propagation in impedance measurement with integration of wind turbines in relay with quadrilateral characteristic.</p> Full article ">Figure 10
<p>Invariant zone 1 due to DG aggregation on the relay bar.</p> Full article ">Figure 11
<p>Infeed effect.</p> Full article ">Figure 12
<p>Absence of alterations in the operation of the DR with load increased.</p> Full article ">
27 pages, 22334 KiB  
Article
Continuously Learning Prediction Models for Smart Domestic Hot Water Management
by Raphaël Bayle, Marina Reyboz, Aurore Lomet, Victor Cook and Martial Mermillod
Energies 2024, 17(18), 4734; https://doi.org/10.3390/en17184734 - 23 Sep 2024
Viewed by 687
Abstract
Domestic hot water (DHW) consumption represents a significant portion of household energy usage, prompting the exploration of smart heat pump technology to efficiently meet DHW demands while minimizing energy waste. This paper proposes an innovative investigation of models using deep learning and continual [...] Read more.
Domestic hot water (DHW) consumption represents a significant portion of household energy usage, prompting the exploration of smart heat pump technology to efficiently meet DHW demands while minimizing energy waste. This paper proposes an innovative investigation of models using deep learning and continual learning algorithms to personalize DHW predictions of household occupants’ behavior. Such models, alongside a control system that decides when to heat, enable the development of a heat-pumped-based smart DHW production system, which can heat water only when needed and avoid energy loss due to the storage of hot water. Deep learning models, and attention-based models particularly, can be used to predict time series efficiently. However, they suffer from catastrophic forgetting, meaning that when they dynamically learn new patterns, older ones tend to be quickly forgotten. In this work, the continuous learning of DHW consumption prediction has been addressed by benchmarking proven continual learning methods on both real dwelling and synthetic DHW consumption data. Task-per-task analysis reveals, among the data from real dwellings that do not present explicit distribution changes, a gain compared to the non-evolutive model. Our experiment with synthetic data confirms that continual learning methods improve prediction performance. Full article
(This article belongs to the Special Issue Smart Energy Systems: Learning Methods for Control and Optimization)
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<p>Schematic view of a smart domestic hot water management system.</p> Full article ">Figure 2
<p>Prediction model architecture.</p> Full article ">Figure 3
<p>First twenty-eight days of Dwelling A daily cumulative DHW consumption.</p> Full article ">Figure 4
<p>First twenty-eight days of Dwelling B daily cumulative DHW consumption.</p> Full article ">Figure 5
<p>First three weeks of type 1 synthetic time series.</p> Full article ">Figure 6
<p>First three weeks of type 2 synthetic time series.</p> Full article ">Figure 7
<p>Dwelling A MAE between multiple time horizon predictions and their associated ground truth for each task. The prediction time horizons are 0.5 h, 1 h, 2 h, 6 h, 12 h, 18 h, 24 h. The results shown are obtained from an average of 5 runs.</p> Full article ">Figure 8
<p>Dwelling B MAE between multiple time horizon predictions and their associated ground truth for each task. The prediction time horizons are 0.5 h, 1 h, 2 h, 6 h, 12 h, 18 h, 24 h. The results shown are obtained from an average of 5 runs.</p> Full article ">Figure 9
<p>Dwelling A standard deviation associated with the 5 runs whose mean is shown in <a href="#energies-17-04734-f007" class="html-fig">Figure 7</a>.</p> Full article ">Figure 10
<p>Dwelling B standard deviation associated with the 5 runs whose mean is shown in <a href="#energies-17-04734-f008" class="html-fig">Figure 8</a>.</p> Full article ">Figure 11
<p>Example of 6-h prediction versus ground truth for 10 days in the last task of Dwelling A’s DHW consumption with a model continuously trained with the Dream Net algorithm.</p> Full article ">Figure 12
<p>Residual plot (ground truth minus predicted value) of the last task of Dwelling A DHW consumption with a model continuously trained with Dream Net with all tasks except the last one of Dwelling A.</p> Full article ">Figure 13
<p>Residual plot (ground truth minus predicted value) histogram of the last task of Dwelling A DHW consumption with a model continuously trained using the Dream Net algorithm with all tasks except the last one in Dwelling A.</p> Full article ">Figure 14
<p>Example of 24-h prediction versus ground truth for 11 days of d1_test, after training the model on d1_train.</p> Full article ">Figure 15
<p>Example of 24-h prediction versus ground truth for 11 days of d2_test, after training the model on d1_train.</p> Full article ">Figure 16
<p>Example of 24-h prediction versus ground truth for 11 days of d1_test, after first training the model on d1_train and then finetuning the model on d2_train, with no particular continual learning strategy.</p> Full article ">Figure 17
<p>Example of 24-h prediction versus ground truth for 11 days of d2_test, after first training the model on first d1_train and then finetuning the model on d2_train, with no particular continual learning strategy.</p> Full article ">Figure 18
<p>Example of 24-h prediction versus ground truth for 11 days of d1_test, after training the model first on d1_train and then on d2_train using Dream Net.</p> Full article ">Figure 19
<p>Example of 24-h prediction versus ground truth for 11 days of d2_test, after training the model first on d1_train and then on d2_train using Dream Net.</p> Full article ">Figure A1
<p>Example of 6 h prediction versus ground truth for 10 days in the last task of Dwelling A DHW consumption with a model continuously trained with DER algorithm.</p> Full article ">Figure A2
<p>Example of 6 h prediction versus ground truth for 10 days in the last task of Dwelling A DHW consumption with a model continuously trained with ER algorithm.</p> Full article ">Figure A3
<p>Example of 6 h prediction versus ground truth for 10 days in the last task of Dwelling A DHW consumption with a model continuously trained with finetuning setup.</p> Full article ">Figure A4
<p>Example of 6 h prediction versus ground truth for 10 days in the last task of Dwelling A DHW consumption with a model continuously trained with offline setup.</p> Full article ">Figure A5
<p>Example of 6 h prediction versus ground truth for 10 days in the last task of Dwelling B DHW consumption with a model continuously trained with DER algorithm.</p> Full article ">Figure A6
<p>Example of 6 h prediction versus ground truth for 10 days in the last task of Dwelling B DHW consumption with a model continuously trained with ER algorithm.</p> Full article ">Figure A7
<p>Example of 6 h prediction versus ground truth for 10 days in the last task of Dwelling B DHW consumption with a model continuously trained with Dream Net algorithm.</p> Full article ">Figure A8
<p>Example of 6 h prediction versus ground truth for 10 days in the last task of Dwelling B DHW consumption with a model continuously trained with the Finetuning setup.</p> Full article ">Figure A9
<p>Example of 6 h prediction versus ground truth for 10 days in the last task of Dwelling B DHW consumption with a model continuously trained with the offline setup.</p> Full article ">Figure A10
<p>Residual plots (ground truth value minus 6 h predicted value) of the last task of Dwelling A DHW consumption with models continuously trained with DER with every task except the last one in Dwelling A.</p> Full article ">Figure A11
<p>Residual plots (ground truth value minus 6 h predicted value) of the last task of Dwelling A DHW consumption with models continuously trained with ER with every task except the last one in Dwelling A.</p> Full article ">Figure A12
<p>Residual plots (ground truth value minus 6 h predicted value) of the last task of Dwelling A DHW consumption with models continuously trained with finetuning setting with every task except the last one in Dwelling A.</p> Full article ">Figure A13
<p>Residual plots (ground truth value minus 6 h predicted value) of the last task of Dwelling A DHW consumption with models continuously trained with offline setting with every task except the last one in Dwelling A.</p> Full article ">Figure A14
<p>Example of 24 h prediction versus ground truth for 11 days of d1_test, after the model has been initially trained on d1_train and then learned d2_train using ER.</p> Full article ">Figure A15
<p>Example of 24 h prediction versus ground truth for 11 days of d2_test, after the model has been initially trained on d1_train and then learned d2_train using ER.</p> Full article ">Figure A16
<p>Example of 24 h prediction versus ground truth for 11 days of d1_test, after the model has been initially trained on d1_train and then learned d2_train using DER.</p> Full article ">Figure A17
<p>Example of 24 h prediction versus ground truth for 11 days of d2_test, after the model has been initially trained on d1_train and then learned d2_train using DER.</p> Full article ">
17 pages, 4346 KiB  
Article
Technical Analysis of the Possibility of Burning Hydrogen in Furnaces of the Metallurgical Sector
by Andrzej Gołdasz, Karol Sztekler and Łukasz Mika
Energies 2024, 17(18), 4733; https://doi.org/10.3390/en17184733 - 23 Sep 2024
Viewed by 591
Abstract
This article analyses the possibility of using hydrogen as fuel in furnaces used in the metallurgical industry. The research was conducted for a selected continuous furnace. For this purpose, based on actual measurements, a heat balance of the furnace was prepared to determine [...] Read more.
This article analyses the possibility of using hydrogen as fuel in furnaces used in the metallurgical industry. The research was conducted for a selected continuous furnace. For this purpose, based on actual measurements, a heat balance of the furnace was prepared to determine its energy indicators. These values were used to verify the developed numerical model in IPSEpro 7.0 software. Numerical calculations were performed for three variants: pure natural gas; 30% hydrogen, 70% natural gas; and 100% hydrogen. The determined values of gas and combustion air streams allowed for achieving the assumed charge temperature in the heating technology. Calculations of the impact of the excess combustion air ratio on process parameters were also carried out. It was found that no changes are required in the exhaust gas removal system, but verification of the fan supplying air to cool the exhaust gases before the recuperator is necessary. The amount of hydrogen required to fuel the continuous furnace also increases significantly (nearly threefold), which may also affect operating costs. At the same time, the emission of carbon dioxide into the atmosphere is completely reduced, which may be an important criterion when considering modernization options for heating furnaces in the metallurgical industry. Full article
(This article belongs to the Topic Advanced Processes in Metallurgical Technologies, 2nd Volume)
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<p>Gross electricity consumption GWh, EU, 2000–2020 [<a href="#B2-energies-17-04733" class="html-bibr">2</a>].</p> Full article ">Figure 2
<p>Gross electricity consumption GWh, PL, 2000–2020 [<a href="#B2-energies-17-04733" class="html-bibr">2</a>].</p> Full article ">Figure 3
<p>The structure of hydrogen energy industry.</p> Full article ">Figure 4
<p>Methods of hydrogen production.</p> Full article ">Figure 5
<p>Influence of the proportion of hydrogen in a CH<sub>4</sub>/H<sub>2</sub> mixture on laminar flame speed and combustion temperature.</p> Full article ">Figure 6
<p>Cross-section of the Datapaq Slab Reheat System.</p> Full article ">Figure 7
<p>Temperature fluctuations: (<b>a</b>) the charge axis, and (<b>b</b>) the exhaust gas axis during the heating at selected measurement points; <span class="html-italic">t</span><sub>1</sub>, <span class="html-italic">t</span><sub>2</sub>—left; <span class="html-italic">t</span><sub>3</sub>, <span class="html-italic">t</span><sub>4</sub>—centre; <span class="html-italic">t</span><sub>5</sub>, <span class="html-italic">t</span><sub>6</sub>—right.</p> Full article ">Figure 8
<p>Heat balance of the furnace—Sankey diagram.</p> Full article ">Figure 9
<p>Heating furnace operation model developed in IPSEpro 7.0.</p> Full article ">Figure 10
<p>Flow rates of gas (<b>a</b>) and combustion air (<b>b</b>) for each design variant.</p> Full article ">Figure 11
<p>Flow rates of exhaust gas and combustion air (<b>a</b>) and exhaust gas temperature upstream and downstream of the recuperator (<b>b</b>)—100% natural gas.</p> Full article ">Figure 12
<p>Flow rates of exhaust gas and combustion air (<b>a</b>) and exhaust gas temperature upstream and downstream of the recuperator (<b>b</b>)—30% H<sub>2.</sub></p> Full article ">Figure 13
<p>Flow rates of exhaust gas and combustion air (<b>a</b>) and exhaust gas temperature upstream and downstream of the recuperator (<b>b</b>)—100% hydrogen.</p> Full article ">
21 pages, 8531 KiB  
Article
Development of a Simulation Model for a New Rotary Engine to Optimize Port Location and Operating Conditions Using GT-POWER
by Young-Jic Kim, Young-Joon Park, Tae-Joon Park and Chang-Eon Lee
Energies 2024, 17(18), 4732; https://doi.org/10.3390/en17184732 - 23 Sep 2024
Viewed by 630
Abstract
The objective of this study is to develop a 1D CFD simulation model to identify the optimal design parameters, using GT-POWER prior to the optimization of a new rotary engine derived from a three-lobe gerotor pump (GP3 RTE) based on 3D CFD simulation. [...] Read more.
The objective of this study is to develop a 1D CFD simulation model to identify the optimal design parameters, using GT-POWER prior to the optimization of a new rotary engine derived from a three-lobe gerotor pump (GP3 RTE) based on 3D CFD simulation. The models were compared based on their respective development stages (steps 1–4) to ascertain the impact of each parameter on performance. The step 4 model, which exhibited a similar trend to that observed in the 3D CFD results, was selected for further analysis and validation. The developed model accurately predicted GP3 RTE performance in terms of fuel consumption, indicated power, efficiency, and exhaust gas reticulation (EGR) behavior, approaching the accuracy of the CONVERGE model. Furthermore, the optimal intake/exhaust port locations and operating conditions of the GP3 RTE were derived using the developed step 4 model. The model provided a convenient and powerful tool for obtaining basic information regarding the unique behavior of the GP3 RTE, thereby enabling the optimization of the design parameters without the necessity for time-consuming three-dimensional design modifications. Full article
(This article belongs to the Special Issue Advances in Powertrain Design for Greener and Sustainable Non-road Mobile Machineries)
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<p>The GP3 engine developed in this study.</p> Full article ">Figure 2
<p>Core configuration for the developed GP3 engine.</p> Full article ">Figure 3
<p>Port open timing and four strokes for the first cylinder.</p> Full article ">Figure 4
<p>Locus of points for the GP3 RTE engine rotor and housing (<span style="color:red">red</span>: EP, <span style="color:blue">blue</span>: IP).</p> Full article ">Figure 5
<p>Flowchart to derive the VRE parameters from the GP3 RTE design and other parameters.</p> Full article ">Figure 6
<p>GP3 RTE, VRE, and square (<b>a</b>) surface area and (<b>b</b>) surface-area-to-volume ratio versus the SRA.</p> Full article ">Figure 7
<p>Intake and exhaust flow paths of (<b>a</b>) GP3 RTE core configuration and (<b>b</b>) VRE model.</p> Full article ">Figure 8
<p>(<b>a</b>) Example of effective IW area (SRA = 0°), (<b>b</b>) intake, and (<b>c</b>) exhaust window areas versus the shaft rotation angle.</p> Full article ">Figure 8 Cont.
<p>(<b>a</b>) Example of effective IW area (SRA = 0°), (<b>b</b>) intake, and (<b>c</b>) exhaust window areas versus the shaft rotation angle.</p> Full article ">Figure 9
<p>Comparison of the geometric open areas and effective port areas versus the SRA.</p> Full article ">Figure 10
<p>GT-POWER logic used for the VRE model of the GP3 RTE.</p> Full article ">Figure 11
<p>P-V diagrams of each step model.</p> Full article ">Figure 12
<p>Cumulative combustion rate of the VRE model obtained from 3D CFD of GP3 RTE.</p> Full article ">Figure 13
<p>Typical P-V diagrams obtained from 3D CFD and the step 4 VRE model.</p> Full article ">Figure 14
<p>Intake and exhaust port timing for experimental GP3 RTE, where the solid line without symbols represents the Cy 1 effective area, * represents Cy 2, and (○) represents Cy 3; ⇑ and ⇓ indicate the rotor direction (or piston for the reciprocating motion of Cy 1).</p> Full article ">Figure 15
<p>Flow near the exhaust port interference in the CONVERGE model.</p> Full article ">Figure 16
<p>Mass flow rates of intake and exhaust port for the step 4 VRE 1D model: (blue) intake port mass flow, (red) exhaust port mass flow, and ⇑ ⇓ indicate the direction of rotor advance. Flow direction in a typical engine is positive (0 dashed line: TDC; 180 dashed line: BDC).</p> Full article ">Figure 17
<p>Intake/exhaust port effective areas versus the SRA, for each intake/exhaust port location. (<b>a</b>) Intake port effective areas with respect SRA. (<b>b</b>) Exhaust port effective areas versus the SRA.</p> Full article ">Figure 18
<p>Primary optimization to derive the main analysis target cases: ○ shows the positions of the intake and exhaust ports of the experimental engine.</p> Full article ">Figure 19
<p>IMEP for each case from <a href="#energies-17-04732-t005" class="html-table">Table 5</a> at 3000/6000 RPM.</p> Full article ">Figure 19 Cont.
<p>IMEP for each case from <a href="#energies-17-04732-t005" class="html-table">Table 5</a> at 3000/6000 RPM.</p> Full article ">Figure 20
<p>Efficiency corresponding to each case listed in <a href="#energies-17-04732-t005" class="html-table">Table 5</a> at 3000/6000 RPM.</p> Full article ">
18 pages, 12164 KiB  
Article
A Lightweight Electric Meter Recognition Model for Power Inspection Robots
by Shuangshuang Song, Hongsai Tian and Feng Zhao
Energies 2024, 17(18), 4731; https://doi.org/10.3390/en17184731 - 23 Sep 2024
Viewed by 521
Abstract
Power inspection robots are essential for ensuring safe and optimal operation of power systems. However, during the operation of the power inspection robot, constraints imposed by computational and storage resources slow down the detection speed of the power system, failing to meet real-time [...] Read more.
Power inspection robots are essential for ensuring safe and optimal operation of power systems. However, during the operation of the power inspection robot, constraints imposed by computational and storage resources slow down the detection speed of the power system, failing to meet real-time monitoring requirements. To address these issues, this study proposes a lightweight electric meter recognition model for power inspection robots based on YOLOv5. The aim is to ensure efficient operation of the model on embedded devices, achieve real-time meter recognition, and enhance the practicality of the inspection robot. In the proposed model, GhostNet, a lightweight network, is employed as the YOLOv5 backbone feature extraction module, thus improving the model’s computational efficiency. In addition, the Wise-IoU (WIoU) loss function is used to improve the localization accuracy of the electric meter recognition model. Moreover, the GSConv module was introduced in the neck network for further model lightweighting. The experimental results demonstrated that the proposed model achieves a recognition accuracy of 98.8%, a recall rate of 98.8%, and a frame rate of 416.67 frames per second, while reducing computational volume by 25% compared to the YOLOv5 model. Furthermore, through case studies and comparisons, we illustrated the effectiveness and superiority of the proposed approach. Full article
(This article belongs to the Section F1: Electrical Power System)
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<p>Structure of the electric meter recognition model.</p> Full article ">Figure 2
<p>Network structure of the Ghost module.</p> Full article ">Figure 3
<p>Structure of the GhostNet bottleneck network. (<b>a</b>) Ghost bottleneck with stride = 1; (<b>b</b>) Ghost bottleneck with stride = 2.</p> Full article ">Figure 4
<p>The structure of the GSConv module.</p> Full article ">Figure 5
<p>Plot of train loss and precision changes.</p> Full article ">Figure 6
<p>Recognition results for an electric meter with complex background features.</p> Full article ">Figure 7
<p>Recognition results for an electric meter with reflective features.</p> Full article ">Figure 8
<p>Meter recognition results under different lighting conditions: (<b>a</b>) recognition results of mechanical meters under different light conditions; (<b>b</b>) recognition results of liquid crystal type meters under different light conditions.</p> Full article ">Figure 9
<p>Recognition results of meter images at different shooting angles.</p> Full article ">Figure 10
<p>Recognition results for different types of electric meters: (<b>a</b>) Recognition results for Tableone-type dial meters; (<b>b</b>) recognition results for Tabletwo-type dial meters; (<b>c</b>) recognition results for Tablethree-type dial meters.</p> Full article ">Figure 10 Cont.
<p>Recognition results for different types of electric meters: (<b>a</b>) Recognition results for Tableone-type dial meters; (<b>b</b>) recognition results for Tabletwo-type dial meters; (<b>c</b>) recognition results for Tablethree-type dial meters.</p> Full article ">Figure 11
<p>Comparison of F1 and FPS for different values of hyperparameters in WIoU.</p> Full article ">Figure 12
<p>Electric meter recognition experiment of power inspection robot.</p> Full article ">Figure 13
<p>Experimental results of electric meter recognition for power inspection robot.</p> Full article ">
20 pages, 12712 KiB  
Article
Experimental Research on Pressure Pulsation and Flow Structures of the Low Specific Speed Centrifugal Pump
by Weiling Lv, Yang Zhang, Wenbin Zhang, Ping Ni, Changjiang Li, Jiaqing Chen and Bo Gao
Energies 2024, 17(18), 4730; https://doi.org/10.3390/en17184730 - 23 Sep 2024
Viewed by 639
Abstract
The low specific speed centrifugal pump plays a crucial role in industrial applications, and ensuring its efficient and stable operation is extremely important for the safety of the whole system. The pump must operate with an extremely high head, an extremely low flow [...] Read more.
The low specific speed centrifugal pump plays a crucial role in industrial applications, and ensuring its efficient and stable operation is extremely important for the safety of the whole system. The pump must operate with an extremely high head, an extremely low flow rate, and a very fast speed. The internal flow structure is complex and there is a strong interaction between dynamic and static components; consequently, the hydraulic excitation force produced becomes a significant factor that triggers abnormal vibrations in the pump. Therefore, this study focuses on a low specific speed centrifugal pump and uses a single-stage model pump to conduct PIV and pressure pulsation tests. The findings reveal that the PIV tests successfully captured the typical jet-wake structure at the outlet of the impeller, as well as the flow separation structure at the leading edge of the guide vanes and the suction surface. On the left side of the discharge pipe, large-scale flow separation and reverse flow happen as a result of the flow-through effect, producing a strong vortex zone. The flow field on the left side of the pressure chamber is relatively uniform, and the low-speed region on the suction surface of the guide vanes is reduced due to the reverse flow. The results of the pressure pulsation test showed that the energy of pressure pulsation in the flow passage of the guide vane occurs at the fBPF and its harmonics, and the interaction between the rotor and stator is significant. Under the same operating condition, the RMS value distribution and amplitude at fBPF of each measurement point are asymmetric in the circumferential direction. The amplitude of fBPF near the discharge pipe is lower, while the RMS value is higher. A complex flow structure is shown by the larger amplitude and RMS value of the fBPF on the left side of the pressure chamber. With the flow rate increasing, the energy at fBPF of each measurement point increases first and then decreases, while the RMS value decreases, indicating a more uniform flow field inside the pump. Full article
(This article belongs to the Section K: State-of-the-Art Energy Related Technologies)
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<p>Schematic diagram of the model pump.</p> Full article ">Figure 2
<p>Pressure pulsation test platform; (<b>a</b>) Sensors and measurement points; (<b>b</b>) Closed-loop circuit.</p> Full article ">Figure 3
<p>Hydraulic components of the model pump and PIV system; (<b>a</b>) impeller, (<b>b</b>) diffuser, (<b>c</b>) annular volute, (<b>d</b>) PIV system, (<b>e</b>) PIV measurement area.</p> Full article ">Figure 4
<p>Performance curve of the model pump.</p> Full article ">Figure 5
<p>Time-domain signals of pressure pulsations at three measurement points under different operating conditions.</p> Full article ">Figure 6
<p>Frequency domain of pressure pulsations at three different measurement points under different operating conditions.</p> Full article ">Figure 7
<p>Amplitude of pressure pulsations at typical frequencies under different operating conditions.</p> Full article ">Figure 7 Cont.
<p>Amplitude of pressure pulsations at typical frequencies under different operating conditions.</p> Full article ">Figure 8
<p>The variation in pressure pulsation energy at typical frequencies under different operating conditions.</p> Full article ">Figure 9
<p>RMS at different measurement points under different operating conditions.</p> Full article ">Figure 10
<p>The absolute velocity field in region A1 under different operating conditions.</p> Full article ">Figure 11
<p>Distribution of absolute velocity in different positions of region A1 under different operating conditions. (<b>a</b>) Distribution of absolute velocity in the middle line L1 of the guide vane passage, (<b>b</b>) Distribution of absolute velocity in the discharge pipe L2 of the volute chamber, (<b>c</b>) Distribution of absolute velocity in the discharge pipe L3 of the volute chamber.</p> Full article ">Figure 12
<p>Absolute velocity field in region A2 under different operating conditions.</p> Full article ">Figure 13
<p>Distribution of axial vorticity cloud in region A1 under different operating conditions.</p> Full article ">Figure 13 Cont.
<p>Distribution of axial vorticity cloud in region A1 under different operating conditions.</p> Full article ">Figure 14
<p>Vorticity distribution in region A2 under different operating conditions.</p> Full article ">
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