Recent Advances in Polymer Nanocomposites Based on Polyethylene and Polyvinylchloride for Power Cables

Mansour, Diaa‐Eldin A.; Abdel-Gawad, Nagat M. K.; Dein, Adel Z. El; Ahmed, Hanaa M.; Darwish, Mohamed M. F.; Lehtonen, Matti

doi:10.3390/ma14010066

Cited by 56 publications

(40 citation statements)

References 123 publications

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“…Further, Table 2 shows the lower and upper bounds of the decision variables of the model at two different operating condi-tions, (operating condition #1: 1/1 bar and 343.15 K) for both stacks (250 W and NedStack PS6) and (operating condition #2: 3/5 bar and 353.15 k) for 250 W stack. The simulation results have been compared to other literature works [18][19][20][21][22]. Moreover, four competitive methods; SSA [46], SCA [47], MFO [48], and PSO [16] have been implemented to test the effectiveness of COA.…”

Section: Resultsmentioning

confidence: 99%

“…A PEMFC is considered one of the most promising devices that convert chemical energy fuels into electrical energy based on electrochemical responses [15][16][17][18]. Specifically, the PEMFCs have numerous rewards, e.g., good electrical efficiency, little emission, and flexibility in fuel, that make them applicable to diverse applications [20][21][22][23][24]. For instance, they can apply in the combined tough issue owing to PEMFC being a compound multivariable powerfully coupled scheme.…”

Section: Introductionmentioning

confidence: 99%

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Optimal Estimation of Proton Exchange Membrane Fuel Cells Parameter Based on Coyote Optimization Algorithm

et al. 2021

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In recent years, the penetration of fuel cells in distribution systems is significantly increased worldwide. The fuel cell is considered an electrochemical energy conversion component. It has the ability to convert chemical to electrical energies as well as heat. The proton exchange membrane (PEM) fuel cell uses hydrogen and oxygen as fuel. It is a low-temperature type that uses a noble metal catalyst, such as platinum, at reaction sites. The optimal modeling of PEM fuel cells improves the cell performance in different applications of the smart microgrid. Extracting the optimal parameters of the model can be achieved using an efficient optimization technique. In this line, this paper proposes a novel swarm-based algorithm called coyote optimization algorithm (COA) for finding the optimal parameter of PEM fuel cell as well as PEM stack. The sum of square deviation between measured voltages and the optimal estimated voltages obtained from the COA algorithm is minimized. Two practical PEM fuel cells including 250 W stack and Ned Stack PS6 are modeled to validate the capability of the proposed algorithm under different operating conditions. The effectiveness of the proposed COA is demonstrated through the comparison with four optimizers considering the same conditions. The final estimated results and statistical analysis show a significant accuracy of the proposed method. These results emphasize the ability of COA to estimate the parameters of the PEM fuel cell model more precisely.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimal Estimation of Proton Exchange Membrane Fuel Cells Parameter Based on Coyote Optimization Algorithm

et al. 2021

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“…In this regard, the PV efficiency is mostly dependent on their operation at maximum power point (MPP) during different grid conditions. The MPP tracking (MPPT) is considered a control unit to preserve the output DC generated power at the maximum rate at numerous environmental and load circumstances [4][5][6][7]. A major benefit of efficiency improvement of PV is to minimize the overall operating cost of using these distributed generations (DG), which is a challenge towards the effective utilization of renewable DG units [8][9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Promising MPPT Methods Combining Metaheuristic, Fuzzy-Logic and ANN Techniques for Grid-Connected Photovoltaic

Ali

Mahmoud

Lehtonen

et al. 2021

Sensors

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This paper addresses the improvement of tracking of the maximum power point upon the variations of the environmental conditions and hence improving photovoltaic efficiency. Rather than the traditional methods of maximum power point tracking, artificial intelligence is utilized to design a high-performance maximum power point tracking control system. In this paper, two artificial intelligence-based maximum power point tracking systems are proposed for grid-connected photovoltaic units. The first design is based on an optimized fuzzy logic control using genetic algorithm and particle swarm optimization for the maximum power point tracking system. In turn, the second design depends on the genetic algorithm-based artificial neural network. Each of the two artificial intelligence-based systems has its privileged response according to the solar radiation and temperature levels. Then, a novel combination of the two designs is introduced to maximize the efficiency of the maximum power point tracking system. The novelty of this paper is to employ the metaheuristic optimization technique with the well-known artificial intelligence techniques to provide a better tracking system to be used to harvest the maximum possible power from photovoltaic (PV) arrays. To affirm the efficiency of the proposed tracking systems, their simulation results are compared with some conventional tracking methods from the literature under different conditions. The findings emphasize their superiority in terms of tracking speed and output DC power, which also improve photovoltaic system efficiency.

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“…Consequently, DC microgrids have been proven as one of the most efficient and cost-effective systems in the integration of RES with loads, as they decrease the AC-DC and DC-AC power conversion stages compared to AC microgrids [10,11]. Machine learning and artificial intelligence have shown promising performance in different electrical engineering applications [12][13][14][15][16] as well as power system components, e.g., power transformers and high voltage transmission lines [17][18][19][20][21][22][23]. Figure 1 illustrates the microgrid components in which the load and the diesel generator along with the wind turbines are connected to the AC side.…”

Section: Introductionmentioning

confidence: 99%

Novel Control Strategy for Enhancing Microgrid Operation Connected to Photovoltaic Generation and Energy Storage Systems

et al. 2021

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Recently, the penetration of energy storage systems and photovoltaics has been significantly expanded worldwide. In this regard, this paper presents the enhanced operation and control of DC microgrid systems, which are based on photovoltaic modules, battery storage systems, and DC load. DC–DC and DC–AC converters are coordinated and controlled to achieve DC voltage stability in the microgrid. To achieve such an ambitious target, the system is widely operated in two different modes: stand-alone and grid-connected modes. The novel control strategy enables maximum power generation from the photovoltaic system across different techniques for operating the microgrid. Six different cases are simulated and analyzed using the MATLAB/Simulink platform while varying irradiance levels and consequently varying photovoltaic generation. The proposed system achieves voltage and power stability at different load demands. It is illustrated that the grid-tied mode of operation regulated by voltage source converter control offers more stability than the islanded mode. In general, the proposed battery converter control introduces a stable operation and regulated DC voltage but with few voltage spikes. The merit of the integrated DC microgrid with batteries is to attain further flexibility and reliability through balancing power demand and generation. The simulation results also show the system can operate properly in normal or abnormal cases, thanks to the proposed control strategy, which can regulate the voltage stability of the DC bus in the microgrid with energy storage systems and photovoltaics.

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Recent Advances in Polymer Nanocomposites Based on Polyethylene and Polyvinylchloride for Power Cables

Cited by 56 publications

References 123 publications

Optimal Estimation of Proton Exchange Membrane Fuel Cells Parameter Based on Coyote Optimization Algorithm

Optimal Estimation of Proton Exchange Membrane Fuel Cells Parameter Based on Coyote Optimization Algorithm

Promising MPPT Methods Combining Metaheuristic, Fuzzy-Logic and ANN Techniques for Grid-Connected Photovoltaic

Novel Control Strategy for Enhancing Microgrid Operation Connected to Photovoltaic Generation and Energy Storage Systems

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