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Improved Performance of Flywheel Fast Charging System (FFCS) Using Enhanced Artificial Immune System EAIS
Faculty
Engineering
Year:
2020
Type of Publication:
ZU Hosted
Pages:
Authors:
Ahmed Mohamed Othman Abdelmaksoud
Staff Zu Site
Abstract In Staff Site
Journal:
IEEE Systems Journal IEEE
Volume:
Keywords :
Improved Performance , Flywheel Fast Charging System
Abstract:
There are worldwide tendencies to reduce greenhouse gas emissions toward sustainable communities. The increase in the penetration of electric vehicles (EVs) is an important strategy, which requires the development of regular and fast charging infrastructures. A flywheel fast charging system (FFCS) is proposed to provide reliable fast charging infrastructures for e-buses and EVs using flywheel technology. This paper presents an advanced computational intelligence technique based on an enhanced artificial immune system (EAIS) to improve the performance of the FFCS to support transportation electrification. FFCSs are optimally integrated with utility grid networks, where they offer loading balance and grid protection from any collapse. In addition, the FFCS can achieve a significant reduction in energy costs and maximize energy supply from clean energy resources. The EAIS is an advanced optimization technique that is proposed to tune the optimal dynamic parameters of the FFCS to achieve the improved response. MATLAB/Simulink simulations show results that prove the effectiveness of the proposed system.
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Ahmed Mohamed Othman Abdelmaksoud, "Performance optimisation for novel green plug-energy economizer in micro-grids based on recent heuristic algorithm", IET Institution of Engineering and Technology IET Digital Library, 2016
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Ahmed Mohamed Othman Abdelmaksoud, "Optimal capacitor placement by Enhanced Bacterial Foraging Optimization (EBFO) with accurate thermal re-rating of critical cables", Elsevier, 2016
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Ahmed Mohamed Othman Abdelmaksoud, "Enhanced Backtracking Search Algorithm for Optimal Coordination of Directional Over-current Relays Including Distributed Generation", Taylor & Francis Group, 2016
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Ahmed Mohamed Othman Abdelmaksoud, "Optimal reconfiguration comprising voltage stability aspect using enhanced binary particle swarm optimization algorithm", Taylor & Francis, 2015
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Ahmed Mohamed Othman Abdelmaksoud, "Control Design for AC/DC Microgrid for Residential Home Cluster", Intelligent Control & Automation, 2015
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