4.7 Article

Minimum hydrogen consumption based control strategy of fuel cell/ PV/battery/supercapacitor hybrid system using recent approach based parasitism-predation algorithm

期刊

ENERGY
卷 225, 期 -, 页码 -

出版社

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.energy.2021.120316

关键词

Fuel cell; Renewable energy; Energy management strategy; Hybrid RESs; Parasitism-predation algorithm

资金

  1. Deputyship for Research AMP
  2. Innovation, Ministry of Education in Saudi Arabia [375213500]
  3. central laboratory at Jouf University

向作者/读者索取更多资源

This paper proposes an energy management strategy based on the parasitism-predation algorithm for hybrid renewable energy sources to supply aircraft in emergency situations. The strategy aims to minimize hydrogen consumption and improve aircraft power durability, showing superior efficiency and minimum hydrogen consumption in experimental results.
In hybrid renewable energy sources containing different storage devices like fuel cells, batteries, and supercapacitors, minimizing the hydrogen consumption is the main target for economic aspects and operation enhancement. External energy maximization strategy (EEMS) is the most popular energy management strategy used with hybrid renewable energy sources. However, gradient-based method is employed in EEMS which has low convergence, moreover it doesn't guarantee the optimum solution. Therefore, this paper proposes for first time an energy management strategy based on recent meta heuristic optimizer of parasitism-predation algorithm employed in hybrid source comprises photovoltaic/fuel cell/battery/supercapacitor for supplying aircraft in emergency state during landing. The main target is hydrogen consumption minimization, this helps in enhancing the power durability to the aircraft in case of curtailment of the main power source. The selection of parasitism-predation algorithm (PPA) is due to requirement of less parameters defined by the user and its high convergence ability. The proposed strategy is compared to other conventional and programmed approaches of state machine control, water cycle algorithm, dynamic differential annealed optimization, spotted hyena optimizer, EEMS, marine predator algorithm, and mayfly optimization algorithm. The obtained results confirmed the superiority of the proposed method achieving efficiency of 95.34% and minimum hydrogen consumption of 15.7559 gm. (c) 2021 Elsevier Ltd. All rights reserved.

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