Improved metaheuristic optimization algorithm applied to hydrogen fuel cell and photovoltaic cell parameter extraction

En Jui Liu, Yi Hsuan Hung*, Che Wun Hong

*此作品的通信作者

研究成果: 雜誌貢獻期刊論文同行評審

摘要

As carriers of green energy, proton exchange membrane fuel cells (PEMFCs) and photovoltaic (PV) cells are complex and nonlinear multivariate systems. For simulation analysis, optimization control, efficacy prediction, and fault diagnosis, it is crucial to rapidly and accurately establish reliability modules and extract parameters from the system modules. This study employed three types of particle swarm optimization (PSO) algorithms to find the optimal parameters of two energy models by minimizing the sum squared errors (SSE) and roots mean squared errors (RMSE). The three algorithms are inertia weight PSO, constriction PSO, and momentum PSO. The obtained calculation results of these three algorithms were compared with those obtained using algorithms from other relevant studies. This study revealed that the use of momentum PSO enables rapid convergence (under 30 convergence times) and the most accurate modeling and yields the most stable parameter extraction (SSE of PEMFC is 2.0656, RMSE of PV cells is 8.839 × 10−4 ). In summary, momentum PSO is the algorithm that is most suitable for system parameter identification with multiple dimensions and complex modules.

原文英語
文章編號619
期刊Energies
14
發行號3
DOIs
出版狀態已發佈 - 2021 二月 1

ASJC Scopus subject areas

  • 可再生能源、永續發展與環境
  • 燃料技術
  • 能源工程與電力技術
  • 能源(雜項)
  • 控制和優化
  • 電氣與電子工程

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