Maximum Power Point Tracking of Photovoltaic Module Arrays Based on a Modified Gray Wolf Optimization Algorithm

Kuo-Hua Huang, Kuei-Hsiang Chao (), Ying-Piao Kuo and Hong-Han Chen
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Kuo-Hua Huang: Department of Electrical Engineering, National Chin-Yi University of Technology, Taichung 41170, Taiwan
Kuei-Hsiang Chao: Department of Electrical Engineering, National Chin-Yi University of Technology, Taichung 41170, Taiwan
Ying-Piao Kuo: Department of Electrical Engineering, National Chin-Yi University of Technology, Taichung 41170, Taiwan
Hong-Han Chen: Department of Electrical Engineering, National Chin-Yi University of Technology, Taichung 41170, Taiwan

Energies, 2023, vol. 16, issue 11, 1-21

Abstract: In this study, a modified gray wolf optimization algorithm (GWOA) was proposed to facilitate the maximum power point tracking (MPPT) of photovoltaic module arrays (PMAs). To increase the voltage conversion ratio and achieve a voltage boost through reduced duty cycles, a high-voltage step-up converter with a coupled inductor was used to replace the conventional energy storage inductor. To achieve global MPPT, the iteration parameters of the proposed GWOA were adjusted according to the slope of the PMA power–voltage (P–V) curve. According to the simulation results, the modified GWOA is more effective in MPPT than the perturbation and observation algorithm and conventional GWOA when multiple peaks appear in the P–V curve of a shaded PMA. In addition, the modified GWOA exhibits an improved tracking speed response and steady-state response.

Keywords: gray wolf optimization algorithm; photovoltaic module array; global maximum power point tracking; partial shading; coupled inductor; high-voltage step-up converter (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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