A Hybrid Approach for Photovoltaic Maximum Power Tracking under Partial Shading Using Honey Badger and Genetic Algorithms

Zhi-Kai Fan, Annisa Setianingrum, Kuo-Lung Lian () and Suwarno Suwarno
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Zhi-Kai Fan: Department of Electrical Engineering, National Taiwan University of Science and Technology, No. 43, Section 4, Keelung Rd., Taipei 106, Taiwan
Annisa Setianingrum: Department of Electrical Engineering, National Taiwan University of Science and Technology, No. 43, Section 4, Keelung Rd., Taipei 106, Taiwan
Kuo-Lung Lian: Department of Electrical Engineering, National Taiwan University of Science and Technology, No. 43, Section 4, Keelung Rd., Taipei 106, Taiwan
Suwarno Suwarno: Department of Electrical Power Engineering, School of Electrical Engineering and Informatics, Bandung Institute of Technology, Jalan Ganesha No. 10, Bandung 40132, Indonesia

Energies, 2024, vol. 17, issue 16, 1-16

Abstract: This study presents a new approach for Maximum Power Point Tracking (MPPT) by combining the honey badger algorithm (HBA) with a Genetic Algorithm (GA). The integration aims to optimize photovoltaic (PV) system performance in partial shading conditions (PSCs). Initially, the HBA is utilized to explore extensively and identify potential solutions while avoiding local optima. If necessary, the GA is then employed to escape local optima through selection, crossover, and mutation operations. On average, this proposed method has a 40% improvement in tracking time and 0.77% in efficiency compared with the HBA. In a dynamic case, the proposed method achieves a 4.81% improvement compared to HBA.

Keywords: maximum power point tracking; photovoltaic system; honey badger algorithm; genetic algorithm; partial shading condition (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: 2024
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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