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,
Suwarno Suwarno
Affiliations
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
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.
