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

• Published in: Energies (Basel) Vol. 17; no. 16; p. 3935
• Main Authors: Fan, Zhi-Kai, Setianingrum, Annisa, Lian, Kuo-Lung, Suwarno, Suwarno
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-08-2024
• Subjects: Accuracy; Economic justification; Efficiency; Energy; Environmental aspects; genetic algorithm; Genetic algorithms; honey badger algorithm; Mathematical optimization; maximum power point tracking; Methods; Optimization; partial shading condition; Photovoltaic power generation; photovoltaic system; Renewable resources; Solar energy; Taiwan
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en17163935

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.