Synergistic Application of Particle Swarm Optimization and Gravitational Search Algorithm for Solar PV Performance Improvement

Abstract

This study aims to optimize photovoltaic systems by developing a novel hybrid metaheuristic approach for maximum power point tracking (MPPT). The proposed method eclectically combines particle swarm optimization (PSO) and gravitational search algorithm (GSA) to overcome individual limitations and leverage complementary strengths. PSO, while surpassing in exploration, may suffer from premature convergence. GSA demonstrates strong exploitation capabilities but can struggle with slow convergence. A simulation model is developed to evaluate the hybrid algorithm’s performance in optimizing PV systems’ duty cycle. The approach utilizes the exploitation capabilities of PSO and GSA to navigate the search space effectively. Results demonstrate that the hybrid algorithm outperforms traditional techniques and standalone metaheuristics, achieving improved convergence time, faster settling time, and enhanced MPPT tracking efficiency. Under varying irradiance conditions, the proposed method consistently delivers higher power generation and improved overall PV system efficiency, offering a promising solution for optimizing PV systems and maximizing energy generation.