Maximum Power Point Tracking Using Particle Swarm Optimization for Photovoltaic Systems

The particle swarm optimization (PSO) technique for maximum power point tracking (MPPT) in solar photovoltaic (PV) system under several environmental circumstances is investigated in this work. Four cases are examined: (i) constant irradiation and temperature, (ii) partial shading with constant temperature, (iii) constant irradiation with varying temperature, and (iv) partial shading with varying temperature. PSO optimized power extraction in all cases, effectively tracking the global Maximum Power Point (GMPP) and avoiding local maxima in partial shading. The algorithm adapted well to irradiance and temperature changes, enhancing PV system efficiency. The results obtained from PSO are compared with Flower Pollination Algorithm (FPA), showing superior performance. PSO demonstrates better convergence time and tracking efficiency compared to FPA. These findings suggest that PSO can significantly improve performance in large-scale PV installations exposed to environmental challenges like shading and temperature variations.