Particle Swarm Optimization with Targeted Position-Mutated Elitism (PSO-TPME) for Partially Shaded PV Systems

Abstract

In partial shading situations, the power–voltage (P–V) characteristics of photovoltaic (PV) systems become more complex due to many local maxima. Hence, traditional maximum power point tracking (MPPT) techniques fail to recognize the global maximum power point (MPP), resulting in a significant drop in the produced power. Global optimization strategies, such as metaheuristic approaches, efficiently address this issue. This work implements the recent “particle swarm optimization through targeted position-mutated elitism” (PSO-TPME) with a reinitialization mechanism on a PV system under partial shading conditions. The fast-converging and global exploration capabilities of PSO-TPME make it appealing for online optimization. PSO-TPME also offers the flexibility of tuning the particle classifier, elitism, mutation level, and mutation probability. This work analyzes several PSO-TPME parameter settings for the MPPT of partially shaded PV systems. Simulations of the PV system under varying shading patterns show that PSO-TPME, with balanced exploitation–exploration settings, outperforms PSO in terms of convergence speed and the amount of captured energy during convergence. Furthermore, simulations of partial shading conditions with fast-varying, smooth, and step-changing irradiance demonstrated that the proposed MPPT technique is capable of dealing with these severe conditions, capturing more than 97.7% and 98.35% of the available energy, respectively.