Adaptive metaheuristic strategies for optimal power point tracking in photovoltaic systems under fluctuating shading conditions

Abstract

In recent years, there has been a growing interest in photovoltaic (PV) systems due to their capacity to generate clean energy, reduce pollution, and promote environmental sustainability. Optimizing the operational efficiency of PV systems has become a critical goal, particularly under challenging conditions like partial shading. Traditional maximum power point tracking (MPPT) methods face limitations in addressing this issue effectively. To tackle these challenges, this study introduces an enhanced MPPT approach based on the grey wolf optimizer (GWO), tailored to excel in GMPP tracking even under partial shading conditions. The algorithm harnesses adaptive and exploratory capabilities inspired by the behaviour of grey wolves in the wild. To comprehensively evaluate the proposed GWO-based MPPT algorithm's effectiveness, we conduct a comparative analysis with established metaheuristic algorithms, including particle swarm optimization (PSO) and the Pelican optimization algorithm (POA). Through this comparison, our study provides valuable insights into the algorithm's efficiency, behavior, and adaptability in addressing the complex challenges posed by partial shading scenarios in PV systems, thereby contributing to the advancement of efficient solar energy conversion.