Global Maximum Power Point Tracking of Photovoltaic Systems under Partial Shading Using Crow Search Algorithm

As a clean and sustainable energy source, photovoltaic (PV) systems are being included more and more into contemporary energy infrastructures. However, environmental factors like partial shade and variations in irradiance have a significant impact on their ability to generate power. The power-voltage (P-V) characteristic of the PV array displays many local maxima under partial shade conditions (PSC), making it challenging to determine the global maximum power point (GMPP) using traditional maximum power point tracking (MPPT) techniques. Among these methods, the popular Perturb and Observe (P&O) approach has the disadvantage of becoming stuck at local maxima, which lowers the efficiency of power extraction. This paper suggests a metaheuristic-based MPPT strategy that uses the Crow Search Algorithm (CSA) to get around this restriction. The CSA has proven to have strong exploration and exploitation capabilities in challenging optimization problems, and it draws inspiration from the clever ways crows hide and retrieve food. In this work, CSA is used to track the global maximum power point under partial shading conditions by determining the PV system's ideal operating voltage. Using MATLAB simulations, the suggested CSA-based MPPT's performance is assessed and contrasted with the traditional P&O algorithm. The findings demonstrate that although the P&O technique may become stuck in a local maximum, the CSA method effectively converges to the global maximum power point even in the presence of multiple local maxima. Additionally, the suggested method shows improved power extraction efficiency and quicker convergence. These findings demonstrate how well the CSA-based MPPT approach works to enhance photovoltaic systems' performance in difficult environmental circumstances.