Comparative Analysis of Grey Wolf Optimization and Particle Swarm Optimization based GMPPT for Partially Shaded PV Array

This paper presents a study and comparative analysis of particle swarm optimization (PSO) and grey wolf optimization (GWO) to track global maximum power point (GMPP) of photovoltaic (PV) array under partial shading conditions (PSCs). Tracking GMPP of PV array under PSCs is difficult for classical algorithms like, hill climbing (HC), perturb and observe (P& O), increment conductance, etc. Under PSCs, there exists multiple local maxima and one global maximum in P-V curve of the PV array, and these algorithms are entrapped to the local maxima. Therefore, to track GMPP, many artificial intelligence based, optimization based and hybrid GMPP tracking algorithm are proposed. In this paper, the working principles of PSO and GWO based GMPP tracking algorithms are discussed for better understanding. The tracking performance of both algorithms under different PSCs are examined on MATLAB software using a resistive load connected PV system. Further, their energy losses, tracking error and tracking power efficiency are calculated and compared for further clarification.