A Comparative Study of Three Meta-Heuristic MPPT Techniques for PV Systems Under Complex Partial Shading Conditions

The energy generation performance of solar panels can be greatly influenced by partial shading conditions (PSCs). The phenomenon is ascribed to the presence of several maximum power points (MPPs) evident on the characteristic curves of power-voltage (P-V). These MPPs encompass diverse local peaks (LPs) along with a solitary global peak (GP). Traditional (MPPT) techniques fall short in precisely tracing the GP owing to the complex and nonlinear features of PV characteristic curves during instances of PSC. Hence, the adoption of MPPT techniques based on artificial intelligence (AI) becomes essential to effectively track the GP. In this research endeavor, three metaheuristic approaches for MPPT are investigated, namely the Team Game Algorithm (TGA), Gray Wolf Optimization (GWO), and Particle Swarm Optimization (PSO). These algorithms are practically implemented, and their efficacy is experimentally validated while considering different solar irradiation levels while keeping the temperature constant. The experimental evaluation aims to compare the dynamic behavior of the photovoltaic (PV) array under various weather conditions. In contrast to other approaches, the TGA technique offers outstanding convergence capabilities, as shown by the experimental tests carried out in this study.