Improved Snake Optimizer Algorithm-Based GMPPT With a Fast Response to the Load Variations Under Different Weather Conditions for PV Systems

Photovoltaic (PV) systems are subjected to varying irradiance conditions throughout the day and because of the bypass diodes that are connected in parallel with the PV module to prevent hotspot occurrences, leading to several peaks on the power curve during the partial shading conditions (PSCs). This article has proposed an improved snake optimizer algorithm to track the maximum power with fast and accurate tracking. Moreover, a new strategy is proposed to differentiate between the uniform shading conditions (USCs) and PSCs to prevent unnecessary searching on the entire PV curve during the USCs, leading to fast-tracking. Furthermore, a new strategy has been presented to detect the load variation with a fast convergence speed. The proposed method was experimentally validated with a buck–boost converter with a sampling time of 0.05 s. The experimental validation of the proposed method provides fast and accurate tracking with an average tracking time of less than 0.75 s of different weather conditions with 99.86% average efficiency. Furthermore, a comparison of the proposed technique with recent metaheuristic algorithms in this area is also implemented to assess the efficacy of the proposed method.