Hybrid SALP Swarm Grey Wolf Optimized Fuzzy Based Maximum Power Point Tracking in Photovoltaic Panel

Abstract

The global need for renewable energy sources (RES) is increasing day by day, as the degradation of fossil fuel is happening in the environment. There were many RES available such as wind, solar, hydropower, geothermal, and biomass among them the solar energy was regarded as the sustainable to satisfy the energy demand. Because of its cheap maintenance, long lifespan, and cleanliness, solar photovoltaic (PV) systems are becoming more and more popular among RES. The primary goal of the proposed system is to expand array efficiency by optimizing the performance of fuzzy-based maximum power point tracking (MPPT) controllers using a hybrid approach that combines Salp Swarm Optimization (SSO) and Grey Wolf Optimization (GWO). The proposed hybrid optimization algorithm effectively tunes the parameters of the fuzzy logic controller (FLC), this leads to improve the tracking performance. This leads to a robust and efficient MPPT controller using fuzzy logic to accurately track the MPP under unpredictable ecological circumstances. The proposed method values are given to the Simulink/Matlab environment, and the results are taken. The efficiency obtained by the proposed system is 99.8% and the power obtained is 3995W. The proposed method efficiently tracks the performance of the MPPT.