A novel intelligent optimization-based maximum power point tracking control of photovoltaic system under partial shading conditions

Due to its abundant natural supply and environmentally friendly features, solar photovoltaic (PV) production based on renewable energy is the ideal substitute for conventional energy sources. The efficiency of solar power generation under partial shading conditions (PSCs) is significantly increased by maximizing power extraction from the PV system. The maximum power point tracking (MPPT) method is to track maximum PowerPoint (MPP). This research proposes a photovoltaic MPPT control in partial shading conditions using Loxo-Canis (LOXOCAN) optimization algorithm. The ultimate goal of the novel method is to track the solar photovoltaic system's maximum power point under conditions of partial shading using the LOXOCAN algorithm. The proposed LOXOCAN algorithm is a combination of Elephant-herd optimization (EHO) and Coyote Optimization Algorithm (COA). The K-p, K-i, and K-d parameters of the PID controller of the MPPT controller will be tuned to their optimum values using the proposed optimization strategy. Higher MPPT performance and a quick convergence at the global maxima are shown in the proposed Loxo-Canis approach. Also, the recommended hybrid Loxo-Canis MPPT approach offers faster MPPT, less computational work, and higher efficiency.