An Intelligent Hybrid DE-PSO GMPPT Integrated with Accurate PSC Load Variation Detection for PV Systems

Photovoltaic (PV) modules exhibit nonlinear behavior, where variations in temperature and irradiation in-fluence their performances. This attribute becomes considerably more intricate when there is non-uniform solar radiation, which affects the extracted PV power. To optimize this last, it is essential to utilize a converter that functions as a Global Maximum Power Point Tracker (GMPPT), which ensures the keeping of the Maximum Power Point (MPP), regardless of the state of the climatic conditions. This research presents a new hybrid Maximum Power Point Tracking (MPPT) technique that combines the Differential Evolution-Particle Swarm Op-timization (DE-PSO) algorithm with load variation detection. The purpose of this method is to solve the mentioned problem. The GMPPT technique effectively addresses the inherent limitations of conventional MPPT methods such as P&O and INC. The framework provides a straightforward and robust MPPT solution for load variations without recalculating and repeatedly performing the DE-PSO algorithm when there is only a variation in uniform irradiance. The suggested hybrid GMPPT with the partially shaded condition (PSC) load variation detection technique was evaluated by simulations, and using Matlab/Simulink software environment. The simulation results for different PSC profiles highlight the algorithm's precision, ability to track, convergence speed towards the GMPP, and efficiency. The algorithm's convergence to GMPP is independent of the initial conditions of the search process.