Adaptive particle swarm optimization based model predictive control MPPT algorithm for PV systems under partial shading conditions

High step-up DC/DC converter and the Maximum Power Point Tracking (MPPT) technique is more critical for photovoltaic (PV) systems to extract the Maximum Power Point (MPP) from PV systems due to variations in climate conditions. The conventional algorithms are degraded by the low tracking speed and more oscillations in steady-state that occur when the irradiance changes rapidly. This paper applied the Model Predictive Control (MPC) MPPT algorithm in conjunction with the Adaptive Particle Swarm Optimization (APSO) algorithm. The APSO-MPC MPPT algorithm that has been proposed is capable of rapidly monitoring the maximum power with minimal optimum number of sensors and low oscillations in the steady state and no drift from the ultimate power point under irradiance changes. Additionally, the suggested method is capable of promptly monitoring the GMPP by using Adaptive Variable Step Size (AVSS) and prevent trips between the Global Maximum Power Point (GMPP) and Local Maximum Power Point (LMPP). MATLAB/SIMULINK is used to implement the suggested APSO-MPC based MPPT algorithm for a PV array under rapidly changing irradiance conditions and partial shading conditions (PSCs). The findings demonstrate that the suggested method can follow the MPP with quicker convergence, less steady-state oscillations, greater precision, and the capacity to stay clear of local maximum traps.