Intelligent Nonlinear PID-Controller Combined With Optimization Algorithm for Effective Global Maximum Power Point Tracking of PV Systems

Many advanced techniques efficiently harvest the global maximum power (GMP) of the photovoltaic (PV) system, including machine learning techniques and metaheuristic algorithms based maximum power point tracking (MPPT). Nevertheless, they have shortcomings such as sluggish convergence and local maxima power (LMP) trapping. Combining techniques improves productivity. This work proposes an intelligent nonlinear proportional-integral-derivative (NPID) controller coupled with hybrid salp particle swarm optimization algorithm (SPSOA) to successfully harvest the GMP of PV system. The SPSOA-NPID controller’s performance is measured in regard to settle time, rising time, overshooting, peak time, undershoot, rotor rotation speed, and GMP under varied realistic irradiation and temperature profiles. In this work, the optimum parameter settings for the proposed NPID and the basic PID controllers were obtained utilizing the hybrid genetic algorithm and PSO (GA-PSO) techniques. Simulation findings proved the success and robustness of the SPSOA-NPID-based MPPT controller followed by GA-PSO-PID, GA-PID, GA-NPID, PSO-PID, PSO-NPID, P&O and INC respectively. The proposed SPSOA-NPID method obtained an average efficiency of (0.9946), the lowest average ripples (8.214 W), and the fastest average tracking time (0.052 s). Lastly, a hardware-in-loop (HIL) experimental carried out to confirm that the proposed SPSOA-NPID control can be practically implemented. Consequently, it is determined that the proposed SPSOA-NPID based GA-PSO control system is a potential MPPT approach based on the thorough research that have been given.