Maximizing Energy Output of Photovoltaic Systems: Hybrid PSO-GWO-CS Optimization Approach

Abstract

Photovoltaic (PV) systems suffer from partial shade and nonuniform irradiance conditions. Meanwhile, each PV module has a bypass shunt diode (BSD) to prevent hotspots. BSD also causes a series of a peak in the power-voltage characteristics of the PV array, trapping traditional maximum Power Point Tracking (MPPT) methods in local peaks. This study aims to address these challenges by combining cuckoo search (CS), gray wolf optimization (GWO), and particle swarm optimization (PSO) to enhance MPPT performance. The results compared the yield power by Tracking the MPP using only GWO, CS, or PSO MPPT techniques and combining them. Results show that in four cases: in case 1) Uniform Irradiation in three patterns (High, Medium, and Low), In case 2) Fixed Nonuniform Irradiation, While In case 3) Slow Dynamic Nonuniform Irradiation and case 4) ) Fast Dynamic nonuniform irradiation. The efficiency (PSO + CS) 97.86%, (PSO + GWO) 97.74%, and (GWO + CS) 98.55% were the highest performers in the case 1 results in (high, medium, and low), respectively. In Case 2, the efficiency (GWO + CS) is 98.62%, and it operates more effectively under fixed nonuniform irradiance. It has the highest efficiency in both Cases 3 and 4, even though its respective PSO + GWO efficiencies are 97.45% and 97.26%. Based on these results, a hybrid mode of merging algorithms based on weather radiation conditions is proposed.