A Novel Hybrid Optimization Algorithm for Maximum Power Point Tracking of Partially Shaded Photovoltaic Systems

Abstract

A novel hybrid optimization technique (WOA–SSA) combines whale optimization (WOA) and salp swarm (SSA) algorithms are presented. The proposed technique is designed to gather the benefits and features of SSA and WOA algorithms. The proposed technique is applied for tracking the global maximum power point (GMPP) and improve the performance of photovoltaic (PV) plants during the conditions of partial shading (PSC). The evaluation of the performance of the proposed technique is performed via MATLAB/SIMULINK. Moreover, a comparative analysis is exhibited to confirm the performance of the planned WOA–SSA technique against the conventional SSA and WOA, separately. The obtained results show the superiority of the designed WOA–SSA technique considering tracking efficiency. Moreover, the proposed WOA–SSA algorithm reaches the best solution in less time and with a better convergence speed compared to SSA and WOA. The statistical results confirm that the success rate has been enhanced from 76.6667% and 73.333%, respectively, with WOA and SSA to 95% with the proposed hybrid algorithm. Furthermore, the value of the standard deviation of 2.7877 and 2.5329 based on WOA and SSA is reduced to 0.3320 in the case of the proposed WOA–SSA. Index Terms—Global MPPT; Partially shaded PV Systems; Hybrid optimization; Whale optimization algorithm; Salp swarm algorithm; Convergence.