A Novel Salp Swarm Optimization MPP Tracking Algorithm for the Solar Photovoltaic Systems under Partial Shading Conditions

Abstract

To extract the maximum solar power from the photovoltaic (PV) panel/array with the high conversion efficiency under partial shading condition (PSC), this paper discusses a new and an efficient maximum power point (MPP) tracking algorithm. The proposed algorithm is based on the bio-inspired salp swarm optimization (SSO), and the algorithm forecasts the global MPP (GMPP) with the fast convergence to GMPP and high tracking efficiency. The SSO algorithm thus reduces the computational burden as encountered in whale optimization algorithm (WOA), and gray wolf optimization (GWO) algorithm discussed in the various literatures. The modeling and simulation of the proposed SSO algorithm are done with the help of Matlab/Simulink software to validate the effectiveness to locate the MPP during PSCs. The simulation results prove that the proposed SSO algorithm exhibits a high PV power output with the tracking efficiency of more than 95% at the faster convergence rate to GMPP. The SSO algorithm is experimentally verified on the conventional boost converter under different shading conditions.