Application of Grey Wolf Optimization Algorithm for Maximum Power Point Tracking of Solar Panels

Abstract

One of the applications of evolutionary algorithms is increasing the efficiency of photovoltaic (PV) systems. The main problem with using standard algorithms like the Incremental Conductance (IC) controller for maximum power point tracking (MPPT) under partial shading conditions (PSC) is that they do not provide reliable tracking of the global peak of the volt-watt characteristic, leading to increased losses and reduced power plant performance. Furthermore, there is currently no methodology for selecting the optimal sampling time of soft computing algorithm-based maximum power trackers for PV systems. The aim of this paper is to apply the Grey Wolf technique with optimally selected sampling time, which will result in fast and reliable tracking of the global maximum point of the PV panels. The results show that the selected optimal sampling time for the digital MPP controllers can increase the performance and efficiency of MPPT controllers. A DC-DC boost converter is used to match the PV panels with the resistive load. Several simulations were performed using MATLAB/Simulink to examine the performance of the proposed system. The results demonstrate that the proposed Grey Wolf algorithm can quickly capture the GMPP within 0.2 seconds under different shading conditions of the PV panels.