An Enhanced Grey Wolf Optimization Algorithm for Photovoltaic Maximum Power Point Tracking Control Under Partial Shading Conditions

A partial shading condition (PSC) is one of the most common problems in photovoltaic (PV) system. It causes output power a PV system drastically decrease. Meta-heuristic algorithms (MHA) can track maximum point power-voltage curve with multiple peaks. Grey wolf optimization (GWO) algorithm new based on MHA. has been used to solve many applications including MPPT for However, accuracy and tracking time original GWO (OGWO) still be further improved various PSCs. Therefore, there have some modified grey proposed improve GWO. Nevertheless, only incremental improvement made. an enhanced (EGWO) proposed, which adds weighting average, pouncing behavior nonlinear convergence factor OGWO. In particular, since real wolves may engage action when they are hunting, inclusion completes yields great improvements. As will shown via experiment, EGWO reduce (up 45.5% OGWO) dynamic efficiency by more than 2%, compared OGWO.Moreover, achieves highest existing other swarm algorithms.