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