| Title |
Comparative Study of Artificial-Intelligence-based Methods to Track the Global Maximum Power Point of a Photovoltaic Generation System |
| Authors |
Chaeeun Lee ; Yohan Jang ; Seunghoon Choung ; Sungwoo Bae |
| DOI |
https://doi.org/10.6113/TKPE.2022.27.4.297 |
| Keywords |
Artificial intelligence; Global maximum power point; Maximum power point tracking; Photovoltaic generation |
| Abstract |
This study compares the performance of artificial intelligence (AI)-based maximum power point tracking (MPPT) methods under partial shading conditions in a photovoltaic generation system. Although many studies on AI-based MPPT have been conducted, few studies comparing the tracking performance of various AI-based global MPPT methods seem to exist in the literature. Therefore, this study compares four representative AI-based global MPPT methods including fuzzy logic control (FLC), particle swarm optimization (PSO), grey wolf optimization (GWO), and genetic algorithm (GA). Each method is theoretically analyzed in detail and compared through simulation studies with MATLAB/Simulink under the same conditions. Based on the results of performance comparison, PSO, GWO, and GA successfully tracked the global maximum power point. In particular, the tracking speed of GA was the fastest among the investigated methods under the given conditions. |