| Title |
An Improved Genetic Algorithm-based Global Maximum Power Point Tracking Method for Photovoltaic Generation |
| Authors |
Chae-Eun Lee ; Yo-Han Jang ; Seung-Hoon Choung ; Sung-Woo Bae |
| DOI |
https://doi.org/10.6113/TKPE.2023.28.3.223 |
| Keywords |
Improved genetic algorithm; Maximum power point tracking; Photovoltaic generation system; Partial shading conditions |
| Abstract |
This paper presents a novel method for tracking the global maximum power point(GMPP) that occurs under partial shading conditions of photovoltaic generation systems. The proposed method dynamically adjusts a crossover probability of the gene used in the genetic algorithm(GA). The gene crossover probability is adjusted through the fitness evaluation for each gene. As a result, the proposed method can track the GMPP quickly and accurately without an unnecessary crossover probability. The superiority of the proposed method is verified by comparative studies with the existing GA through simulation results in MATLAB/Simulink and real-time experimental results in hardware-in-the-loop simulation(HILS). The simulation and experimental results demonstrated that the proposed method can track the GMPP faster than the existing GA under the designed partial shading conditions. |