| Title |
Research on MPPT Control of Photovoltaic Power Generation Systems Based on GA-GWO-INC Cooperative Optimization |
| Authors |
(Jin Peng) ; (Yang Zhou) ; (Yangyi Zhang) |
| DOI |
https://doi.org/10.5573/IEIESPC.2026.15.5.741 |
| Keywords |
Partial shading; Photovoltaic array; MPPT; GA-GWO-INC hybrid algorithm |
| Abstract |
This research presents a new Maximum Power Point Tracking (MPPT) algorithm for photovoltaic systems under partial shading, which is a hybrid of Genetic Algorithm (GA), Grey Wolf Optimization (GWO), and Incremental Conductance (INC). To improve global diversification, the GA-GWO-INC MPPT uses GA-based crossover and mutation. It also utilizes GWO for quicker convergence and INC for accurate local optimization. Different partial shading patterns were used to test the algorithm, and the results obtained were also used to compare the performance of the proposed method with those of traditional INC, GWO, and GA-GWO-INC based MPPT methods.
The outcomes indicate that the proposed GA-GWO-INC MPPT attains a tracking accuracy of up to 99.998%, outperforms other methods in all tests, and delivers a faster response with a computing time reduced by 9.4%-34%. In addition, the method keeps away from local optima while still achieving high accuracy and thus is the best performer in dynamic partial shading situations. The main discoveries demonstrate that GA-GWO-INC MPPT strategies have the potential to raise the efficiency, stability, and reliability of photovoltaic power systems to a great extent under complex shading conditions. |