Enhancing photovoltaic system performance using an advanced hybrid MPPT strategy: a performance and efficiency assessment under variable environmental conditions

Photovoltaic (PV) power generation is highly sensitive to variations in solar irradiance and temperature, making reliable maximum power point tracking (MPPT) essential for maximizing energy extraction under dynamic operating conditions. In this study, we developed and systematically evaluated a hybrid artificial neural network–genetic algorithm (ANN–GA) MPPT strategy that combines nonlinear MPP prediction with global optimization. The proposed controller was evaluated using an MSX-60 PV module model under irradiance levels ranging from 300 to 1000 W/m 2 and temperatures ranging from 291–323 K. The proposed method was benchmarked under identical operating conditions against seven other methods using operating ranges, root mean square error (RMSE), mean absolute error (MAE), R 2 , tracking efficiency, and control-loop execution time as performance metrics. ANN–GA achieved the highest maximum-power range (18.64–54.22 W) and current range (1.39–3.55 A), while yielding RMSE values of 5.39 W, 1.21 V, and 0.28 A for P mp , V mp , and I mp , respectively. Compared with the standalone ANN, these errors were reduced by approximately 11.9%, 10.4%, and 12.5%, respectively, confirming the added value of GA-based optimization. The corresponding R 2 values reached 0.894, 0.893, and 0.893, while the tracking efficiencies were 87.4%, 88.6%, and 94.3%, respectively. The repeated-run analysis further showed that ANN–GA maintained lower RMSE values and narrower error distributions than GA–P&O (perturb and observe), indicating improved repeatability and robustness across independent runs and different PV module configurations. In addition, ANN–GA required approximately 88% less control-loop execution time than GA–P&O, the second-best hybrid method, with reductions of 88.19% for the Solarex MSX-60 module and 88.17% for the Kyocera KC200GT module. Overall, the results show that the proposed hybrid strategy provides a favorable balance among tracking accuracy, repeatability, and computational efficiency under the investigated simulation conditions.