Grey wolf-based heuristic methods for accurate parameter extraction to optimize the performance of PV modules

Abstract

Parameter prediction for PV solar cells plays a crucial role in controlling and optimizing the performance of PV modules. In this study, the parameter prediction of a four-diode PV model was carried out using the Improved Grey Wolf Optimization (IGWO) algorithm, which builds upon the Grey Wolf Optimization (GWO) algorithm. The parameters required for the four-diode PV model were optimized based on a predefined objective function. Subsequently, the obtained data were compared with the data from RTCFrance Solar Cell to validate the accuracy and reliability of the optimization results. The evaluation of the optimization results revealed that the Sum Square Error (SSE) values for PSOGWO, AGWOCS, GWOCS, and GWO were 3.96E-05, while the MSE value for IGWO was 3.6309E-05. These findings clearly demonstrate that the proposed IGWO algorithm outperforms the other algorithms used in the study, based on the minimized SSE values. This study emphasizes the importance of parameter prediction in optimizing PV performance, and it contributes to thefield by introducing the novel IGWO algorithm for the four-diode PV model. The algorithm's superior performance, as demonstrated through extensive testing and comparison with existing algorithms, validates its efficacy in accurately predicting the parameters for the PV solar cell model.