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

Abstract

AbstractParameterprediction for PV solar cells plays a crucial role in controlling andoptimizing the performance of PV modules. In this study, the parameter prediction of a four‐diode PV model wascarried out using the Improved Grey Wolf Optimization (IGWO) algorithm, whichbuilds upon the Grey Wolf Optimization (GWO) algorithm. The parameters requiredfor the four‐diode PV model were optimized based on a predefined objectivefunction. Subsequently, the obtained data were compared with the data from RTCFrance Solar Cell to validate the accuracy and reliability of the optimizationresults. The evaluation of the optimization results revealed that the SumSquare 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 demonstratethat the proposed IGWO algorithm outperforms the other algorithms used in thestudy, based on the minimized SSE values. This study emphasizes the importanceof parameter prediction in optimizing PV performance, and it contributes to thefield by introducing the novel IGWO algorithm for the four‐diode PV model. Thealgorithm's superior performance, as demonstrated through extensive testing andcomparison with existing algorithms, validates its efficacy in accuratelypredicting the parameters for the PV solar cell model.