Accurate identification of unknown internal parameters in photovoltaic (PV) cells is crucial and significantly affects the subsequent system-performance analysis and control.However

noise

insufficient data acquisition

and loss of recorded data can deteriorate the extraction accuracy of unknown parameters.Hence

this study proposes an intelligent parameter-identification strategy that integrates artificial ecosystem optimization(AEO)and a Bayesian neural network(BNN)for PV cell parameter extraction.A BNN is used for data preprocessing

including data denoising and prediction.Furthermore

the AEO algorithm is utilized to identify unknown parameters in the single-diode model (SDM)

double-diode model (DDM)

and three-diode model (TDM).Nine other metaheuristic algorithms(MhAs) are adopted for an unbiased and comprehensive validation.Simulation results show that BNN-based data preprocessing combined with effective MhAs significantly improve the parameter-extraction accuracy and stability compared with methods without data preprocessing.For instance

under denoised data

the accuracies of the SDM

DDM

and TDM increase by 99.69%

99.70%

and 99.69%

respectively

whereas their accuracy improvements increase by 66.71%

59.65%

and 70.36%

respectively.