Efficient identification of photovoltaic cell parameters via Bayesian neural network-artificial ecosystem optimization algorithm

• Four photovoltaic models are established; • Ten different meta-heuristic algorithms for parameter identification for all four models are discussed and compared; • Data preprocessing through Bayesian neural network, including adding noise, reducing noise, and predicting the data. 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.