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

Abstract

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.