A Hybrid Model Parameter Extraction Method for Single-Diode Model of PV Module

Modeling of PV system is essential to access its efficiency and performance under various operating conditions and has great applicative prospect in MPPT and power forecasting technique. Multiple parameters are involved in describing the output characteristics of PV cell and the non-convex nature of optimization problem requires large amount of computation to identify those parameters. In this paper, a hybrid method is presented, which employs analytical approach and artificial neural network (ANN) to extract parameters of single-diode PV module model based on practical I-V curves. Six characteristics values are obtained from I-V curves of PV model with different model parameters. Data dimension reduction is involved to directly get two parameters by assuming the analytical approach, and the mapping relations between those obtained characteristic values and the rest of model parameters are specified by applying ANN. Simulation and experiment are carried out to verify the performance of the proposed method.