Performance prediction of PV modules based on artificial neural network and explicit analytical model
Chen Zhang, Yunpeng Zhang, Jialei Su, Tingkun Gu, Ming Yang
Abstract
The accurate characterization and prediction of current-voltage characteristics of photovoltaic (PV) modules under different operating conditions is essential for solar power forecasting and ensuring grid stability. The traditional method based on the single-diode model is inconvenient and complex because the current-voltage equation is implicit. In this paper, a novel method combining an artificial neural network (ANN) with an explicit analytical model (EAM) is proposed for predicting the I-V characteristics of PV modules under different operating conditions. The EAM makes it efficient to obtain the I-V curves from the estimated model parameters due to its simplicity and explicit expression. The ANN based on the EAM is composed of a three-layer feedforward neural network, in which the inputs are solar irradiation and module temperature and the outputs are the four parameters in EAM. Once the ANN is built and trained by using the measured I-V curves, the shape parameters and I-V curve are predicted by only reading solar irradiation and temperature without solving any nonlinear implicit equations. The accuracy and capability of the proposed method are verified by the experimental data for different types of PV modules. Moreover, the dependence of shape parameters in the EAM on solar irradiation and temperature is investigated first.