Research on real-time identification method of model parameters for the photovoltaic array

Abstract

Real-time identification study of model parameters has a facilitating effect on fault diagnosis and health state evaluation in photovoltaic (PV). Two effective methods for the real-time identification of PV array model parameters are proposed. Firstly, a PV array modeling method is proposed. This method is used to verify the accuracy of real-time model parameter identification. Then, an effective preprocessing method for the measured current–voltage (I–V) curves is proposed. The preprocessing method can improve the data quality of measured I–V curves, which helps improve the accuracy of the model parameter extraction. Next, the gorilla troops optimizer (GTO) is used to extract parameters of measured I–V curves. The extracted historical model parameters are the data sources for the real-time model parameter identification. Finally, the real-time parameter identification methods based on the time series prediction and the irradiance–temperature (G–T) grid searching are proposed. The root mean square error (RMSE) between the calculated I–V and measured I–V curves is one of the critical evaluation metrics. The RMSE of the time series prediction-based method ranges from 0.01A to 0.1A. The RMSE of the G–T grid searching-based method is around 0.01A. These two methods can complement each other and have good application prospects.