Research on Fault Prognostic of Photovoltaic System Based on LSTM-SA

Abstract

In the fault prognostic of photovoltaic systems, it is difficult to establish mathematical or physical models of complex components or systems. Therefore, this paper proposes a hybrid model of LSTM-SA, based on the principle of self-attention(SA) mechanism and long short-term memory (LSTM) neural network, combining the idea of self-attention and LSTM for timing problems processing capability to prognosticate faults of different equipment. Experimental verification of LSTM, LSTM-SA, BPNN and RNN models using the data of#102, #110 and #519 equipment respectively shows that the root mean square error (RMSE) of the model based on LSTM-SA is lower than that of the other three models in sunny days, indicating that the LSTM model with self-attention mechanism is optimized. Finally, the mixed model based on LSTM-SA is used to prognosticate the fault of different devices. The results are as follows: the fault of the #611 device at the 136th time point, and the fault of the #513 device at the 187th time point.