Research on fault diagnosis of solar photovoltaic module based on CNN-LSTM

The solar photovoltaic industry has developed rapidly in recent years. Accurate diagnosis of the location and type of PV module faults can improve the efficiency of operation and maintenance personnel. In this paper, a deep learning diagnostic model based on convolutional neural networks-long short term memory(CNN-LSTM) is proposed, which can be used to complete the detection task. In this paper, a fault classification method based on current performance is established. The algorithm firstly designs a feature extraction algorithm based on the layout characteristics of the PV array, and extracts the lateral and vertical features of the PV array current to obtain the spatial and temporal characteristics. The CNN network further extracts the lateral features and compresses the vertical features to solve the problem of single feature types and slow training. Finally, the LSTM neural network is used to complete the fault diagnosis of the PV modules.