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

Dianzi Jishu Yingyong

Zhang Yunqin¹  Chen Zehua¹  Liu Xiaofeng¹  Cheng Qize¹  Jiang Wenjie²  Shen Liang² 

[1] College of Data Science，Taiyuan University of Technology，Taiyuan 030001，China;Jinneng Clean Energy Co.，Ltd.，Taiyuan 030001，China;

关键词: photovoltaic module;    feature extraction;    cnn;    lstm;    fault diagnosis;   

DOI  :  10.16157/j.issn.0258-7998.191066

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown