• 中国计算机学会会刊
  • 中国科技核心期刊
  • 中文核心期刊

Computer Engineering & Science ›› 2021, Vol. 43 ›› Issue (12): 2223-2230.

Previous Articles     Next Articles

A fault diagnosis model of distributed photovoltaic power stations based on deep residual network

XIE Xiang-ying1,2,LIU Hu3,WANG Dong4,LENG Biao1   

  1. (1.School of Computer Science and Engineering,Beihang University,Beijing 100191;

    2.Department of Strategic Development,State Grid Electronic Commerce Co.,Ltd.,Beijing 100053;

    3.Department of the Internet,State Grid Corporation of China,Beijing 100031;

    4.Department of Digital Technology,State Grid Electronic Commerce Co.,Ltd.,Beijing 100053,China)

  • Received:2020-12-11 Revised:2021-01-17 Accepted:2021-12-25 Online:2021-12-25 Published:2021-12-31

Abstract: The deployment environment of distributed photovoltaic power plants is relatively complicated, and many kinds of faults inevitably occur during the actual operation. In order to solve the above problem, this paper proposes a fault diagnosis model of distributed photovoltaic power stations based on deep residual network. It analyzes and processes the sequence data of equipment operation, and achieves rapid and accurate judgment of fault categories. This model applies a one-dimensional convolution kernel to perceive the characteristics of time series data. Then, it uses a multi-level convolution structure to increase the diagnostic ability. Finally, the residual network is utilized to solve the problem of gradient disappearance caused by the increase of model depth, and accelerate the training of the deep model. The experimental results based on the power station test data show that the residual network model achieves higher fault diagnosis accuracy than several state-of-the-art intelligent models. The application of this model can not only greatly reduce the investment in fault inspection of photovoltaic power plants, but also improve the efficiency of fault diagnosis of photovoltaic power plants.


Key words: fault diagnosis, residual network, deep learning, artificial intelligence

CLC Number: