Journal of Power Electronics

The Korean Institute of Power Electronics (전력전자학회)
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Partial discharge detection of insulated conductors based on CNN-LSTM of attention mechanisms

  • Li, Zhongzhi (School of Computer Science, Shenyang Aerospace University) ;
  • Qu, Na (School of Safety Engineering, Shenyang Aerospace University) ;
  • Li, Xiaoxue (School of Electronic and Information Engineering, Shenyang Aerospace University) ;
  • Zuo, Jiankai (Department of Computer Science and Technology, Tongji University) ;
  • Yin, Yanzhen (School of Science, Shenyang Aerospace University)
  • Received : 2020.09.26
  • Accepted : 2021.03.09
  • Published : 2021.07.20
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Abstract

Under the condition of a strong electric field, partial discharge often occurs when insulated wire is damaged. The recognition of partial discharge is an effective method for the fast and accurate detection of high voltage insulated wire faults. This paper proposes a PD recognition algorithm based on a convolutional neural network and long short-term memory (LSTM). In addition, attention mechanisms are introduced to give separate weights to LSTM hidden states through a mapping, weighting, and learning parameter matrix. This is done to reduce the loss of historical information and to strengthen the influence of important information. The complex relationship between the voltage signal change and the grid operation state response has been established. The proposed method is verified by the ENET data set published by VSB University. The recognition accuracy is 95.16% for no-PD and 94.44% for PD. Results from the proposed algorithm show that this method has a higher detection accuracy.

Keywords

Acknowledgement

This work was supported by the National Natural Science Foundation of China (Grant No. 61901283), and the College students training program of innovation and entrepreneurship in Shenyang Aerospace University (Grant No. 202010143007).

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