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Aging Detection of 110 kV XLPE Cable for a CFETR Power Supply System Based on Deep Neural Network

Author

Listed:
  • Hui Chen

    (Institutes of Plasma Physics, Chinese Academy of Sciences, Hefei 230031, China
    Scinece Island Branch, Graduate School of USTC, Hefei 230026, China)

  • Junjia Wang

    (Institutes of Plasma Physics, Chinese Academy of Sciences, Hefei 230031, China)

  • Hejun Hu

    (Institutes of Plasma Physics, Chinese Academy of Sciences, Hefei 230031, China
    Scinece Island Branch, Graduate School of USTC, Hefei 230026, China)

  • Xiaofeng Li

    (Institutes of Plasma Physics, Chinese Academy of Sciences, Hefei 230031, China
    Scinece Island Branch, Graduate School of USTC, Hefei 230026, China)

  • Yiyun Huang

    (Institutes of Plasma Physics, Chinese Academy of Sciences, Hefei 230031, China)

Abstract

To detect the aging of power cables in the TOKAMAK power supply systems, this paper proposed a deep neural network diagnosis model and algorithm for power cable aging, based on logistic regression according to the characteristics of different high-order harmonics generated by different aging parts of the power cable. The experimental results showed that the model has high diagnostic accuracy, and the average error is only 2.35%. The method proposed in this paper has certain application potential in the CFETR power cable auxiliary monitoring system.

Suggested Citation

  • Hui Chen & Junjia Wang & Hejun Hu & Xiaofeng Li & Yiyun Huang, 2022. "Aging Detection of 110 kV XLPE Cable for a CFETR Power Supply System Based on Deep Neural Network," Energies, MDPI, vol. 15(9), pages 1-12, April.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:9:p:3127-:d:801582
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