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

Hui Chen, Junjia Wang, Hejun Hu, Xiaofeng Li and Yiyun Huang
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Hui Chen: Institutes of Plasma Physics, Chinese Academy of Sciences, Hefei 230031, 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
Xiaofeng Li: Institutes of Plasma Physics, Chinese Academy of Sciences, Hefei 230031, China
Yiyun Huang: Institutes of Plasma Physics, Chinese Academy of Sciences, Hefei 230031, China

Energies, 2022, vol. 15, issue 9, 1-12

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.

Keywords: TOKAMAK; cable aging; CFETR; high harmonic content; deep neural network (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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