LAPSE:2023.13489v1
Published Article
LAPSE:2023.13489v1
Aging Detection of 110 kV XLPE Cable for a CFETR Power Supply System Based on Deep Neural Network
March 1, 2023
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.
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.
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Keywords
cable aging, CFETR, deep neural network, high harmonic content, TOKAMAK
Suggested Citation
Chen H, Wang J, Hu H, Li X, Huang Y. Aging Detection of 110 kV XLPE Cable for a CFETR Power Supply System Based on Deep Neural Network. (2023). LAPSE:2023.13489v1
Author Affiliations
Chen H: Institutes of Plasma Physics, Chinese Academy of Sciences, Hefei 230031, China; Scinece Island Branch, Graduate School of USTC, Hefei 230026, China [ORCID]
Wang J: Institutes of Plasma Physics, Chinese Academy of Sciences, Hefei 230031, China
Hu H: Institutes of Plasma Physics, Chinese Academy of Sciences, Hefei 230031, China; Scinece Island Branch, Graduate School of USTC, Hefei 230026, China
Li X: Institutes of Plasma Physics, Chinese Academy of Sciences, Hefei 230031, China; Scinece Island Branch, Graduate School of USTC, Hefei 230026, China
Huang Y: Institutes of Plasma Physics, Chinese Academy of Sciences, Hefei 230031, China
Wang J: Institutes of Plasma Physics, Chinese Academy of Sciences, Hefei 230031, China
Hu H: Institutes of Plasma Physics, Chinese Academy of Sciences, Hefei 230031, China; Scinece Island Branch, Graduate School of USTC, Hefei 230026, China
Li X: Institutes of Plasma Physics, Chinese Academy of Sciences, Hefei 230031, China; Scinece Island Branch, Graduate School of USTC, Hefei 230026, China
Huang Y: Institutes of Plasma Physics, Chinese Academy of Sciences, Hefei 230031, China
Journal Name
Energies
Volume
15
Issue
9
First Page
3127
Year
2022
Publication Date
2022-04-25
ISSN
1996-1073
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PII: en15093127, Publication Type: Journal Article
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LAPSE:2023.13489v1
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https://doi.org/10.3390/en15093127
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Mar 1, 2023
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