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

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