Research on PRS/IRS Time Registration Based on Fully connected Neural Network

Wang, Wangyang; Dong, Wenfeng; Yu, Tianli; Du, Yuansong

doi:10.1109/itaic49862.2020.9339062

2020 IEEE 9th Joint International Information Technology and Artificial Intelligence Conference (ITAIC) 2020

DOI: 10.1109/itaic49862.2020.9339062

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Research on PRS/IRS Time Registration Based on Fully connected Neural Network

Wangyang Wang¹,

Wenfeng Dong²,

Tianli Yu³

et al.

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

Chen

Wang

et al. 2022

Energies

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

Chen

Wang

et al. 2022

Energies

View full text Add to dashboard Cite

show abstract

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Research on PRS/IRS Time Registration Based on Fully connected Neural Network

Cited by 1 publication

References 1 publication

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

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

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