Home Journals Articles Updates About
CN
image
收藏切换
A cable defect assessment method based on high-voltage XLPE cable evolved gas and multi-scale convolutional features fusion
收藏切换
Tao SUN1, Liangpeng YE1, Fan ZHANG2, Jiaqing ZHANG1, Yi GUO1, Kai ZHOU2, Xuyang MIAO1
Insulating Materials | 2025, 58(3) : 117 - 124
Less
收藏切换
Insulating Materials | 2025, 58(3): 117-124
• Special Issue on Advanced Cable Insulation •
A cable defect assessment method based on high-voltage XLPE cable evolved gas and multi-scale convolutional features fusion
Full
Tao SUN1, Liangpeng YE1, Fan ZHANG2, Jiaqing ZHANG1, Yi GUO1, Kai ZHOU2, Xuyang MIAO1
Affiliations
  • 1 State Grid Anhui Electric Power Co., Ltd. Electric Power Research Institute, Hefei 230601, China
  • 2 College of Electrical Engineering, Sichuan University, Chengdu 610065, China
Published: 2025-03-20 doi: 10.16790/j.cnki.1009-9239.im.2025.03.013
Outline
收藏切换

This paper proposed a defect assessment method for high-voltage XLPE cable based on a multi-scale correlation feature fusion convolutional neural network. On the basis of a data-driven approach, this method established the potential relationship model between characteristic gas concentration and defect type by training a convolutional neural network, thereby diagnosing the cable defects based on characteristic gas concentration. Firstly, simulated data were obtained using a data augmentation technique based on mean shift. Then, a 1D convolutional neural network based on multi-scale correlation feature fusion was designed. Finally, the training and defect identification were carried out on the basis of simulation data by using the convolutional neural network. The results show that the method on the synthetic data test set and the real basic data achieves defect recognition accuracies of 92% and 88%, respectively. It is indicated that the proposed method can effectively utilize characteristic gas concentration to diagnose cable defects.

cable early fault diagnosis  /  characteristic gas analysis  /  convolutional neural network  /  data-driven  /  mean shift
孙韬, 叶良鹏, 张帆, 张佳庆, 过羿, 周凯, 缪煦扬. 基于高压XLPE电缆逸出气体与多尺度卷积特征融合的电缆缺陷评估方法研究. 绝缘材料, 2025 , 58 (3) : 117 -124 . DOI: 10.16790/j.cnki.1009-9239.im.2025.03.013
Tao SUN, Liangpeng YE, Fan ZHANG, Jiaqing ZHANG, Yi GUO, Kai ZHOU, Xuyang MIAO. A cable defect assessment method based on high-voltage XLPE cable evolved gas and multi-scale convolutional features fusion[J]. Insulating Materials, 2025 , 58 (3) : 117 -124 . DOI: 10.16790/j.cnki.1009-9239.im.2025.03.013
Appendix
Less
Year 2025 volume 58 Issue 3
802
394
Cite this Article
BibTeX
Article Info
doi: 10.16790/j.cnki.1009-9239.im.2025.03.013
  • Receive Date：2024-05-10
  • Online Date：2025-11-07
  • Published：2025-03-20
Article Data
Affiliations
History
  • Received：2024-05-10
  • Revised：2024-06-12
Funding
Affiliations
    1 State Grid Anhui Electric Power Co., Ltd. Electric Power Research Institute, Hefei 230601, China
    2 College of Electrical Engineering, Sichuan University, Chengdu 610065, China
References
Share
https://castjournals.cast.org.cn/joweb/jycl/EN/10.16790/j.cnki.1009-9239.im.2025.03.013
Share to
QR

Scan QR to access full text

Cite this article
BibTeX
Citations
表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
关闭全屏
  • BibTeX
  • EndNote
  • RefWorks
  • TxT
Links
Articles
Latest Articles
Most Read
Collections
Updates
Events
News
Multimedia
About
About Us
Contact
No. 86 Xueyuan South Road, Haidian District, Beijing
100081
010-62662699(Operation and Technical Support)
qkjq@cast.org.cn

Copyright © 2025 China Association for Science and Technology. All rights reserved. For all open access content, the relevant licensing terms apply.
Sponsored by the Office of the Leading Group for Cybersecurity and Informatization of CAST, and supported by Science and Technology Review Publishing House