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A cable defect assessment method based on high-voltage XLPE cable evolved gas and multi-scale convolutional features fusion
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Tao SUN1, Liangpeng YE1, Fan ZHANG2, Jiaqing ZHANG1, Yi GUO1, Kai ZHOU2, Xuyang MIAO1
Insulating Materials | 2025, 58(3) : 117 - 124
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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
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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
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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
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
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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
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  • Received：2024-05-10
  • Revised：2024-06-12
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    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
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https://castjournals.cast.org.cn/joweb/jycl/EN/10.16790/j.cnki.1009-9239.im.2025.03.013
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光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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