全球能源互联网（英文）2023，Vol.6Issue(1)：36-49,14.DOI:10.1016/j.gloei.2023.02.004
基于深度学习的XLPE电缆绝缘缺陷识别
Identification of XLPE cable insulation defects based on deep learning
摘要
Abstract
The insulation aging of cross-linked polyethylene (XLPE) cables is the main reason for the reduction in cable life. There is currently a lack of rapid and effective methods for detecting cable insulation defects in power-related sectors. To this end, this paper presents a method for identifying insulation defects in XLPE cables based on deep learning algorithms. First, the principle of the harmonic method for detecting cable insulation defects is introduced. Second, the ANSYS software is used to simulate the cable insulation layer containing bubbles, protrusions, and water tree defects, and the effects of each type of defect on the magnetic field strength and eddy loss current of the cable insulation layer are analyzed. Then, a total of 10 characteristic quantities of the total harmonic content and 2nd to 10th harmonic currents are constructed to establish a database of cable insulation defects. Finally, the deep learning algorithm, long short-term memory (LSTM), is used to accurately identify the types of insulation defects in cables. The results indicate that the LSTM algorithm can effectively diagnose and identify insulation defects in cables with an accuracy of 95.83％.关键词
绝缘缺陷/深度学习/大数据/涡损电流Key words
Insulation defects/Deep learning/Database/Eddy loss current引用本文复制引用
周涛,朱晓中,杨海飞,闫旭阳,靳雪君,万庆祝..基于深度学习的XLPE电缆绝缘缺陷识别[J].全球能源互联网（英文）,2023,6(1):36-49,14.基金项目
This work was supported by the technology project of the State Grid Shanxi Electric Power Company.The name of the project is"Research and Application of Cable electrification diagnosis Technology based on Harmonic method"(5205C02000GL). （5205C02000GL）
