<?xml version="1.1" encoding="utf-8"?>
<article xsi:noNamespaceSchemaLocation="http://jats.nlm.nih.gov/publishing/1.1/xsd/JATS-journalpublishing1-mathml3.xsd" dtd-version="1.1" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"><front><journal-meta><journal-id journal-id-type="publisher-id">ME</journal-id><journal-title-group><journal-title>Modern Engineering</journal-title></journal-title-group><issn>2996-6973</issn><eissn>2996-6981</eissn><publisher><publisher-name>Art and Design</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.61369/ME.2025070031</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title>电力电缆故障诊断研究综述</title><url>https://artdesignp.com/journal/ME/2/7/10.61369/ME.2025070031</url><author>程子霞,李子扬,李弈杰</author><pub-date pub-type="publication-year"><year>2025</year></pub-date><volume>2</volume><issue>7</issue><history><date date-type="pub"><published-time>2025-07-20</published-time></date></history><abstract>电力电缆是城市基础建设中必不可少的一环，是传输电能的重要载体，它的是否正常运行关系到城市生产生活的安全和稳定，因此电力电缆线路的故障诊断是一直以来的关键问题。为了保证电力电缆安全可靠地运行，必须快速、准确地检测出故障。本文简述了电力电缆故障产生的原因并对电力电缆的故障检测以及定位方法进行了梳理，对比分析了不同类型的电力电缆故障检测以及定位方法，以此推进电力电缆故障诊断的研究和应用工作。</abstract><keywords>电力电缆,故障诊断,故障识别,故障定位</keywords></article-meta></front><body/><back><ref-list><ref id="B1" content-type="article"><label>1</label><element-citation publication-type="journal"><p>[1] 贾宏涛. 基于深度学习的电力电缆故障在线诊断研究[D]. 西安理工大学,2023.[2] 张兴超, 肖妮, 王陆. 一种电力电缆故障定位系统设计[J].中国科技信息,2025,(13):119-122.[3] 袁燕岭, 李世松, 董杰, 等. 电力电缆诊断检测技术综述[J].电测与仪表,2016,53(11):1-7.[4] 高青松, 杨靖. 电力电缆故障诊断研究综述[J].贵州电力技术,2016,19(05):54-58.[5] 姚海燕, 张静, 留毅, 等. 基于多尺度小波判据和时频特征关联的电缆早期故障检测和识别方法[J].电力系统保护与控制,2015,43(09):115-123.[6] 李根, 周文俊, 喻莹, 等. 邻近共地高压电缆线路护层电流特征与故障诊断[J].高电压技术,2025,51(02):698-707.[7]周正雄, 夏向阳, 朱鹏, 等. 高压电缆早期间歇性电弧接地故障识别方法[J].中国电力,2020,53(12):167-176.[8] Wang, L., Li, Z., Wang, X., Zhang, P., Zhang, J., &amp;amp; Li, Z. (2023, December). Cable Fault Diagnosis Based on Metal Sheath Grounding Current Monitoring Technology. In 2023 IEEE 6th International Conference on Automation, Electronics and Electrical Engineering (AUTEEE) (pp. 748-752). IEEE.[9] 张育粱. 基于护层电流在线监测的高压电缆故障识别方法与系统设计研究[D]. 长沙理工大学,2022.[10] Wang, D., Zhang, X., Guo, L., Hu, Y., Ma, H., Liu, B., &amp;amp; Zhou, L. (2024). Fault diagnosis for terminal of 10-kV XLPE cable based on the improved m-training algorithm. IEEE Sensors Journal, 24(8), 13142-13152.[11] Anitha, B., Mathavan, S., Santhosh, S., &amp;amp; Sowmiya, R. (2025, April). Machine Learning for Fault Detection and Localization in Underground Power Cables: Improving Reliability in Power Systems. In 2025 5th International Conference on Trends in Material Science and Inventive Materials (ICTMIM) (pp. 1516-1521). IEEE.[12] 吴川兰, 吴浩, 邱富泓, 等. 电力电缆早期故障诊断技术研究综述[J].光纤与电缆及其应用技术,2025,(01):16-20+32.[13] 高超, 刘泽辉, 曹栋, 等. 基于1DCNN-BiLSTM的电力电缆故障诊断[J].郑州大学学报( 工学版),2023,44(05):86-92.[14] 肖旰, 周莉, 李敬兆, 等. 基于EEMD融合BAS-CNN的高压电缆故障诊断[J].电子测量技术,2022,45(04):160-167.[15] 李效明. 结合Light-GBM算法和CNN-BiLSTM算法的改进电缆故障诊断方法[J].电气自动化,2025,47(02):108-111.[16] Zhang, H. (2023, August). Cable Fault Detection and Diagnosis Method Based on Convolutional Neural Network. In 2023 IEEE International Conference on Image Processing and Computer Applications (ICIPCA) (pp. 1463-1466). IEEE.[17] Dong, Z., Feng, B., Hu, X., Zeng, H., Wu, Y., &amp;amp; Chen, Q. (2023, December). Cable Fault Identification Method Based on mRMR and Optimized Convolutional Neural Network. In 2023 6th International Conference on Electronics and Electrical Engineering Technology (EEET) (pp. 99-104). IEEE.[18] Liu, C., Qi, Y., Zhang, Y., Xu, Z., Wang, S., Ding, Y., ... &amp;amp; Wu, Y. (2024, September). Diagnosis Method for Partial Discharge Faults in Power Cables Based on Deep Learning. In 2024 The 9th International Conference on Power and Renewable Energy (ICPRE) (pp. 91-96). IEEE.[19] Liu, Y., Qiu, Y., Chen, Q., Xie, Q., Lin, L., &amp;amp; Zhang, T. (2025, May). Research on Power Cable Fault Diagnosis Based on Sparrow Search Algorithm. In 2025 IEEE 5th International Conference on Electronic Technology, Communication and Information (ICETCI) (pp. 310-314). IEEE.[20] 王荣亮, 李天翼, 王浩楠. 基于行波互相关法的电力电缆故障定位技术研究[J].电气技术与经济,2025,(03):96-98.[21] 冯新宇, 柴侨峥, 付志伟, 等. 改进EWT算法的高压电缆局部放电故障定位方法[J].黑龙江科技大学学报,2023,33(02):259-265.[22] 付大赓. 高压电缆局部放电源定位技术研究[D]. 大连交通大学,2020.[23] 孙春雨. 基于高频信号幅频特征的电缆局部放电定位技术研究[D]. 华北电力大学( 北京),2023.[24] 隽永飞, 袁志文, 亓松. 我国电力电缆行业发展现状及展望[J].电力与能源,2025,46(03):237-244.[25] 唐丹, 吴浩, 蔡源. 电力电缆早期故障诊断研究综述[J].电线电缆,2023,(06):1-5.</p><pub-id pub-id-type="doi"/></element-citation></ref></ref-list></back></article>
