Artificial Intelligence in Cable Fault Detection and Localization: Recent Advances and Research Challenges

Qianqiu Shao (), Songhai Fan, Zongxi Zhang, Fenglian Liu, Zhengzheng Fu, Pinlei Lv and Zhou Mu
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Qianqiu Shao: Power Transmission and Transformation Technology Center, State Grid Sichuan Electric Power Research Institute, Chengdu 610095, China
Songhai Fan: Power Transmission and Transformation Technology Center, State Grid Sichuan Electric Power Research Institute, Chengdu 610095, China
Zongxi Zhang: Power Transmission and Transformation Technology Center, State Grid Sichuan Electric Power Research Institute, Chengdu 610095, China
Fenglian Liu: Power Transmission and Transformation Technology Center, State Grid Sichuan Electric Power Research Institute, Chengdu 610095, China
Zhengzheng Fu: Power Transmission and Transformation Technology Center, State Grid Sichuan Electric Power Research Institute, Chengdu 610095, China
Pinlei Lv: Power Transmission and Transformation Technology Center, State Grid Sichuan Electric Power Research Institute, Chengdu 610095, China
Zhou Mu: Power Transmission and Transformation Technology Center, State Grid Sichuan Electric Power Research Institute, Chengdu 610095, China

Energies, 2025, vol. 18, issue 14, 1-35

Abstract: With the large-scale integration of new power systems and distributed generators (DGs), cable fault detection and localization face numerous challenges, where artificial intelligence (AI) techniques demonstrate significant advantages. This review first outlines the causes of cable faults and traditional methods for fault detection and localization. Subsequently, it comprehensively analyzes the applications of both conventional machine learning and deep learning approaches in this field, elaborating on their application scenarios, strengths, defects, and successful case studies, providing valuable references for researchers and professionals. Additionally, the paper discusses the strengths and limitations of current AI techniques, along with the impacts introduced by DG integration. Finally, it highlights future development trends and potential research directions for advancing AI-based solutions in cable fault detection and localization.

Keywords: cable fault; fault detection; fault location; artificial intelligence; deep learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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