Artificial Intelligence in Cable Fault Detection and Localization: Recent Advances and Research Challenges
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 traditi...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-07-01
|
| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/18/14/3662 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849246511704047616 |
|---|---|
| author | Qianqiu Shao Songhai Fan Zongxi Zhang Fenglian Liu Zhengzheng Fu Pinlei Lv Zhou Mu |
| author_facet | Qianqiu Shao Songhai Fan Zongxi Zhang Fenglian Liu Zhengzheng Fu Pinlei Lv Zhou Mu |
| author_sort | Qianqiu Shao |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-c2e7f363cd0b40e08ae7caf2d218ecfd |
| institution | Kabale University |
| issn | 1996-1073 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-c2e7f363cd0b40e08ae7caf2d218ecfd2025-08-20T03:58:27ZengMDPI AGEnergies1996-10732025-07-011814366210.3390/en18143662Artificial Intelligence in Cable Fault Detection and Localization: Recent Advances and Research ChallengesQianqiu Shao0Songhai Fan1Zongxi Zhang2Fenglian Liu3Zhengzheng Fu4Pinlei Lv5Zhou Mu6Power Transmission and Transformation Technology Center, State Grid Sichuan Electric Power Research Institute, Chengdu 610095, ChinaPower Transmission and Transformation Technology Center, State Grid Sichuan Electric Power Research Institute, Chengdu 610095, ChinaPower Transmission and Transformation Technology Center, State Grid Sichuan Electric Power Research Institute, Chengdu 610095, ChinaPower Transmission and Transformation Technology Center, State Grid Sichuan Electric Power Research Institute, Chengdu 610095, ChinaPower Transmission and Transformation Technology Center, State Grid Sichuan Electric Power Research Institute, Chengdu 610095, ChinaPower Transmission and Transformation Technology Center, State Grid Sichuan Electric Power Research Institute, Chengdu 610095, ChinaPower Transmission and Transformation Technology Center, State Grid Sichuan Electric Power Research Institute, Chengdu 610095, ChinaWith 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.https://www.mdpi.com/1996-1073/18/14/3662cable faultfault detectionfault locationartificial intelligencedeep learning |
| spellingShingle | Qianqiu Shao Songhai Fan Zongxi Zhang Fenglian Liu Zhengzheng Fu Pinlei Lv Zhou Mu Artificial Intelligence in Cable Fault Detection and Localization: Recent Advances and Research Challenges Energies cable fault fault detection fault location artificial intelligence deep learning |
| title | Artificial Intelligence in Cable Fault Detection and Localization: Recent Advances and Research Challenges |
| title_full | Artificial Intelligence in Cable Fault Detection and Localization: Recent Advances and Research Challenges |
| title_fullStr | Artificial Intelligence in Cable Fault Detection and Localization: Recent Advances and Research Challenges |
| title_full_unstemmed | Artificial Intelligence in Cable Fault Detection and Localization: Recent Advances and Research Challenges |
| title_short | Artificial Intelligence in Cable Fault Detection and Localization: Recent Advances and Research Challenges |
| title_sort | artificial intelligence in cable fault detection and localization recent advances and research challenges |
| topic | cable fault fault detection fault location artificial intelligence deep learning |
| url | https://www.mdpi.com/1996-1073/18/14/3662 |
| work_keys_str_mv | AT qianqiushao artificialintelligenceincablefaultdetectionandlocalizationrecentadvancesandresearchchallenges AT songhaifan artificialintelligenceincablefaultdetectionandlocalizationrecentadvancesandresearchchallenges AT zongxizhang artificialintelligenceincablefaultdetectionandlocalizationrecentadvancesandresearchchallenges AT fenglianliu artificialintelligenceincablefaultdetectionandlocalizationrecentadvancesandresearchchallenges AT zhengzhengfu artificialintelligenceincablefaultdetectionandlocalizationrecentadvancesandresearchchallenges AT pinleilv artificialintelligenceincablefaultdetectionandlocalizationrecentadvancesandresearchchallenges AT zhoumu artificialintelligenceincablefaultdetectionandlocalizationrecentadvancesandresearchchallenges |