IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v18y2025i14p3662-d1699118.html
   My bibliography  Save this article

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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Qianqiu Shao & Songhai Fan & Zongxi Zhang & Fenglian Liu & Zhengzheng Fu & Pinlei Lv & Zhou Mu, 2025. "Artificial Intelligence in Cable Fault Detection and Localization: Recent Advances and Research Challenges," Energies, MDPI, vol. 18(14), pages 1-35, July.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:14:p:3662-:d:1699118
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/18/14/3662/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/18/14/3662/
    Download Restriction: no
    ---><---

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:18:y:2025:i:14:p:3662-:d:1699118. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.