IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v18y2025i10p2551-d1655866.html
   My bibliography  Save this article

Enhanced Fault Localization for Active Distribution Networks via Robust Three-Phase State Estimation

Author

Listed:
  • Guorun He

    (State Key Laboratory of Intelligent Power Distribution Equipment and System, Hebei University of Technology, Tianjin 300401, China
    Innovation Research Institute of Hebei University of Technology in Shijiazhuang, Shijiazhuang 050200, China)

  • Dong Liang

    (State Key Laboratory of Intelligent Power Distribution Equipment and System, Hebei University of Technology, Tianjin 300401, China
    Innovation Research Institute of Hebei University of Technology in Shijiazhuang, Shijiazhuang 050200, China)

  • Yuezi Zhao

    (State Key Laboratory of Intelligent Power Distribution Equipment and System, Hebei University of Technology, Tianjin 300401, China
    Innovation Research Institute of Hebei University of Technology in Shijiazhuang, Shijiazhuang 050200, China)

  • Xiaoxue Wang

    (State Key Laboratory of Intelligent Power Distribution Equipment and System, Hebei University of Technology, Tianjin 300401, China
    Innovation Research Institute of Hebei University of Technology in Shijiazhuang, Shijiazhuang 050200, China)

Abstract

Accurate fault localization is critical for ensuring reliable power supply in active distribution networks, yet conventional state estimation (SE)-based methods fail to differentiate authentic fault responses from measurement distortions due to uncertainties in fault parameters. To overcome this limitation, a robust three-phase SE-driven fault localization methodology is proposed. First, a measurement transformation-based SE model is built for fault conditions, leveraging real-time voltage phasor measurements and pseudo-measurements derived from pre-fault SE results. Then, a robust fault SE model is built using the quadratic-constant-based generalized maximum likelihood estimation, solved through the iteratively reweighted least squares algorithm that postpones phasor measurement weight updates until after initial iterations to prevent residual contamination. Furthermore, a fault localization algorithm is proposed through the systematic traversal of candidate buses, where each potential fault localization is assessed by performing robust fault SE with the fault current injected into this bus. The matching index is designed, accounting for the weight disparity of different types of measurements and measurement placement. Extensive simulations on a 33-bus unbalanced distribution network validate the method’s effectiveness under various measurement noise levels, fault resistances and incorrect data severity. The approach maintains comparable accuracy to conventional SE under normal operating conditions, while it exhibits superior robustness against measurement anomalies and effectively preserves fault localization reliability when confronted with incorrect data.

Suggested Citation

  • Guorun He & Dong Liang & Yuezi Zhao & Xiaoxue Wang, 2025. "Enhanced Fault Localization for Active Distribution Networks via Robust Three-Phase State Estimation," Energies, MDPI, vol. 18(10), pages 1-17, May.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:10:p:2551-:d:1655866
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/18/10/2551/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/18/10/2551/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Rizeakos, V. & Bachoumis, A. & Andriopoulos, N. & Birbas, M. & Birbas, A., 2023. "Deep learning-based application for fault location identification and type classification in active distribution grids," Applied Energy, Elsevier, vol. 338(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Islam, Md. Zahidul & Lin, Yuzhang & Vokkarane, Vinod M. & Yu, Nanpeng, 2023. "Robust learning-based real-time load estimation using sparsely deployed smart meters with high reporting rates," Applied Energy, Elsevier, vol. 352(C).
    2. Wang, Tian & Yin, Linfei, 2024. "Dual-module multi-head spatiotemporal joint network with SACGA for wind turbines fault detection," Energy, Elsevier, vol. 308(C).
    3. Wadim Strielkowski & Andrey Vlasov & Kirill Selivanov & Konstantin Muraviev & Vadim Shakhnov, 2023. "Prospects and Challenges of the Machine Learning and Data-Driven Methods for the Predictive Analysis of Power Systems: A Review," Energies, MDPI, vol. 16(10), pages 1-31, May.
    4. Alam, Md Morshed & Hossain, M.J. & Habib, Md Ahasan & Arafat, M.Y. & Hannan, M.A., 2025. "Artificial intelligence integrated grid systems: Technologies, potential frameworks, challenges, and research directions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 211(C).
    5. Zhang, Pan & Mansouri, Seyed Amir & Rezaee Jordehi, Ahmad & Tostado-Véliz, Marcos & Alharthi, Yahya Z. & Safaraliev, Murodbek, 2024. "An ADMM-enabled robust optimization framework for self-healing scheduling of smart grids integrated with smart prosumers," Applied Energy, Elsevier, vol. 363(C).

    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:10:p:2551-:d:1655866. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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.