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Data-Driven Fault Localization in Distribution Systems with Distributed Energy Resources

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  • Zhidi Lin

    (Future Network of Intelligence Institute (FNii), The Chinese University of Hong Kong, Shenzhen 518172, China
    Shenzhen Research Institute of Big Data (SRIBD), Shenzhen 518172, China
    School of Informatics, Xiamen University, Xiamen 361005, China)

  • Dongliang Duan

    (Shenzhen Research Institute of Big Data (SRIBD), Shenzhen 518172, China
    Department of Electrical and Computer Engineering, University of Wyoming, Laramie, WY 82071, USA)

  • Qi Yang

    (School of Informatics, Xiamen University, Xiamen 361005, China)

  • Xuemin Hong

    (School of Informatics, Xiamen University, Xiamen 361005, China)

  • Xiang Cheng

    (Shenzhen Research Institute of Big Data (SRIBD), Shenzhen 518172, China
    School of Electronics Engineering and Computer Science, Peking University, Beijing 100080, China)

  • Liuqing Yang

    (Department of Electrical and Computer Engineering, Colorado State University, Fort Collins, CO 80523, USA)

  • Shuguang Cui

    (Future Network of Intelligence Institute (FNii), The Chinese University of Hong Kong, Shenzhen 518172, China
    Shenzhen Research Institute of Big Data (SRIBD), Shenzhen 518172, China
    Department of Electrical and Computer Engineering, University of California, Davis, CA 95616, USA)

Abstract

The integration of Distributed Energy Resources (DERs) introduces a non-conventional two-way power flow which cannot be captured well by traditional model-based techniques. This brings an unprecedented challenge in terms of the accurate localization of faults and proper actions of the protection system. In this paper, we propose a data-driven fault localization strategy based on multi-level system regionalization and the quantification of fault detection results in all subsystems/subregions. This strategy relies on the tree segmentation criterion to divide the entire system under study into several subregions, and then combines Support Vector Data Description (SVDD) and Kernel Density Estimation (KDE) to find the confidence level of fault detection in each subregion in terms of their corresponding p -values. By comparing the p -values, one can accurately localize the faults. Experiments demonstrate that the proposed data-driven fault localization can greatly improve the accuracy of fault localization for distribution systems with high DER penetration.

Suggested Citation

  • Zhidi Lin & Dongliang Duan & Qi Yang & Xuemin Hong & Xiang Cheng & Liuqing Yang & Shuguang Cui, 2020. "Data-Driven Fault Localization in Distribution Systems with Distributed Energy Resources," Energies, MDPI, vol. 13(1), pages 1-16, January.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:1:p:275-:d:305584
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    References listed on IDEAS

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    1. Gururajapathy, S.S. & Mokhlis, H. & Illias, H.A., 2017. "Fault location and detection techniques in power distribution systems with distributed generation: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 949-958.
    2. Zhengqi Jiang & Vinit Sahasrabudhe & Ahmed Mohamed & Haim Grebel & Roberto Rojas-Cessa, 2019. "Greedy Algorithm for Minimizing the Cost of Routing Power on a Digital Microgrid," Energies, MDPI, vol. 12(16), pages 1-19, August.
    3. Simone Minniti & Niyam Haque & Phuong Nguyen & Guus Pemen, 2018. "Local Markets for Flexibility Trading: Key Stages and Enablers," Energies, MDPI, vol. 11(11), pages 1-21, November.
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    Cited by:

    1. Ying Tian & Qiang Zou & Jin Han, 2021. "Data-Driven Fault Diagnosis for Automotive PEMFC Systems Based on the Steady-State Identification," Energies, MDPI, vol. 14(7), pages 1-17, March.
    2. Yuriy Bilan & Marcin Rabe & Katarzyna Widera, 2022. "Distributed Energy Resources: Operational Benefits," Energies, MDPI, vol. 15(23), pages 1-7, November.
    3. Mirosław Kornatka & Anna Gawlak, 2021. "An Analysis of the Operation of Distribution Networks Using Kernel Density Estimators," Energies, MDPI, vol. 14(21), pages 1-12, October.
    4. Jan Maciej Kościelny & Michał Syfert & Paweł Wnuk, 2021. "Diagnostic Row Reasoning Method Based on Multiple-Valued Evaluation of Residuals and Elementary Symptoms Sequence," Energies, MDPI, vol. 14(9), pages 1-18, April.

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