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Fault Current Tracing and Identification via Machine Learning Considering Distributed Energy Resources in Distribution Networks

Author

Listed:
  • Wanghao Fei

    (School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK 73019, USA)

  • Paul Moses

    (School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK 73019, USA)

Abstract

The growth of intermittent distributed energy sources (DERs) in distribution grids is raising many new operational challenges for utilities. One major problem is the back feed power flows from DERs that complicate state estimation for practical problems, such as detection of lower level fault currents, that cause the poor accuracy of fault current identification for power system protection. Existing artificial intelligence (AI)-based methods, such as support vector machine (SVM), are unable to detect lower level faults especially from inverter-based DERs that offer limited fault currents. To solve this problem, a current tracing method (CTM) has been proposed to model the single distribution feeder as several independent parallel connected virtual lines that traces the detailed contribution of different current sources to the power line current. Moreover, for the first time, the enhanced current information is used as the expanded feature space of SVM to significantly improve fault current detection on the power line. The proposed method is shown to be sensitive to very low level fault currents which is validated through simulations.

Suggested Citation

  • Wanghao Fei & Paul Moses, 2019. "Fault Current Tracing and Identification via Machine Learning Considering Distributed Energy Resources in Distribution Networks," Energies, MDPI, vol. 12(22), pages 1-12, November.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:22:p:4333-:d:286686
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    Citations

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    Cited by:

    1. Govind Sahay Yogee & Om Prakash Mahela & Kapil Dev Kansal & Baseem Khan & Rajendra Mahla & Hassan Haes Alhelou & Pierluigi Siano, 2020. "An Algorithm for Recognition of Fault Conditions in the Utility Grid with Renewable Energy Penetration," Energies, MDPI, vol. 13(9), pages 1-22, May.
    2. Barja-Martinez, Sara & Aragüés-Peñalba, Mònica & Munné-Collado, Íngrid & Lloret-Gallego, Pau & Bullich-Massagué, Eduard & Villafafila-Robles, Roberto, 2021. "Artificial intelligence techniques for enabling Big Data services in distribution networks: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 150(C).
    3. Akilu Yunusa-Kaltungo & Ruifeng Cao, 2020. "Towards Developing an Automated Faults Characterisation Framework for Rotating Machines. Part 1: Rotor-Related Faults," Energies, MDPI, vol. 13(6), pages 1-20, March.
    4. 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).
    5. Hernandez-Matheus, Alejandro & Löschenbrand, Markus & Berg, Kjersti & Fuchs, Ida & Aragüés-Peñalba, Mònica & Bullich-Massagué, Eduard & Sumper, Andreas, 2022. "A systematic review of machine learning techniques related to local energy communities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 170(C).

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