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A Hilbert–Huang Transform-Based Adaptive Fault Detection and Classification Method for Microgrids

Author

Listed:
  • Yijin Li

    (School of Mechanical Electronic and Information Engineering, China University of Mining and Technology-Beijing, Haidian District, Beijing 100083, China)

  • Jianhua Lin

    (School of Mechanical Electronic and Information Engineering, China University of Mining and Technology-Beijing, Haidian District, Beijing 100083, China)

  • Geng Niu

    (State Grid Shanghai Energy Interconnection Research Institute, China Electric Power Research Institute, Haidian District, Beijing 100192, China)

  • Ming Wu

    (State Grid Shanghai Energy Interconnection Research Institute, China Electric Power Research Institute, Haidian District, Beijing 100192, China)

  • Xuteng Wei

    (School of Mechanical Electronic and Information Engineering, China University of Mining and Technology-Beijing, Haidian District, Beijing 100083, China)

Abstract

Fault detection in microgrids is of great significance for power systems’ safety and stability. Due to the high penetration of distributed generations, fault characteristics become different from those of traditional fault detection. Thus, we propose a new fault detection and classification method for microgrids. Only current information is needed for the method. Hilbert–Huang Transform and sliding window strategy are used in fault characteristic extraction. The instantaneous phase difference of current high-frequency component is obtained as the fault characteristic. A self-adaptive threshold is set to increase the detection sensitivity. A fault can be detected by comparing the fault characteristic and the threshold. Furthermore, the fault type is identified by the utilization of zero-sequence current. Simulations for both section and system have been completed. The instantaneous phase difference of the current high-frequency component is an effective fault characteristic for detecting ten kinds of faults. Using the proposed method, the maximum fault detection time is 13.8 ms and the maximum fault type identification time is 14.8 ms. No misjudgement happens under non-fault disturbance conditions. The simulations indicate that the proposed method can achieve fault detection and classification rapidly, accurately, and reliably.

Suggested Citation

  • Yijin Li & Jianhua Lin & Geng Niu & Ming Wu & Xuteng Wei, 2021. "A Hilbert–Huang Transform-Based Adaptive Fault Detection and Classification Method for Microgrids," Energies, MDPI, vol. 14(16), pages 1-16, August.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:16:p:5040-:d:615879
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    References listed on IDEAS

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    2. Younis M. Nsaif & Molla Shahadat Hossain Lipu & Aini Hussain & Afida Ayob & Yushaizad Yusof & Muhammad Ammirrul A. M. Zainuri, 2022. "A New Voltage Based Fault Detection Technique for Distribution Network Connected to Photovoltaic Sources Using Variational Mode Decomposition Integrated Ensemble Bagged Trees Approach," Energies, MDPI, vol. 15(20), pages 1-20, October.
    3. Behnam Firouzi & Khalid A. Alattas & Mohsen Bakouri & Abdullah K. Alanazi & Ardashir Mohammadzadeh & Saleh Mobayen & Afef Fekih, 2022. "A Type-2 Fuzzy Controller for Floating Tension-Leg Platforms in Wind Turbines," Energies, MDPI, vol. 15(5), pages 1-19, February.
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    5. Huan Zhou & Jianyun Chen & Manyuan Ye & Qincui Fu & Song Li, 2023. "Transient Fault Signal Identification of AT Traction Network Based on Improved HHT and LSTM Neural Network Algorithm," Energies, MDPI, vol. 16(3), pages 1-21, January.

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