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Intelligent Islanding Detection of Microgrids Using Long Short-Term Memory Networks

Author

Listed:
  • Syed Basit Ali Bukhari

    (Department of Electrical Engineering, The University of Azad Jammu and Kashmir, Muzaffarabad 13100, Pakistan)

  • Khawaja Khalid Mehmood

    (Department of Electrical Engineering, The University of Azad Jammu and Kashmir, Muzaffarabad 13100, Pakistan)

  • Abdul Wadood

    (Department of Electrical Engineering, Kamra Campus, Air University, Kamra 43570, Pakistan)

  • Herie Park

    (Department of Electrical Engineering, Dong-A University, Busan 49315, Korea)

Abstract

This paper presents a new intelligent islanding detection scheme (IIDS) based on empirical wavelet transform (EWT) and long short-term memory (LSTM) network to identify islanding events in microgrids. The concept of EWT is extended to extract features from three-phase signals. First, the three-phase voltage signals sampled at the terminal of targeted distributed energy resource (DER) or point of common coupling (PCC) are decomposed into empirical modes/frequency subbands using EWT. Then, instantaneous amplitudes and instantaneous frequencies of the three-phases at different frequency subbands are combined, and various statistical features are calculated. Finally, the EWT-based features along with the three-phase voltage signals are input to the LSTM network to differentiate between non-islanding and islanding events. To assess the efficacy of the proposed IIDS, extensive simulations are performed on an IEC microgrid and an IEEE 34-node system. The simulation results verify the effectiveness of the proposed IIDS in terms of non-detection zone (NDZ), computational time, detection accuracy, and robustness against noisy measurement. Furthermore, comparisons with existing intelligent methods and different LSTM architectures demonstrate that the proposed IIDS offers higher reliability by significantly reducing the NDZ and stands robust against measurements uncertainty.

Suggested Citation

  • Syed Basit Ali Bukhari & Khawaja Khalid Mehmood & Abdul Wadood & Herie Park, 2021. "Intelligent Islanding Detection of Microgrids Using Long Short-Term Memory Networks," Energies, MDPI, vol. 14(18), pages 1-16, September.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:18:p:5762-:d:634447
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    References listed on IDEAS

    as
    1. Wen Fan & Ning Kang & Robert Hebner & Xianyong Feng, 2020. "Islanding Detection in Rural Distribution Systems," Energies, MDPI, vol. 13(20), pages 1-12, October.
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    Cited by:

    1. Faisal Mumtaz & Kashif Imran & Abdullah Abusorrah & Syed Basit Ali Bukhari, 2023. "An Extensive Overview of Islanding Detection Strategies of Active Distributed Generations in Sustainable Microgrids," Sustainability, MDPI, vol. 15(5), pages 1-19, March.
    2. Syed Basit Ali Bukhari & Abdul Wadood & Tahir Khurshaid & Khawaja Khalid Mehmood & Sang Bong Rhee & Ki-Chai Kim, 2022. "Empirical Wavelet Transform-Based Intelligent Protection Scheme for Microgrids," Energies, MDPI, vol. 15(21), pages 1-17, October.
    3. Sowmya Ramachandradurai & Narayanan Krishnan & Natarajan Prabaharan, 2022. "Unintentional Passive Islanding Detection and Prevention Method with Reduced Non-Detection Zones," Energies, MDPI, vol. 15(9), pages 1-26, April.
    4. Yan Xia & Feihong Yu & Xingzhong Xiong & Qinyuan Huang & Qijun Zhou, 2022. "A Novel Microgrid Islanding Detection Algorithm Based on a Multi-Feature Improved LSTM," Energies, MDPI, vol. 15(8), pages 1-24, April.
    5. Paul Cristian Andrei & Horia Andrei, 2022. "Power Systems’ Connectivity and Resiliency: Modeling, Simulation and Analysis," Energies, MDPI, vol. 15(8), pages 1-3, April.
    6. Yuheng Wang & Kashif Habib & Abdul Wadood & Shahbaz Khan, 2023. "The Hybridization of PSO for the Optimal Coordination of Directional Overcurrent Protection Relays of the IEEE Bus System," Energies, MDPI, vol. 16(9), pages 1-21, April.

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