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End-to-end microgrid protection using distributed data-driven methods

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  • Chen, Yue
  • Chakraborty, Soham
  • Zamzam, Ahmed
  • Wang, Jing

Abstract

This paper introduces an end-to-end microgrid protection framework that offers real-time system monitoring, fault-related decision making, and circuit breaker control. This is achieved through the design of distributed data-driven techniques based on the support vector machine method, where each relay is responsible for distributed data collection, fault detection, fault localization, and fault isolation. Local communication is established among neighboring relays, fostering cooperative fault localization and isolation. This decentralized design not only reduces the computational and communication requirements but also enables the adaptability of each relay under varying operational dynamics. The proposed end-to-end protection framework was validated using MATLAB/Simulink simulations on a 100% renewable microgrid, achieving an accuracy of 93.1% with response time of 0.0523 s, in protecting against a range of fault scenarios that are characterized by various types, locations, impedances, load conditions, photovoltaic power levels, and microgrid operating modes.

Suggested Citation

  • Chen, Yue & Chakraborty, Soham & Zamzam, Ahmed & Wang, Jing, 2025. "End-to-end microgrid protection using distributed data-driven methods," Applied Energy, Elsevier, vol. 391(C).
    • Handle: RePEc:eee:appene:v:391:y:2025:i:c:s0306261925005276
    DOI: 10.1016/j.apenergy.2025.125797
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    References listed on IDEAS

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    1. Mohammad Reza Shadi & Hamid Mirshekali & Rahman Dashti & Mohammad-Taghi Ameli & Hamid Reza Shaker, 2021. "A Parameter-Free Approach for Fault Section Detection on Distribution Networks Employing Gated Recurrent Unit," Energies, MDPI, vol. 14(19), pages 1-15, October.
    2. Wang, Ting & Zhang, Chunyan & Hao, Zhiguo & Monti, Antonello & Ponci, Ferdinanda, 2023. "Data-driven fault detection and isolation in DC microgrids without prior fault data: A transfer learning approach," Applied Energy, Elsevier, vol. 336(C).
    3. Uzair, Muhammad & Li, Li & Eskandari, Mohsen & Hossain, Jahangir & Zhu, Jian Guo, 2023. "Challenges, advances and future trends in AC microgrid protection: With a focus on intelligent learning methods," Renewable and Sustainable Energy Reviews, Elsevier, vol. 178(C).
    4. Muhammad Uzair & Mohsen Eskandari & Li Li & Jianguo Zhu, 2022. "Machine Learning Based Protection Scheme for Low Voltage AC Microgrids," Energies, MDPI, vol. 15(24), pages 1-19, December.
    5. Patnaik, Bhaskar & Mishra, Manohar & Bansal, Ramesh C. & Jena, Ranjan Kumar, 2020. "AC microgrid protection – A review: Current and future prospective," Applied Energy, Elsevier, vol. 271(C).
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