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A Critical Review of Data-Driven Transient Stability Assessment of Power Systems: Principles, Prospects and Challenges

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  • Shitu Zhang

    (School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China)

  • Zhixun Zhu

    (GHN Energy Jilin Jiangnan Thermal Power Co., Ltd., Jilin 132013, China)

  • Yang Li

    (School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China)

Abstract

Transient stability assessment (TSA) has always been a fundamental means for ensuring the secure and stable operation of power systems. Due to the integration of new elements such as power electronics, electric vehicles and renewable power generations, dynamic characteristics of power systems are becoming more and more complex, which makes TSA an increasingly urgent task. Since traditional time-domain simulations and direct method cannot meet the actual operation requirements of power systems, data-driven TSA has attracted growing attention from both academia and industry. This paper makes a comprehensive review from the following four aspects: feature extraction and selection, model construction, online learning and rule extraction; and then, summarizes the challenges and prospects for future research; finally, draws the conclusions of this review. This review will be beneficial for relevant researchers to better understand the research status, key technologies, and existing challenges in the field.

Suggested Citation

  • Shitu Zhang & Zhixun Zhu & Yang Li, 2021. "A Critical Review of Data-Driven Transient Stability Assessment of Power Systems: Principles, Prospects and Challenges," Energies, MDPI, vol. 14(21), pages 1-13, November.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:21:p:7238-:d:670997
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    References listed on IDEAS

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    Cited by:

    1. Wadim Strielkowski & Andrey Vlasov & Kirill Selivanov & Konstantin Muraviev & Vadim Shakhnov, 2023. "Prospects and Challenges of the Machine Learning and Data-Driven Methods for the Predictive Analysis of Power Systems: A Review," Energies, MDPI, vol. 16(10), pages 1-31, May.
    2. Bojun Kong & Jian Zhu & Shengbo Wang & Xingmin Xu & Xiaokuan Jin & Junjie Yin & Jianhua Wang, 2023. "Comparative Study of the Transmission Capacity of Grid-Forming Converters and Grid-Following Converters," Energies, MDPI, vol. 16(6), pages 1-13, March.
    3. Petar Sarajcev & Antonijo Kunac & Goran Petrovic & Marin Despalatovic, 2022. "Artificial Intelligence Techniques for Power System Transient Stability Assessment," Energies, MDPI, vol. 15(2), pages 1-21, January.

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