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Artificial Intelligence Techniques for Power System Transient Stability Assessment

Author

Listed:
  • Petar Sarajcev

    (Department of Power Engineering, University of Split, FESB, HR21000 Split, Croatia)

  • Antonijo Kunac

    (Department of Power Engineering, University of Split, FESB, HR21000 Split, Croatia)

  • Goran Petrovic

    (Department of Power Engineering, University of Split, FESB, HR21000 Split, Croatia)

  • Marin Despalatovic

    (Department of Power Engineering, University of Split, FESB, HR21000 Split, Croatia)

Abstract

The high penetration of renewable energy sources, coupled with decommissioning of conventional power plants, leads to the reduction of power system inertia. This has negative repercussions on the transient stability of power systems. The purpose of this paper is to review the state-of-the-art regarding the application of artificial intelligence to the power system transient stability assessment, with a focus on different machine, deep, and reinforcement learning techniques. The review covers data generation processes (from measurements and simulations), data processing pipelines (features engineering, splitting strategy, dimensionality reduction), model building and training (including ensembles and hyperparameter optimization techniques), deployment, and management (with monitoring for detecting bias and drift). The review focuses, in particular, on different deep learning models that show promising results on standard benchmark test cases. The final aim of the review is to point out the advantages and disadvantages of different approaches, present current challenges with existing models, and offer a view of the possible future research opportunities.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:2:p:507-:d:722488
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    References listed on IDEAS

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    1. Arcadio Perilla & Stelios Papadakis & Jose Luis Rueda Torres & Mart van der Meijden & Peter Palensky & Francisco Gonzalez-Longatt, 2020. "Transient Stability Performance of Power Systems with High Share of Wind Generators Equipped with Power-Angle Modulation Controllers or Fast Local Voltage Controllers," Energies, MDPI, vol. 13(16), pages 1-17, August.
    2. Ifedayo Oladeji & Ramon Zamora & Tek Tjing Lie, 2021. "An Online Security Prediction and Control Framework for Modern Power Grids," Energies, MDPI, vol. 14(20), pages 1-27, October.
    3. 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.
    4. Yuwei Zhang & Wenying Liu & Fangyu Wang & Yaoxiang Zhang & Yalou Li, 2020. "Reactive Power Control Method for Enhancing the Transient Stability Total Transfer Capability of Transmission Lines for a System with Large-Scale Renewable Energy Sources," Energies, MDPI, vol. 13(12), pages 1-14, June.
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    Citations

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    Cited by:

    1. Paweł Pijarski & Piotr Kacejko & Piotr Miller, 2023. "Advanced Optimisation and Forecasting Methods in Power Engineering—Introduction to the Special Issue," Energies, MDPI, vol. 16(6), pages 1-20, March.
    2. Paweł Pijarski & Adrian Belowski, 2024. "Application of Methods Based on Artificial Intelligence and Optimisation in Power Engineering—Introduction to the Special Issue," Energies, MDPI, vol. 17(2), pages 1-42, January.
    3. Jiaojiao Dong & Mirka Mandich & Yinfeng Zhao & Yang Liu & Shutang You & Yilu Liu & Hongming Zhang, 2023. "AI-Based Faster-Than-Real-Time Stability Assessment of Large Power Systems with Applications on WECC System," Energies, MDPI, vol. 16(3), pages 1-12, January.
    4. Mihail Senyuk & Murodbek Safaraliev & Andrey Pazderin & Olga Pichugova & Inga Zicmane & Svetlana Beryozkina, 2023. "Methodology for Power Systems’ Emergency Control Based on Deep Learning and Synchronized Measurements," Mathematics, MDPI, vol. 11(22), pages 1-30, November.
    5. Ali M. Hakami & Kazi N. Hasan & Mohammed Alzubaidi & Manoj Datta, 2022. "A Review of Uncertainty Modelling Techniques for Probabilistic Stability Analysis of Renewable-Rich Power Systems," Energies, MDPI, vol. 16(1), pages 1-26, December.
    6. Zhencheng Fan & Zheng Yan & Shiping Wen, 2023. "Deep Learning and Artificial Intelligence in Sustainability: A Review of SDGs, Renewable Energy, and Environmental Health," Sustainability, MDPI, vol. 15(18), pages 1-20, September.
    7. Dimitris A. Barkas & Stavros D. Kaminaris & Konstantinos K. Kalkanis & George Ch. Ioannidis & Constantinos S. Psomopoulos, 2022. "Condition Assessment of Power Transformers through DGA Measurements Evaluation Using Adaptive Algorithms and Deep Learning," Energies, MDPI, vol. 16(1), pages 1-17, December.

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