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Bulk Low-Inertia Power Systems Adaptive Fault Type Classification Method Based on Machine Learning and Phasor Measurement Units Data

Author

Listed:
  • Mihail Senyuk

    (Department of Automated Electrical Systems, Ural Federal University, 620002 Yekaterinburg, Russia)

  • Svetlana Beryozkina

    (College of Engineering and Technology, American University of the Middle East, Kuwait)

  • Inga Zicmane

    (Faculty of Electrical and Environmental Engineering, Institute of Industrial Electronics, Electrical Engineering and Energy, Riga Technical University, Azenes Street 12/1, LV-1048 Riga, Latvia)

  • Murodbek Safaraliev

    (Department of Automated Electrical Systems, Ural Federal University, 620002 Yekaterinburg, Russia)

  • Viktor Klassen

    (Department of Automated Electrical Systems, Ural Federal University, 620002 Yekaterinburg, Russia)

  • Firuz Kamalov

    (Department of Electrical Engineering, Canadian University Dubai, Dubai 117781, United Arab Emirates)

Abstract

This research focuses on developing and testing a method for classifying disturbances in power systems using machine learning algorithms and phasor measurement unit (PMU) data. To enhance the speed and accuracy of disturbance classification, we employ a range of ensemble machine learning techniques, including Random forest, AdaBoost, Extreme gradient boosting (XGBoost), and LightGBM. The classification method was evaluated using both synthetic data, generated from transient simulations of the IEEE24 test system, and real-world data from actual transient events in power systems. Among the algorithms tested, XGBoost achieved the highest classification accuracy, with 96.8% for synthetic data and 85.2% for physical data. Additionally, this study investigates the impact of data sampling frequency and calculation window size on classification performance. Through numerical experiments, we found that increasing the signal sampling rate beyond 5 kHz and extending the calculation window beyond 5 ms did not significantly improve classification accuracy.

Suggested Citation

  • Mihail Senyuk & Svetlana Beryozkina & Inga Zicmane & Murodbek Safaraliev & Viktor Klassen & Firuz Kamalov, 2025. "Bulk Low-Inertia Power Systems Adaptive Fault Type Classification Method Based on Machine Learning and Phasor Measurement Units Data," Mathematics, MDPI, vol. 13(2), pages 1-26, January.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:2:p:316-:d:1570484
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    References listed on IDEAS

    as
    1. Mihail Senyuk & Svetlana Beryozkina & Alexander Berdin & Alexander Moiseichenkov & Murodbek Safaraliev & Inga Zicmane, 2022. "Testing of an Adaptive Algorithm for Estimating the Parameters of a Synchronous Generator Based on the Approximation of Electrical State Time Series," Mathematics, MDPI, vol. 10(22), pages 1-18, November.
    2. Yayao Zhang & Miao Han & Meng Zhan, 2023. "The Concept and Understanding of Synchronous Stability in Power Electronic-Based Power Systems," Energies, MDPI, vol. 16(6), pages 1-15, March.
    3. 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.
    4. Ali S. Alghamdi, 2022. "A New Self-Adaptive Teaching–Learning-Based Optimization with Different Distributions for Optimal Reactive Power Control in Power Networks," Energies, MDPI, vol. 15(8), pages 1-24, April.
    5. Mihail Senyuk & Svetlana Beryozkina & Murodbek Safaraliev & Andrey Pazderin & Ismoil Odinaev & Viktor Klassen & Alena Savosina & Firuz Kamalov, 2024. "Bulk Power Systems Emergency Control Based on Machine Learning Algorithms and Phasor Measurement Units Data: A State-of-the-Art Review," Energies, MDPI, vol. 17(4), pages 1-33, February.
    Full references (including those not matched with items on IDEAS)

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