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Power System Transient Stability Assessment Based on Machine Learning Algorithms and Grid Topology

Author

Listed:
  • Mihail Senyuk

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

  • Murodbek Safaraliev

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

  • Firuz Kamalov

    (Department of Electrical Engineering, Canadian University Dubai, Dubai P.O. Box 415053, United Arab Emirates)

  • Hana Sulieman

    (Department of Mathematics and Statistics, American University of Sharjah, Sharjah P.O. Box 26666, United Arab Emirates)

Abstract

This work employs machine learning methods to develop and test a technique for dynamic stability analysis of the mathematical model of a power system. A distinctive feature of the proposed method is the absence of a priori parameters of the power system model. Thus, the adaptability of the dynamic stability assessment is achieved. The selected research topic relates to the issue of changing the structure and parameters of modern power systems. The key features of modern power systems include the following: decreased total inertia caused by integration of renewable sources energy, stricter requirements for emergency control accuracy, highly digitized operation and control of power systems, and high volumes of data that describe power system operation. Arranging emergency control in these new conditions is one of the prominent problems in modern power systems. In this study, the emergency control algorithms based on ensemble machine learning algorithms (XGBoost and Random Forest) were developed for a low-inertia power system. Transient stability of a power system was analyzed as the base function. Features of transmission line maintenance were used to increase accuracy of estimation. Algorithms were tested using the test power system IEEE39. In the case of the test sample, accuracy of instability classification for XGBoost was 91.5%, while that for Random Forest was 81.6%. The accuracy of algorithms increased by 10.9% and 1.5%, respectively, when the topology of the power system was taken into account.

Suggested Citation

  • Mihail Senyuk & Murodbek Safaraliev & Firuz Kamalov & Hana Sulieman, 2023. "Power System Transient Stability Assessment Based on Machine Learning Algorithms and Grid Topology," Mathematics, MDPI, vol. 11(3), pages 1-15, January.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:3:p:525-:d:1040336
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    References listed on IDEAS

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    1. Petar Sarajcev & Antonijo Kunac & Goran Petrovic & Marin Despalatovic, 2021. "Power System Transient Stability Assessment Using Stacked Autoencoder and Voting Ensemble," Energies, MDPI, vol. 14(11), pages 1-26, May.
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    Cited by:

    1. Andrey Pazderin & Inga Zicmane & Mihail Senyuk & Pavel Gubin & Ilya Polyakov & Nikita Mukhlynin & Murodbek Safaraliev & Firuz Kamalov, 2023. "Directions of Application of Phasor Measurement Units for Control and Monitoring of Modern Power Systems: A State-of-the-Art Review," Energies, MDPI, vol. 16(17), pages 1-43, August.

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