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Mathematics
Volume 13
Issue 23
10.3390/math13233756
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Methodology for Small-Signal Stability Emergency Control in Low-Inertia Power Systems Using Phasor Measurements and Machine Learning Algorithms: A Data-Driven Approach

by
Mihail Senyuk
1,
Svetlana Beryozkina
2,
Muhammad Nadeem
2,
Ismoil Odinaev
1,
Inga Zicmane
3,* and
Murodbek Safaraliev
1,4
1
Department of Automated Electrical Systems, Ural Federal University, 620002 Yekaterinburg, Russia
2
College of Engineering and Technology, American University of the Middle East, Egaila 54200, Kuwait
3
Faculty of Electrical and Environmental Engineering, Riga Technical University, 12/1 Azenes Str., 1048 Riga, Latvia
4
Department of Electrical Stations, Tajik Technical University named after academician M.S. Osimi, Dushanbe 734042, Tajikistan
*
Author to whom correspondence should be addressed.
Mathematics 2025, 13(23), 3756; https://doi.org/10.3390/math13233756 (registering DOI)
Submission received: 2 September 2025 / Revised: 14 November 2025 / Accepted: 19 November 2025 / Published: 23 November 2025
(This article belongs to the Special Issue Mathematical and Computational Methods for Electrical Engineering)
Versions Notes

Abstract

In the process of decarbonizing electricity generation, renewable energy sources are actively being integrated into traditional power systems. As a result, the inertia of the energy system is reduced, and the speed of transition processes is accelerated. This can lead to instability under small disturbances. This necessitates changing traditional approaches to implementing algorithms for emergency control automation. The paper proposes a methodology to solve the problem of small-signal stability analysis in low-inertia energy systems. The task of the small-signal stability analysis problem is reduced to multi-class classification problems. The proposed methodology can be divided into two main parts: selecting the most informative input features and classifying control actions. The IEEE24 mathematical model of the power system serves as a data source. Measurements from this model are received via phasor measurement units. Among the feature selection algorithms considered, the Random Forest algorithm proved to be the most effective. In terms of efficiency in solving the control action selection problem, the LightGBM algorithm proved dominant. Its accuracy in noise-free data was 98%. With 20 dB of data noise, the algorithm’s accuracy decreased slightly: 97%. The algorithm’s time delay was only 0.07 ms.
Keywords: power system; small signal stability; emergency control; machine learning; low inertia; renewable energy sources power system; small signal stability; emergency control; machine learning; low inertia; renewable energy sources

Share and Cite

MDPI and ACS Style

Senyuk, M.; Beryozkina, S.; Nadeem, M.; Odinaev, I.; Zicmane, I.; Safaraliev, M. Methodology for Small-Signal Stability Emergency Control in Low-Inertia Power Systems Using Phasor Measurements and Machine Learning Algorithms: A Data-Driven Approach. Mathematics 2025, 13, 3756. https://doi.org/10.3390/math13233756

AMA Style

Senyuk M, Beryozkina S, Nadeem M, Odinaev I, Zicmane I, Safaraliev M. Methodology for Small-Signal Stability Emergency Control in Low-Inertia Power Systems Using Phasor Measurements and Machine Learning Algorithms: A Data-Driven Approach. Mathematics. 2025; 13(23):3756. https://doi.org/10.3390/math13233756

Chicago/Turabian Style

Senyuk, Mihail, Svetlana Beryozkina, Muhammad Nadeem, Ismoil Odinaev, Inga Zicmane, and Murodbek Safaraliev. 2025. "Methodology for Small-Signal Stability Emergency Control in Low-Inertia Power Systems Using Phasor Measurements and Machine Learning Algorithms: A Data-Driven Approach" Mathematics 13, no. 23: 3756. https://doi.org/10.3390/math13233756

APA Style

Senyuk, M., Beryozkina, S., Nadeem, M., Odinaev, I., Zicmane, I., & Safaraliev, M. (2025). Methodology for Small-Signal Stability Emergency Control in Low-Inertia Power Systems Using Phasor Measurements and Machine Learning Algorithms: A Data-Driven Approach. Mathematics, 13(23), 3756. https://doi.org/10.3390/math13233756

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