Methodology for Transient Stability Assessment and Enhancement in Low-Inertia Power Systems Using Phasor Measurements: A Data-Driven Approach

Mihail Senyuk, Svetlana Beryozkina, Ismoil Odinaev, Inga Zicmane () and Murodbek Safaraliev
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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, Egaila 15453, Kuwait
Ismoil Odinaev: Department of Automated Electrical Systems, Ural Federal University, 620002 Yekaterinburg, Russia
Inga Zicmane: Faculty of Electrical and Environmental Engineering, Riga Technical University, 12/1 Azenes Str., 1048 Riga, Latvia
Murodbek Safaraliev: Department of Automated Electrical Systems, Ural Federal University, 620002 Yekaterinburg, Russia

Mathematics, 2025, vol. 13, issue 19, 1-28

Abstract: Modern energy systems are undergoing a profound transformation characterized by the active replacement of conventional fossil-fuel-based power plants with renewable energy sources. This transition aims to reduce the carbon emissions associated with electricity generation while enhancing the economic performance of electric power market players. However, alongside these benefits come several challenges, including reduced overall inertia within energy systems, heightened stochastic variability in grid operation regimes, and stricter demands on the rapid response capabilities and adaptability of emergency controls. This paper presents a novel methodology for selecting effective control laws for low-inertia energy systems, ensuring their dynamic stability during post-emergency operational conditions. The proposed approach integrates advanced techniques, including feature selection via decision tree algorithms, classification using Random Forest models, and result visualization through the Mean Shift clustering method applied to a two-dimensional representation derived from the t-distributed Stochastic Neighbor Embedding technique. A modified version of the IEEE39 benchmark model served as the testbed for numerical experiments, achieving a classification accuracy of 98.3%, accompanied by a control law synthesis delay of just 0.047 milliseconds. In conclusion, this work summarizes the key findings and outlines potential enhancements to refine the presented methodology further.

Keywords: power system; transient stability; emergency control; machine learning; clustering algorithm; low inertia; renewable energy sources (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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