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

Mihail Senyuk, Svetlana Beryozkina, Inga Zicmane (), Murodbek Safaraliev, Viktor Klassen and Firuz Kamalov
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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, 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

Mathematics, 2025, vol. 13, issue 2, 1-26

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.

Keywords: power system; power system faults; bus voltage; fault simulation; fault detection; machine learning; classification; phasor measurement units; digital signal processing; phasor data concentrator; emergency control; short-circuit current; power grid (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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