Bulk Low-Inertia Power Systems Adaptive Fault Type Classification Method Based on Machine Learning and Phasor Measurement Units Data
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,...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/13/2/316 |
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| author | Mihail Senyuk Svetlana Beryozkina Inga Zicmane Murodbek Safaraliev Viktor Klassen Firuz Kamalov |
| author_facet | Mihail Senyuk Svetlana Beryozkina Inga Zicmane Murodbek Safaraliev Viktor Klassen Firuz Kamalov |
| author_sort | Mihail Senyuk |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-21b913251dc84f9da2cc8c99abba9ec4 |
| institution | Kabale University |
| issn | 2227-7390 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-21b913251dc84f9da2cc8c99abba9ec42025-01-24T13:40:09ZengMDPI AGMathematics2227-73902025-01-0113231610.3390/math13020316Bulk Low-Inertia Power Systems Adaptive Fault Type Classification Method Based on Machine Learning and Phasor Measurement Units DataMihail Senyuk0Svetlana Beryozkina1Inga Zicmane2Murodbek Safaraliev3Viktor Klassen4Firuz Kamalov5Department of Automated Electrical Systems, Ural Federal University, 620002 Yekaterinburg, RussiaCollege of Engineering and Technology, American University of the Middle East, KuwaitFaculty of Electrical and Environmental Engineering, Institute of Industrial Electronics, Electrical Engineering and Energy, Riga Technical University, Azenes Street 12/1, LV-1048 Riga, LatviaDepartment of Automated Electrical Systems, Ural Federal University, 620002 Yekaterinburg, RussiaDepartment of Automated Electrical Systems, Ural Federal University, 620002 Yekaterinburg, RussiaDepartment of Electrical Engineering, Canadian University Dubai, Dubai 117781, United Arab EmiratesThis 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.https://www.mdpi.com/2227-7390/13/2/316power systempower system faultsbus voltagefault simulationfault detectionmachine learning |
| spellingShingle | Mihail Senyuk Svetlana Beryozkina Inga Zicmane Murodbek Safaraliev Viktor Klassen Firuz Kamalov Bulk Low-Inertia Power Systems Adaptive Fault Type Classification Method Based on Machine Learning and Phasor Measurement Units Data Mathematics power system power system faults bus voltage fault simulation fault detection machine learning |
| title | Bulk Low-Inertia Power Systems Adaptive Fault Type Classification Method Based on Machine Learning and Phasor Measurement Units Data |
| title_full | Bulk Low-Inertia Power Systems Adaptive Fault Type Classification Method Based on Machine Learning and Phasor Measurement Units Data |
| title_fullStr | Bulk Low-Inertia Power Systems Adaptive Fault Type Classification Method Based on Machine Learning and Phasor Measurement Units Data |
| title_full_unstemmed | Bulk Low-Inertia Power Systems Adaptive Fault Type Classification Method Based on Machine Learning and Phasor Measurement Units Data |
| title_short | Bulk Low-Inertia Power Systems Adaptive Fault Type Classification Method Based on Machine Learning and Phasor Measurement Units Data |
| title_sort | bulk low inertia power systems adaptive fault type classification method based on machine learning and phasor measurement units data |
| topic | power system power system faults bus voltage fault simulation fault detection machine learning |
| url | https://www.mdpi.com/2227-7390/13/2/316 |
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