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

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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Bibliographic Details
Main Authors: Mihail Senyuk, Svetlana Beryozkina, Inga Zicmane, Murodbek Safaraliev, Viktor Klassen, Firuz Kamalov
Format: Article
Language:English
Published: MDPI AG 2025-01-01
Series:Mathematics
Subjects:
power system
power system faults
bus voltage
fault simulation
fault detection
machine learning
Online Access:https://www.mdpi.com/2227-7390/13/2/316
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