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XGBoost delivers the best results: 96.8% accuracy on synthetic data and 85.2% on physical data. Sampling frequency and calculation-window length are also analyzed, showing limited accuracy gains beyond 5 kHz and 5 ms.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & 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Adaptive Fault Type Classification Method Based on Machine Learning and Phasor Measurement Units Data  \nMihail Senyuk 1, Svetlana Beryozkina 2, Inga Zicmane 3, *, Murodbek Safaraliev 1, Viktor Klassen 1 and Firuz Kamalov 4  \nAcademic Editor: Carlos LlopisAlbert  \nReceived: 12 November 2024  \nRevised: 17 December 2024  \nAccepted: 17 January 2025  \nPublished: 19 January 2025  \nCitation: Senyuk, M.; Beryozkina, S.; Zicmane, I.; Safaraliev, M.; Klassen, V.; Kamalov, F. Bulk Low-Inertia Power Systems Adaptive Fault Type Classification Method Based on Machine Learning and Phasor Measurement Units Data. Mathematics 2025, 13, 316. [https://doi.org/](https://doi.org/)[ ](https://doi.org/)[10.3390/math13020316](10.3390/math13020316)  \nCopyright: © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://creativecommons.org/](https://creativecommons.org/)[ ](https://creativecommons.org/)[licenses/by/4.0/](licenses/by/4.0/)) .  \n1 Department of Automated Electrical Systems, Ural Federal University, 620002 Yekaterinburg, Russia; [mdseniuk@urfu.ru](mdseniuk@urfu.ru) (M.S.); [murodbek_03@mail.ru](murodbek_03@mail.ru) (M.S.); [viktor.klassen@urfu.me](viktor.klassen@urfu.me) (V.K.)  \n2 College of Engineering and Technology, American University of the Middle East, Kuwait; [svetlana.berjozkina@aum.edu.kw](svetlana.berjozkina@aum.edu.kw)  \n3 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  \n4 Department of Electrical Engineering, Canadian University Dubai, Dubai 117781, United Arab Emirates; [firuz@cud.ac.ae](firuz@cud.ac.ae)  \n* Correspondence: [inga.zicmane@rtu.lv](inga.zicmane@rtu.lv)  \nAbstract: 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.  \nKeywords: 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  \nMSC: 68T01  \n1. Introduction  \nThe key trends in the development of modern electric power systems (EPS) include the widespread adoption of digital devices for recording, collecting, and analyzing electrical parameters [1,2], the accelerated integration of renewable energy sources (RES) [3–7], and the expansion of permissible operating conditions enabled by digital monitoring systems and adaptive control [8–11] . A notable characteristic of modern EPS is the reduction in system inertia, resulting from the growing share of RES alongside the retirement of fossil fuel power plants. This reduction in inertia leads to faster transient processes and diminishes the effectiveness of emergency control systems.  \nThe greatest impact of change","cbCaiqX5WsMHBOC4","https://ap.wps.com/l/cbCaiqX5WsMHBOC4","pdf",3356932,26,"English","# Abstract\n# Keywords\n# Introduction","[{\"question\":\"What is the main goal of the proposed method?\",\"answer\":\"To classify disturbances in electric power systems using machine learning based on PMU data, improving both speed and classification accuracy.\"},{\"question\":\"Which machine learning models are evaluated in the study?\",\"answer\":\"Random Forest, AdaBoost, XGBoost, and LightGBM are tested using synthetic and real-world disturbance datasets.\"},{\"question\":\"How does sampling frequency and calculation window size affect classification accuracy?\",\"answer\":\"The experiments indicate that increasing sampling frequency beyond 5 kHz and extending the calculation window beyond 5 ms does not significantly improve classification accuracy.\"}]","Bulk Low-Inertia Power Systems Adaptive Fault Type Classification Method Based on Machine Learning and Phasor Measurement Units Data | PDF",66]