Detection of Current Transformer Saturation Based on Machine Learning

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dc.contributor.authorOdinaev, Ismoil
dc.contributor.authorPazderin, Andrey
dc.contributor.authorSafaraliev, Murodbek
dc.contributor.authorKamalov, Firuz
dc.contributor.authorSenyuk, Mihail
dc.contributor.authorGubin, Pavel Y.
dc.date.accessioned2024-04-17T15:25:33Z
dc.date.available2024-04-17T15:25:33Z
dc.date.copyright© 2024
dc.date.issued2024-02
dc.description.abstractOne of the tasks in the operation of electric power systems is the correct functioning of the protection system and emergency automation algorithms. Instrument voltage and current transformers, operating in accordance with the laws of electromagnetism, are most often used for information support of the protection system and emergency automation algorithms. Magnetic core saturation of the specified current transformers can occur during faults. As a result, the correct functioning of the protection system and emergency automation algorithms is compromised. The consequences of current transformers saturation are mostly reflected in the main protections of network elements operating on a differential principle. This work aims to consider the analysis of current transformer saturation detection methods. The problem of identifying current transformer saturation is reduced to binary classification, and methods for solving the problem based on artificial neural networks, support vector machine, and decision tree algorithms are proposed. Computational experiments were performed, and their results were analyzed with imbalanced (dominance of the number of current transformer saturation modes over the number of modes with its normal operation) and balanced classes 0 (no current transformer saturation) and 1 (current transformer saturation). © 2024 by the authors.
dc.identifier.citationOdinaev, I., Pazderin, A., Safaraliev, M., Kamalov, F., Senyuk, M., & Gubin, P. Y. (2024). Detection of Current Transformer Saturation Based on Machine Learning. Mathematics, 12(3), 389. https://doi.org/10.3390/math12030389
dc.identifier.issn22277390
dc.identifier.urihttps://doi.org/10.3390/math12030389
dc.identifier.urihttps://hdl.handle.net/20.500.12519/1027
dc.language.isoen_US
dc.publisherMultidisciplinary Digital Publishing Institute (MDPI)
dc.relationAuthors Affiliations : Odinaev, I., Department of Automated Electrical Systems, Ural Federal University, Yekaterinburg, 620002, Russian Federation; Pazderin, A., Department of Automated Electrical Systems, Ural Federal University, Yekaterinburg, 620002, Russian Federation; Safaraliev, M., Department of Automated Electrical Systems, Ural Federal University, Yekaterinburg, 620002, Russian Federation; Kamalov, F., Department of Electrical Engineering, Canadian University Dubai, Dubai, 117781, United Arab Emirates; Senyuk, M., Department of Automated Electrical Systems, Ural Federal University, Yekaterinburg, 620002, Russian Federation; Gubin, P.Y., Department of Automated Electrical Systems, Ural Federal University, Yekaterinburg, 620002, Russian Federation
dc.relation.ispartofseriesMathematics; Volume 12, Issue 3
dc.rightsCreative Commons Attribution (CC BY) license
dc.rights.holderCopyright : © 2024 by the authors.
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectartificial neural networks
dc.subjectbinary classification tasks
dc.subjectcurrent transformer
dc.subjectdecision tree
dc.subjectprotection system
dc.subjectsaturation detection
dc.subjectsupport vector machine
dc.titleDetection of Current Transformer Saturation Based on Machine Learning
dc.typeArticle

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