Detection of Current Transformer Saturation Based on Machine Learning

Ismoil Odinaev, Andrey Pazderin, Murodbek Safaraliev, Firuz Kamalov (), Mihail Senyuk and Pavel Y. Gubin
Additional contact information
Ismoil Odinaev: Department of Automated Electrical Systems, Ural Federal University, 620002 Yekaterinburg, Russia
Andrey Pazderin: Department of Automated Electrical Systems, Ural Federal University, 620002 Yekaterinburg, Russia
Murodbek Safaraliev: 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
Mihail Senyuk: Department of Automated Electrical Systems, Ural Federal University, 620002 Yekaterinburg, Russia
Pavel Y. Gubin: Department of Automated Electrical Systems, Ural Federal University, 620002 Yekaterinburg, Russia

Mathematics, 2024, vol. 12, issue 3, 1-18

Abstract: One 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).

Keywords: current transformer; saturation detection; protection system; artificial neural networks; support vector machine; decision tree; binary classification tasks (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/12/3/389/pdf (application/pdf)
https://www.mdpi.com/2227-7390/12/3/389/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:12:y:2024:i:3:p:389-:d:1326347

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().