Результаты исследований: Вклад в журнал › Статья › Рецензирование
Результаты исследований: Вклад в журнал › Статья › Рецензирование
}
TY - JOUR
T1 - Detection of Current Transformer Saturation Based on Machine Learning
AU - Odinaev, Ismoil
AU - Pazderin, Andrey
AU - Safaraliev, Murodbek
AU - Kamalov, Firuz
AU - Senyuk, Mihail
AU - Gubin, Pavel
N1 - The reported study was supported by Russian Science Foundation, research project № 23-79-01024.
PY - 2024
Y1 - 2024
N2 - 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). © 2024 by the authors.
AB - 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). © 2024 by the authors.
UR - http://www.scopus.com/inward/record.url?partnerID=8YFLogxK&scp=85184489581
UR - https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=tsmetrics&SrcApp=tsm_test&DestApp=WOS_CPL&DestLinkType=FullRecord&KeyUT=001159915400001
U2 - 10.3390/math12030389
DO - 10.3390/math12030389
M3 - Article
VL - 12
JO - Mathematics
JF - Mathematics
SN - 2227-7390
IS - 3
M1 - 389
ER -
ID: 52642786