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Detection of Current Transformer Saturation Based on Machine Learning

Author

Listed:
  • 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)

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).

Suggested Citation

  • Ismoil Odinaev & Andrey Pazderin & Murodbek Safaraliev & Firuz Kamalov & Mihail Senyuk & Pavel Y. Gubin, 2024. "Detection of Current Transformer Saturation Based on Machine Learning," Mathematics, MDPI, vol. 12(3), pages 1-18, January.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:3:p:389-:d:1326347
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    References listed on IDEAS

    as
    1. Muhammad Ali & Dae-Hee Son & Sang-Hee Kang & Soon-Ryul Nam, 2017. "An Accurate CT Saturation Classification Using a Deep Learning Approach Based on Unsupervised Feature Extraction and Supervised Fine-Tuning Strategy," Energies, MDPI, vol. 10(11), pages 1-24, November.
    2. Sopheap Key & Chang-Sung Ko & Kwang-Jae Song & Soon-Ryul Nam, 2023. "Fast Detection of Current Transformer Saturation Using Stacked Denoising Autoencoders," Energies, MDPI, vol. 16(3), pages 1-16, February.
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