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Fast Detection of Current Transformer Saturation Using Stacked Denoising Autoencoders

Author

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  • Sopheap Key

    (Department of Electrical Engineering, Myongji University, Yongin 17058, Republic of Korea)

  • Chang-Sung Ko

    (Department of Electrical Engineering, Myongji University, Yongin 17058, Republic of Korea)

  • Kwang-Jae Song

    (Department of Electrical Engineering, Myongji University, Yongin 17058, Republic of Korea)

  • Soon-Ryul Nam

    (Department of Electrical Engineering, Myongji University, Yongin 17058, Republic of Korea)

Abstract

Malfunctions in relay protection devices are predominantly caused by current transformer (CT) saturation which produces distortion in current measurements and disturbances in power system protection. The development of deep learning in power system protection is on the rise recently because of its robustness. This study presents a CT saturation detection where the secondary current becomes distorted. The proposed scheme offers a wide range of saturation detection and consists of a moving-window technique and stacked denoising autoencoders. Moreover, Bayesian optimization was used to minimize the difficulty of determining neural network structure for the proposed approach. The performance of the algorithm was evaluated for a-g faults on 154 kV and 345 kV overhead transmission line in South Korea. The waveform variation has been generated by PSCAD for different scenarios that heavily influence CT saturation. Moreover, a comparative analysis with other methods demonstrated the superiority of the proposed DNN method. With the proposed algorithm to detect CT saturation, it significantly yielded high accuracy and precision for CT saturation detection which were approximately 99.71% and 99.32%, respectively.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:3:p:1528-:d:1057100
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    References listed on IDEAS

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    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. Sun-Bin Kim & Vattanak Sok & Sang-Hee Kang & Nam-Ho Lee & Soon-Ryul Nam, 2019. "A Study on Deep Neural Network-Based DC Offset Removal for Phase Estimation in Power Systems," Energies, MDPI, vol. 12(9), pages 1-19, April.
    3. Vattanak Sok & Sun-Woo Lee & Sang-Hee Kang & Soon-Ryul Nam, 2022. "Deep Neural Network-Based Removal of a Decaying DC Offset in Less Than One Cycle for Digital Relaying," Energies, MDPI, vol. 15(7), pages 1-14, April.
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    Cited by:

    1. 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.
    2. Sanlei Dang & Yong Xiao & Baoshuai Wang & Dingqu Zhang & Bo Zhang & Shanshan Hu & Hongtian Song & Chi Xu & Yiqin Cai, 2023. "A High-Precision Error Calibration Technique for Current Transformers under the Influence of DC Bias," Energies, MDPI, vol. 16(24), pages 1-19, December.
    3. Sopheap Key & Gyu-Won Son & Soon-Ryul Nam, 2024. "Deep Learning-Based Algorithm for Internal Fault Detection of Power Transformers during Inrush Current at Distribution Substations," Energies, MDPI, vol. 17(4), pages 1-18, February.

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