IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i4p963-d1341350.html
   My bibliography  Save this article

Deep Learning-Based Algorithm for Internal Fault Detection of Power Transformers during Inrush Current at Distribution Substations

Author

Listed:
  • Sopheap Key

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

  • Gyu-Won Son

    (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

The reliability and stability of differential protection in power transformers could be threatened by several types of inferences, including magnetizing inrush currents, current transformer saturation, and overexcitation from external faults. The robustness of deep learning applications employed for power system protection in recent years has offered solutions to deal with several disturbances. This paper presents a method for detecting internal faults in power transformers occurring simultaneously with inrush currents. It involves utilizing a data window (DW) and stacked denoising autoencoders. Unlike the conventional method, the proposed scheme requires no thresholds to discriminate internal faults and inrush currents. The performance of the algorithm was verified using fault data from a typical Korean 154 kV distribution substation. Inrush current variation and internal faults were simulated and generated in PSCAD/EMTDC, considering various parameters that affect an inrush current. The results indicate that the proposed scheme can detect the appearance of internal faults occurring simultaneously with an inrush current. Moreover, it shows promising results compared to the prevailing methods, ensuring the superiority of the proposed method. From sample N –3, the proposed DNN demonstrates accurate discrimination between internal faults and inrush currents, achieving accuracy, sensitivity, and precision values of 100%.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:4:p:963-:d:1341350
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/4/963/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/17/4/963/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    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. Hongmei Cui & Zhongyang Li & Bingchuan Sun & Teng Fan & Yonghao Li & Lida Luo & Yong Zhang & Jian Wang, 2022. "A New Ice Quality Prediction Method of Wind Turbine Impeller Based on the Deep Neural Network," Energies, MDPI, vol. 15(22), pages 1-18, November.
    3. 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.
    4. 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.
    5. Vattanak Sok & Su-Hwan Kim & Peng Y. Lak & Soon-Ryul Nam, 2024. "A Novel Method for Removal of Dual Decaying DC Offsets to Enhance Discrete Fourier Transform-Based Phasor Estimation," Energies, MDPI, vol. 17(4), pages 1-16, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:17:y:2024:i:4:p:963-:d:1341350. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.