IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i1p512-d1023032.html
   My bibliography  Save this article

Analysis of Interturn Faults on Transformer Based on Electromagnetic-Mechanical Coupling

Author

Listed:
  • Nan Zhu

    (School of Electrical Engineering and Automation, Tianjin University of Technology, Tianjin 300384, China)

  • Ji Li

    (Tianjin Key Laboratory for Control Theory & Application in Complicated Systems, Tianjin 300384, China)

  • Lei Shao

    (Tianjin Key Laboratory for Control Theory & Application in Complicated Systems, Tianjin 300384, China)

  • Hongli Liu

    (Tianjin Key Laboratory for Control Theory & Application in Complicated Systems, Tianjin 300384, China)

  • Lei Ren

    (Tianjin Key Laboratory for Control Theory & Application in Complicated Systems, Tianjin 300384, China)

  • Lihua Zhu

    (Tianjin Key Laboratory for Control Theory & Application in Complicated Systems, Tianjin 300384, China)

Abstract

A running transformer frequently experiences interturn faults; they are typically difficult to detect in their early stages but eventually progress to interturn short circuits, which cause damage to the transformer. Therefore, finding out the fault mechanism of the full interturn fault process can provide a theoretical basis for transformer fault detection. In this paper, an electromagnetic-solid mechanics coupled finite element model consistent with an actual oil-immersed three-phase transformer is established. The transient process of winding from interturn discharge to interturn short circuit is simulated to study the electromagnetic characteristics as well as the mechanical characteristics during transformer failure. The model parameters of the transformer are simulated to obtain the fault current, electromagnetic parameters and other performance parameters to study the characteristics of the magnetic field and coil force when interturn faults occur. Finally, the vibration of the transformer casing is used to detect as well as diagnose the transformer fault situation, providing a theoretical basis for the study of transformer detection and diagnosis capability improvement measures.

Suggested Citation

  • Nan Zhu & Ji Li & Lei Shao & Hongli Liu & Lei Ren & Lihua Zhu, 2023. "Analysis of Interturn Faults on Transformer Based on Electromagnetic-Mechanical Coupling," Energies, MDPI, vol. 16(1), pages 1-13, January.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:1:p:512-:d:1023032
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/1/512/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/1/512/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jia, Xiaoyu & Lin, Mei & Su, Shiwei & Wang, Qiuwang & Yang, Jian, 2022. "Numerical study on temperature rise and mechanical properties of winding in oil-immersed transformer," Energy, Elsevier, vol. 239(PA).
    2. Xiaomu Duan & Tong Zhao & Jinxin Liu & Li Zhang & Liang Zou, 2018. "Analysis of Winding Vibration Characteristics of Power Transformers Based on the Finite-Element Method," Energies, MDPI, vol. 11(9), pages 1-19, September.
    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. Wenqi Ge & Chenchen Zhang & Yi Xie & Ming Yu & Youhua Wang, 2021. "Analysis of the Electromechanical Characteristics of Power Transformer under Different Residual Fluxes," Energies, MDPI, vol. 14(24), pages 1-22, December.
    2. Jannis N. Kahlen & Michael Andres & Albert Moser, 2021. "Improving Machine-Learning Diagnostics with Model-Based Data Augmentation Showcased for a Transformer Fault," Energies, MDPI, vol. 14(20), pages 1-20, October.
    3. Hongwen Liu & Ke Wang & Qing Yang & Lu Yin & Jisheng Huang, 2019. "On-Line Detection of Voltage Transformer Insulation Defects Using the Low-Frequency Oscillation Amplitude and Duration of a Zero Sequence Voltage," Energies, MDPI, vol. 12(4), pages 1-17, February.
    4. Wang, Bo & Jia, Xiaoyu & Yang, Jian & Wang, Qiuwang, 2022. "Numerical study on temperature rise and structure optimization for a three-phase gas insulated switchgear busbar chamber," Energy, Elsevier, vol. 254(PC).
    5. Anastasios Dounis, 2019. "Special Issue “Intelligent Control in Energy Systems”," Energies, MDPI, vol. 12(15), pages 1-9, August.
    6. Milan Oravec & Pavol Lipovský & Miroslav Šmelko & Pavel Adamčík & Mirosław Witoś & Jerzy Kwaśniewski, 2021. "Low-Frequency Magnetic Fields in Diagnostics of Low-Speed Electrical and Mechanical Systems," Sustainability, MDPI, vol. 13(16), pages 1-23, August.

    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:16:y:2023:i:1:p:512-:d:1023032. 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.