IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v18y2025i9p2238-d1644458.html
   My bibliography  Save this article

Simulation Analysis and Experiment Research of Transformer Vibration Based on Electric–Magnetic–Mechanic Coupling

Author

Listed:
  • Long He

    (State Grid Xinjiang Electric Power Co., Ltd., Changji Power Supply Company, Changji 831100, China)

  • Yongming Zhu

    (State Grid Xinjiang Electric Power Co., Ltd., Changji Power Supply Company, Changji 831100, China)

  • Gang Liu

    (State Grid Xinjiang Electric Power Co., Ltd., Changji Power Supply Company, Changji 831100, China)

  • Chen Cao

    (School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, China)

Abstract

To research a transformer’s vibration characteristics, a simulation and an experiment are conducted on a 10 kV transformer. The theoretical model for core and winding vibration is established. The electric–magnetic–mechanic multi-physical field coupling model for the transformer core and winding is constructed, yielding voltage and current waveform and magnetic field distributions. The simulation results show that the amplitude of the main flux for core is 1.79 T, the amplitude of vibration acceleration for core is 0.005 m/s 2 , the magnetic flux leakage is 0.31 T, the amplitude of the vibration acceleration on the side of the winding is 0.0795 m/s 2 , and the amplitude of vibration acceleration on the front midpoint of winding is 0.0387 m/s 2 . The transformer vibration experimental platform is constructed, and no-load and load tests are conducted. Empirical findings demonstrate that the acceleration of core vibration is 0.0047 m/s 2 , and the simulation deviation is 6.38%. The maximum winding vibration acceleration at the side midpoint of phase A is 0.0714 m/s 2 , and at the front midpoint of Phase B is 0.0416 m/s 2 . Compared with experiment results, the simulation deviations are 2.1% and 3.3%, respectively. These conclusions indicate an alignment between the experiment and simulation results, thereby confirming reliability of the methodology.

Suggested Citation

  • Long He & Yongming Zhu & Gang Liu & Chen Cao, 2025. "Simulation Analysis and Experiment Research of Transformer Vibration Based on Electric–Magnetic–Mechanic Coupling," Energies, MDPI, vol. 18(9), pages 1-19, April.
  • Handle: RePEc:gam:jeners:v:18:y:2025:i:9:p:2238-:d:1644458
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/18/9/2238/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/18/9/2238/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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. 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.
    4. 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.
    5. Zhengqin Zhou & Chuanxian Luo & Fengda Zhang & Jing Zhang & Xu Yang & Peng Yu & Minfu Liao, 2025. "Thermal Management in 500 kV Oil-Immersed Converter Transformers: Synergistic Investigation of Critical Parameters Through Simulation and Experiment," Energies, MDPI, vol. 18(9), pages 1-17, April.
    6. Anastasios Dounis, 2019. "Special Issue “Intelligent Control in Energy Systems”," Energies, MDPI, vol. 12(15), pages 1-9, August.
    7. 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:18:y:2025:i:9:p:2238-:d:1644458. 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.