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Analysis of Winding Vibration Characteristics of Power Transformers Based on the Finite-Element Method

Author

Listed:
  • Xiaomu Duan

    (School of Electrical Engineering, Shandong University, Jinan 250061, China)

  • Tong Zhao

    (School of Electrical Engineering, Shandong University, Jinan 250061, China)

  • Jinxin Liu

    (State Grid, Jining Power Supply Company, Jining 272000, China)

  • Li Zhang

    (School of Electrical Engineering, Shandong University, Jinan 250061, China)

  • Liang Zou

    (School of Electrical Engineering, Shandong University, Jinan 250061, China)

Abstract

The winding is the core component of a transformer, and the technology used to diagnose its current state directly affects the operation and maintenance of the transformer. The mechanical vibration characteristics of a dry-type transformer winding are studied in this paper. A short-circuit test was performed on an SCB10-1000/10 dry-type transformer, and the vibration signal at the surface was measured. Based on actual experimental conditions, a vibration-simulation model of the transformer was established using COMSOL Multiphysics software. A multiphysics coupling simulation of the circuit, magnetic field, and solid mechanics of the transformer was performed on this model. The simulation results were compared with measured data to verify the validity of the simulation model. The simulation model for a transformer operating under normal conditions was then used to develop simulation models of transformer-winding looseness, winding deformation, and winding-insulation failure, and the winding fault vibration characteristics were analyzed. The results provide a basis for detecting and analyzing the mechanical state of transformer windings.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:9:p:2404-:d:169193
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    References listed on IDEAS

    as
    1. Jinxin Liu & Guan Wang & Tong Zhao & Li Zhang, 2017. "Fault Diagnosis of On-Load Tap-Changer Based on Variational Mode Decomposition and Relevance Vector Machine," Energies, MDPI, vol. 10(7), pages 1-14, July.
    2. Li Zhang & Wenfang Zhang & Jinxin Liu & Tong Zhao & Liang Zou & Xinghua Wang, 2017. "A New Prediction Model for Transformer Winding Hotspot Temperature Fluctuation Based on Fuzzy Information Granulation and an Optimized Wavelet Neural Network," Energies, MDPI, vol. 10(12), pages 1-13, December.
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    Citations

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    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. Anastasios Dounis, 2019. "Special Issue “Intelligent Control in Energy Systems”," Energies, MDPI, vol. 12(15), pages 1-9, August.
    5. 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.
    6. 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.

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