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A Method of Abnormal States Detection Based on Adaptive Extraction of Transformer Vibro-Acoustic Signals

Author

Listed:
  • Liang Zou

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

  • Yongkang Guo

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

  • Han Liu

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

  • Li Zhang

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

  • Tong Zhao

    (Shandong Provincial Key Lab of UHV Transmission Technology and Equipment, Jinan 250061, China)

Abstract

State monitoring is very important for the safe operation of high-voltage transformers. A non-contact vibro-acoustic detection method based on the Blind Source Separation (BSS) was proposed in this paper to promote the development of transformer on-line monitoring technology. Firstly, the algorithm of Sparse Component Analysis (SCA) was applied for the adaptive extraction of vibro-acoustic signals, which utilizes the sorted local maximum values of the potential function. Then, the operating states of the transformer were detected by analyzing the vibro-acoustic signal eigenvectors. Different conditions including running normally, increasing of transformer vibro-acoustic amplitude and changing of frequency component of transformer vibro-acoustic were simulated. Moreover, experiments were carried out in a 220 kV substation. The research results show that the number of mixed noise sources can be estimated and the transformer vibro-acoustic signal was always ranked first in the separation signals. The source signals were effectively separated from the mixed signals while all of the correlation coefficients are more than 0.98 and the quadratic residuals are less than −32 dB. As for the experiments, the vibro-acoustic signal was separated out successfully from two voice signals and two interference signals. The acoustic signal reflection is considered as the main cause of the signal interference, and the transformer volume source model is considered as the main reason of unstable vibro-acoustic signal amplitude. Finally, the simulated abnormal states of the transformer were well recognized and the state of the tested transformer was judged to be normal.

Suggested Citation

  • Liang Zou & Yongkang Guo & Han Liu & Li Zhang & Tong Zhao, 2017. "A Method of Abnormal States Detection Based on Adaptive Extraction of Transformer Vibro-Acoustic Signals," Energies, MDPI, vol. 10(12), pages 1-18, December.
  • Handle: RePEc:gam:jeners:v:10:y:2017:i:12:p:2076-:d:121982
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    References listed on IDEAS

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    1. Stefan Tenbohlen & Sebastian Coenen & Mohammad Djamali & Andreas Müller & Mohammad Hamed Samimi & Martin Siegel, 2016. "Diagnostic Measurements for Power Transformers," Energies, MDPI, vol. 9(5), pages 1-25, May.
    2. Ehsan Mollasalehi & Qiao Sun & David Wood, 2013. "Contribution of Small Wind Turbine Structural Vibration to Noise Emission," Energies, MDPI, vol. 6(8), pages 1-23, July.
    3. Byung Eun Lee & Jung-Wook Park & Peter A. Crossley & Yong Cheol Kang, 2014. "Induced Voltages Ratio-Based Algorithm for Fault Detection, and Faulted Phase and Winding Identification of a Three-Winding Power Transformer," Energies, MDPI, vol. 7(9), pages 1-19, September.
    4. Radu Godina & Eduardo M. G. Rodrigues & João C. O. Matias & João P. S. Catalão, 2015. "Effect of Loads and Other Key Factors on Oil-Transformer Ageing: Sustainability Benefits and Challenges," Energies, MDPI, vol. 8(10), pages 1-40, October.
    5. Qing Yang & Peiyu Su & Yong Chen, 2017. "Comparison of Impulse Wave and Sweep Frequency Response Analysis Methods for Diagnosis of Transformer Winding Faults," Energies, MDPI, vol. 10(4), pages 1-16, March.
    6. Chen Wang & Jie Wu & Jianzhou Wang & Weigang Zhao, 2016. "Reliability Analysis and Overload Capability Assessment of Oil-Immersed Power Transformers," Energies, MDPI, vol. 9(1), pages 1-19, January.
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    Cited by:

    1. Guo Wang & Yibin Wang & Yongzhi Min & Wu Lei, 2022. "Blind Source Separation of Transformer Acoustic Signal Based on Sparse Component Analysis," Energies, MDPI, vol. 15(16), pages 1-15, August.

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