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Diagnosis of DC Bias in Power Transformers Using Vibration Feature Extraction and a Pattern Recognition Method

Author

Listed:
  • Xiaowen Wu

    (State Grid Hunan Electric Power Corporation Research Institute, Changsha 410007, China)

  • Ling Li

    (School of Electrical Engineering, Wuhan University, Wuhan 430072, China)

  • Nianguang Zhou

    (State Grid Hunan Electric Power Corporation Research Institute, Changsha 410007, China)

  • Ling Lu

    (State Grid Hunan Electric Power Corporation Research Institute, Changsha 410007, China)

  • Sheng Hu

    (State Grid Hunan Electric Power Corporation Research Institute, Changsha 410007, China)

  • Hao Cao

    (State Grid Hunan Electric Power Corporation Research Institute, Changsha 410007, China)

  • Zhiqiang He

    (State Grid Hunan Electric Power Corporation Research Institute, Changsha 410007, China)

Abstract

DC bias is a great threat to the safe operation of power transformers. This paper deals with a new vibration-based technique to diagnose DC bias in power transformers. With this technique, the DC bias status of power transformers can be automatically recognized. The vibration variation process of a 500 kV autotransformer is tested under the influence of DC bias in the monopole trail operation stage of a ±800 kV HVDC transmission system. Comparison of transformer vibration under normal and DC-biased conditions is conducted. Three features are proposed and are validated by sensitivity analysis. The principal component analysis method is employed for feature de-correlation and dimensionality reduction. The least square support vector machine algorithm is used and verified successful in DC bias recognition. A remote on-line monitoring device based on the proposed algorithm is designed and applied in field DC bias diagnosis of power transformers. The suggested diagnostic algorithm and monitoring device could be useful in targeted DC bias control and improving the safe operation level of power transformers.

Suggested Citation

  • Xiaowen Wu & Ling Li & Nianguang Zhou & Ling Lu & Sheng Hu & Hao Cao & Zhiqiang He, 2018. "Diagnosis of DC Bias in Power Transformers Using Vibration Feature Extraction and a Pattern Recognition Method," Energies, MDPI, vol. 11(7), pages 1-20, July.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:7:p:1775-:d:156562
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    References listed on IDEAS

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    1. Lei Peng & Qiang Fu & Yaohong Zhao & Yihua Qian & Tiansheng Chen & Shengping Fan, 2018. "A Non-Destructive Optical Method for the DP Measurement of Paper Insulation Based on the Free Fibers in Transformer Oil," Energies, MDPI, vol. 11(4), pages 1-9, March.
    2. 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.
    3. Qunli Wu & Chenyang Peng, 2016. "A Least Squares Support Vector Machine Optimized by Cloud-Based Evolutionary Algorithm for Wind Power Generation Prediction," Energies, MDPI, vol. 9(8), pages 1-20, July.
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    Cited by:

    1. Jose R. Huerta-Rosales & David Granados-Lieberman & Juan P. Amezquita-Sanchez & Arturo Garcia-Perez & Maximiliano Bueno-Lopez & Martin Valtierra-Rodriguez, 2022. "Contrast Estimation in Vibroacoustic Signals for Diagnosing Early Faults of Short-Circuited Turns in Transformers under Different Load Conditions," Energies, MDPI, vol. 15(22), pages 1-15, November.
    2. Lianguang Liu & Zebang Yu & Zhe Jiang & Jianhong Hao & Wenlin Liu, 2019. "Observation Research on the Effect of UHVDC Grounding Current on Buried Pipelines," Energies, MDPI, vol. 12(7), pages 1-11, April.

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