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access icon openaccess Research on mechanical fault diagnosis method of power transformer winding

This is an open access article published by the IET under the Creative Commons Attribution-NoDerivs License (http://creativecommons.org/licenses/by-nd/3.0/)
Received 28/08/2018, Accepted 19/09/2018, Published 24/10/2018

Today, the accuracy of the fault mechanical diagnosis of transformer winding is low and the fault types cannot be judged, this study proposes a machine condition diagnosis method of transformer winding based on the combination of short-circuit reactance and mechanical vibration. During the process of diagnosis, first of all, from a power transformer's one and two side current and voltage, it can calculate the internal short-circuit reactance of the winding to judge the winding state. Then, it uses the wavelet transform to analyse the vibration signals of the transformer windings under different conditions and it uses the signal spectrum entropy as the input feature vector. Finally, using multi class support vector machine to train and test the feature vector, it realises the classification diagnosis of transformer winding in different states. By setting the actual different transformer winding faults of type S11-M-500/35, it gathers the corresponding parameter data and it tests the diagnosis method for the fault diagnosis of transformer winding verification. The diagnosis results are consistent with the actual fault, which verifies the validity and accuracy of the proposed method that is applied to the transformer winding fault diagnosis.

Inspec keywords: support vector machines; entropy; wavelet transforms; transformer windings; fault diagnosis; power transformers; power engineering computing; vibrations; condition monitoring

Other keywords: fault types; mechanical fault diagnosis method; transformer winding verification; winding state; signal spectrum entropy; diagnosis results; fault mechanical diagnosis; internal short-circuit reactance; input feature vector; vibration signal; machine condition diagnosis method; mechanical vibration; wavelet transform; classification diagnosis; power transformer winding; multiclass support vector machine

Subjects: Knowledge engineering techniques; Power engineering computing; Integral transforms; Transformers and reactors; Integral transforms

References

    1. 1)
      • 11. Bin, Z.: ‘The study on multi-information diagnosis method of power transformer winding mechanical state’. Shen Yang University of Technology, Shenyang, 2015.
    2. 2)
      • 14. Quan-ming, Z., Hui-jin, L.: ‘Application of LS-SVM in classification of power quality disturbance’, Proc. CSEE, 2008, 28, (1), pp. 106–110.
    3. 3)
      • 13. Qiurong, Y., Xin, L., Jianguo, Y.: ‘Features of vibration signal of power transformer using the wavelet theory’, High Volt. Eng., 2007, 33, (1), pp. 165–168.
    4. 4)
      • 2. Wenlong, J., Jianhua, C., Guangfan, L., et al: ‘Statistics and analysis on power transformer damages caused by short-circuit fault in 110 kV and higher voltage classes’, Power Syst. Technol., 1999, 23, (6), pp. 70–74.
    5. 5)
      • 9. Po'an, X.: ‘Study on application of vibration analysis to the condition monitoring of power transformers windings’. Shanghai Jiao Tong University, Shanghai, 2008.
    6. 6)
      • 5. Shuguo, G., Yang, Z., Zhiyong, C., et al: ‘Analysis of a 220 kV transformer windings breakage fault caused by short-circuit’, High Volt. Appar., 2012, 48, (4), pp. 115–118.
    7. 7)
      • 7. Yong, L., Shengchang, J., Xiaochen, L., et al: ‘Principle and application of sweep frequency impedance method for detecting transformer winding deformation’, Insulating Mater., 2014, 47, (3), pp. 85–88.
    8. 8)
      • 1. Jun, L., Liwen, D., Hong, Z.: ‘Aging and life assess-ment of oil-immersed transformer’, High Volt. Eng., 2007, 33, (3), pp. 186–189.
    9. 9)
      • 12. Jianyuan, X., Bin, Z., Xin, L., et al: ‘Application of energy spectrum entropy vector method and RBF neural networks optimized by the particle swarm in high-voltage circuit breaker mechanical fault diagnosis’, High Volt. Eng., 2012, 38, (6), pp. 1299–1306.
    10. 10)
      • 10. Ryder, S.A.: ‘Diagnosing transformer faults using frequency response analysis’, IEEE Electr. Insul. Mag., 2003, 19, (2), pp. 16–22.
    11. 11)
      • 8. Bin, Z., Jianyuan, X., Jiangbo, C.: ‘The diagnosis method of winding deformation based on power transformer vibration information’, High Volt. Eng., 2015, 41, (7), pp. 23341–23349.
    12. 12)
      • 4. Enke, Z., Xuejin, W., Jianhui, C., et al: ‘Analysis on the excess hydrogen content of insulating oil in 500 kV transformer’, High Volt. Appar., 2011, 47, (5), pp. 83–86.
    13. 13)
      • 3. Xiaomei, Q.: ‘Synthetic diagnosis and experience about winding deformation of transformer’, Power Syst. Technol., 2006, 30, (s1), pp. 220–222.
    14. 14)
      • 6. Xin, L., Jun, X., Jianyuan, X.: ‘Application of three phase algorithm s in parameter identification of transformer winding’, J. Shenyang Univ. of Technol., 2009, 31, (1), pp. 16–21.
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