Study on PD detection for GIS based on autocorrelation coefficient and similar Wavelet soft threshold

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Abstract

To address the issue of white noise at partial discharge (PD) ultra high frequency (UHF) signal in gas insulated substation (GIS), this paper develops an external sensor and proposes new empirical mode decomposition (EMD) denoising method based on autocorrelation coefficient and similar wavelet soft threshold. Four types of typical GIS defects at the PD UHF signal were obtained through experiment. The autocorrelation coefficient of intrinsic mode functions (IMF) components at the PD UHF signal was computed, the cut-off point between the noise signal dominant mode and the UHF signal dominant mode was found. The similar wavelet soft threshold denoising was performed on the signal which is dominated by the noise signal, all the UHF signals were finally reconstructed. The signal-to-noise ratio computed by the proposed denoising method was compared with the one computed by the wavelet denoising method, the results shows that the proposed denoising method in this paper is more effective than the wavelet denoising method.

Keywords

Partial discharge Ultra high frequency External sensor Empirical mode decomposition Autocorrelation coefficient Similar Wavelet soft threshold 

Notes

Acknowledgement

This work was Supported by the National Natural Science Foundation of China (No. 61473049); Hunan province science and technology Project (No. 2015GK3018).

References

  1. 1.
    Zhou, H., Liu, C., Lei, G., et al.: The partial discharge case analysis of GIS basin insulator. Insul. Surge Arresters 2, 186–190 (2017)Google Scholar
  2. 2.
    Sun, S., Lu, J., Yu, H., et al.: Detection of typical GIS partial discharge based on UHF method. High Volt. Eng. 48(4), 7–12 (2012)Google Scholar
  3. 3.
    Okabe, S., Kaneko, S., Yoshimura, M., et al.: Propagation characteristics of electromagnetic waves in three-phase-type tank from viewpoint of partial discharge diagnosis on gas insulated switchgear. IEEE Trans. Dielectr. Electr. Insul. 16(1), 199–205 (2009)CrossRefGoogle Scholar
  4. 4.
    Okabe, S., Kaneko, S., Yoshimura, M., et al.: Partial discharge diagnosis method using electromagnetic wave mode transformation in gas insulated switchgear. IEEE Trans. Dielectr. Electr. Insul. 14(3), 702–709 (2007)CrossRefGoogle Scholar
  5. 5.
    Habib, S., Haque, A.: Impulsive noise mitigation in wireless communication systems using EMD technique. In: 2012 7th International Conference on Electrical and Computer Engineering, ICECE, pp. 291–294 (2012)Google Scholar
  6. 6.
    Gao, W., Zhao, D., Ding, D., et al.: Investigation of frequency characteristics of typical PD and the propagation properties in GIS. IEEE Trans. Dielectr. Electr. Insul. 22(3), 1654–1662 (2015)CrossRefGoogle Scholar
  7. 7.
    J, H., Kordi, Behzad, Jozani, M.J.: Classification of simultaneous multiple partial discharge sources based on probabilistic interpretation using a two-step logistic regression algorithm. IEEE Trans. Dielectr. Electr. Insul. 24(1), 54–65 (2017)CrossRefGoogle Scholar
  8. 8.
    Healy, C.T., de Lamare, R.C.: Design of LDPC codes based on multipath EMD strategies for progressive edge growth. IEEE Trans. Commun. 64(8), 3208–3219 (2016)CrossRefGoogle Scholar
  9. 9.
    Li, H., Cheng, C., Chen, J., et al.: Fractal dimension research of the partial discharge UHF signal in GIS based on EMD. High Volt. Eng. 50(6), 104–110 (2014)Google Scholar
  10. 10.
    Dapeng, D., Qin, W., Liu, H., Wang, C., Tang, H.: Implementation of GIS condition monitoring experiment system and study of condition monitoring. High Volt. Appar. 12(50), 100–104 (2014)Google Scholar
  11. 11.
    Li, J., Li, M., Jin, Z.: Some method for diagnosis of GIS partial discharge defects. High Volt. Appar. 50(10), 85–90 (2014)Google Scholar
  12. 12.
    Ye, J., Ding, Y.: Controllable keyword search scheme supporting multiple users. Fut. Gener. Comput. Syst. 81, 433–442 (2018)CrossRefGoogle Scholar
  13. 13.
    Xu, Z., Luo, X., Wang, L.: Incremental building association link network. Comput. Syst. Sci. Eng. 26(3), 153–162 (2011)Google Scholar
  14. 14.
    Donoho, D.: De-noising by soft-thresholding. IEEE Trans. Inf. Theory 41(3), 613–627 (2009)MathSciNetCrossRefMATHGoogle Scholar
  15. 15.
    Sun, B., Zhang, J., Pan, L.: Study on de-noising method of partial discharge based EMD. Insul. Mater. 47(3), 89–93 (2014)Google Scholar
  16. 16.
    Qureshi, A., Brandt-Pearce, M.: On modified EMD: selective extrema analysis. In: 2014 IEEE Workshop on Signal Processing Systems, SiPS, pp. 1–6Google Scholar
  17. 17.
    Tenbohlen, S., Denissov, D., Hoek, S., et al.: Partial discharge measurement in the ultra high frequency (UHF) range. IEEE Trans. Dielectr. Electr. Insul. 15(6), 1544–1552 (2008)CrossRefGoogle Scholar
  18. 18.
    Shen, C., Wang, F., He, R.: Design of active outer sensor for GIS partial discharge detection. Transducer Microsyst. Technol. 34(3):113–115,119 (2015)Google Scholar
  19. 19.
    He, Y., He, Z., Hou, Z., et al.: GIS partial discharge pattern recognition research based on class kernel mean principal component analysis. Electr. Meas. Instrum. 53(2), 84–89 (2016)Google Scholar
  20. 20.
    Vokelj, M., Zupan, S., Prebil, I.: Non-linear multivariate and multiscale monitoring and signal denoising strategy using kernel principal component analysis combined with ensemble empirical mode decomposition method. Mech. Syst. Signal Process. 25(7), 2631–2653 (2011)CrossRefGoogle Scholar
  21. 21.
    Shao, X., He, W., Liu, S., et al.: Numerical research on electromagnetic wave in ultra-high frequency band excited by partial discharge in electrical equipment. J. Xi’an Jiaotong Univ. 50(12), 24–31 (2016)Google Scholar
  22. 22.
    Okabe, S., Yamagiwa, T., Okubo, H.: Detection of harmful metallic particles inside gas insulated switchgear using UHF sensor. IEEE Trans. Dielectr. Electr. Insul. 15(3), 701–709 (2008)CrossRefGoogle Scholar
  23. 23.
    Zhang, X., Xiao, S., Shu, N., et al.: GIS partial discharge pattern recognition based on the chaos theory. IEEE Trans. Dielectr. Electr. Insul. 21(2), 783–790 (2014)CrossRefGoogle Scholar
  24. 24.
    Ye, H., Chen, X., Zhou, T., et al.: Application of white-noise suppression in gas insulated switchgear partial discharge monitoring based on lifting dual-tree complex Wavelet transform. High Volt. Eng. 43(3), 851–858 (2017)Google Scholar
  25. 25.
    Talbi, M., Aouinet, A., Baazaoui, R., et al.: ECG analysis based on Wavelet transform and modulus maxima. Int. J. Comput. Sci. 9(1), 427 (2012)Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.College of Electrical and Information EngineeringChangsha University of Science and TechnologyChangshaChina
  2. 2.State Grid Zhuzhou Power Supply CompanyZhuzhouChina

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