Review of UHF-Based Signal Processing Approaches for Partial Discharge Detection

  • Benjamin SchubertEmail author
  • Mauro Palo
  • Thomas Schlechter
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10672)


Partial Discharge (PD) events are due to local defects in dielectrics and can cause damages to the electrical insulation and eventually to the whole power station. This paper reviews approaches describing procedures and numerical techniques for detecting, denoising, clustering, and classifying PDs in the ultra-high frequency range. For each method the mathematical background is recalled and one or few representative examples from selected papers are shortly described.


Partial Discharge (PD) Ultra-high frequency Signal processing Data analysis 



This work was funded by the State Grid Corporation of China (SGCC) through the R&D project “Research of Key Technology of UHF Wireless Sensing based Substation Partial Discharge Monitoring and Location”.


  1. 1.
    Babnik, T., Aggarwal, R.K., Moore, P.J.: Principal component and hierarchical cluster analyses as applied to transformer partial discharge data with particular reference to transformer condition monitoring. IEEE Trans. Power Deliv. 23(4), 2008–2016 (2008)CrossRefGoogle Scholar
  2. 2.
    Bartnikas, R.: Partial discharges. Their mechanism, detection and measurement. IEEE Trans. Dielectr. Electr. Insul. 9(5), 763–808 (2002)CrossRefGoogle Scholar
  3. 3.
    Chan, J., Ma, H., Saha, T., Ekanayake, C.: Self-adaptive partial discharge signal de-noising based on ensemble empirical mode decomposition and automatic morphological thresholding. IEEE Trans. Dielectr. Electr. Insul. 21(1), 294–303 (2014)CrossRefGoogle Scholar
  4. 4.
    Chang, C., Jin, J., Chang, C., Hoshino, T., Hanai, M., Kobayashi, N.: Separation of corona using wavelet packet transform and neural network for detection of partial discharge in gas-insulated substations. IEEE Trans. Power Deliv. 20(2), 1363–1369 (2005)CrossRefGoogle Scholar
  5. 5.
    Hao, L., Lewin, P.L.: Partial discharge source discrimination using a support vector machine. IEEE Trans. Dielectr. Electr. Insul. 17(1), 189–197 (2010)CrossRefGoogle Scholar
  6. 6.
    Hao, L., Lewin, P.L., Dodd, S.J.: Comparison of support vector machine based partial discharge identification parameters. In: Conference Record of 2006 IEEE International Symposium on Electrical Insulation, pp. 110–113 (2006)Google Scholar
  7. 7.
    Hao, L., Lewin, P.L., Hunter, J.A., Swaffield, D.J., Contin, A., Walton, C., Michel, M.: Discrimination of multiple PD sources using wavelet decomposition and principal component analysis. IEEE Trans. Dielectr. Electr. Insul. 18(5), 1702–1711 (2011)CrossRefGoogle Scholar
  8. 8.
    Hauschild, W., Lemke, E.: High-Voltage Test and Measuring Techniques. Springer, Heidelberg (2014). CrossRefGoogle Scholar
  9. 9.
    Huang, N.E., Shen, Z., Long, S.R., Wu, M.C., Shih, H.H., Zheng, Q., Yen, N.C., Tung, C.C., Liu, H.H.: The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. In: Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, vol. 454, no. 1971, pp. 903–995 (1998)Google Scholar
  10. 10.
    Jolliffe, I.: Principal Component Analysis. Wiley Online Library, Hoboken (2002)zbMATHGoogle Scholar
  11. 11.
    Judd, M.D., Yang, L., Hunter, I.B.B.: Partial discharge monitoring of power transformers using UHF sensors. Part I: sensors and signal interpretation. IEEE Electr. Insul. Mag. 21(2), 5–14 (2005)CrossRefGoogle Scholar
  12. 12.
    Küchler, A.: Hochspannungstechnik. VDI-Verlag, Düsseldorf (2009)CrossRefGoogle Scholar
  13. 13.
    Lai, K.X., Phung, B.T., Blackburn, T.R.: Application of data mining on partial discharge part I: predictive modelling classification. IEEE Trans. Dielectr. Electr. Insul. 17(3), 846–854 (2010)CrossRefGoogle Scholar
  14. 14.
    Li-Xue, L., Cheng-Jun, H., Yi, Z., Xiu-Chen, J.: Partial discharge diagnosis on GIS based on envelope detection. WSEAS Trans. Syst. 7(11), 1238–1247 (2008)Google Scholar
  15. 15.
    Ma, H., Chan, J.C., Saha, T.K., Ekanayake, C.: Pattern recognition techniques and their applications for automatic classification of artificial partial discharge sources. IEEE Trans. Dielectr. Electr. Insul. 20(2), 468–478 (2013)CrossRefGoogle Scholar
  16. 16.
    Ma, X., Zhou, C., Kemp, I.: Interpretation of wavelet analysis and its application in partial discharge detection. IEEE Trans. Dielectr. Electr. Insul. 9(3), 446–457 (2002)CrossRefGoogle Scholar
  17. 17.
    Markalous, S.M., Tenbohlen, S., Feser, K.: Detection and location of partial discharges in power transformers using acoustic and electromagnetic signals. IEEE Trans. Dielectr. Electr. Insul. 15(6), 1576–1583 (2008)CrossRefGoogle Scholar
  18. 18.
    de Oliveira Mota, H., da Rocha, L.C.D., de Moura Salles, T.C., Vasconcelos, F.H.: Partial discharge signal denoising with spatially adaptive wavelet thresholding and support vector machines. Electr. Power Syst. Res. 81(2), 644–659 (2011)CrossRefGoogle Scholar
  19. 19.
    Shalev-Shwartz, S., Ben-David, S.: Understanding Machine Learning: From Theory to Algorithms. Cambridge University Press, Cambridge (2014)CrossRefzbMATHGoogle Scholar
  20. 20.
    Siegel, M., Beltle, M., Tenbohlen, S.: Characterization of UHF PD sensors for power transformers using an oil-filled GTEM cell. IEEE Trans. Dielectr. Electr. Insul. 23(3), 1580–1588 (2016)CrossRefGoogle Scholar
  21. 21.
    Soomro, I.A., Ramdon, M.N.: Study on different techniques of partial discharge (PD) detection in power transformers winding: simulation between paper and EPOXY resin using UHF method. Int. J. Concept. Electr. Electron. Eng. 2(1), 57–61 (2014)Google Scholar
  22. 22.
    Velayutham, M.R., Perumal, S., Basharan, V., Silluvairaj, W.I.M.: Support vector machine-based denoising technique for removal of white noise in partial discharge signal. Electr. Power Compon. Syst. 42(14), 1611–1622 (2014)CrossRefGoogle Scholar
  23. 23.
    Wu, M., Cao, H., Cao, J., Nguyen, H.L., Gomes, J.B., Krishnaswamy, S.P.: An overview of state-of-the-art partial discharge analysis techniques for condition monitoring. IEEE Electr. Insul. Mag. 31(6), 22–35 (2015)CrossRefGoogle Scholar
  24. 24.
    Wu, Z., Huang, N.E.: Ensemble empirical mode decomposition: a noise-assisted data analysis method. Adv. Adapt. Data Anal. 1(01), 1–41 (2009)CrossRefGoogle Scholar
  25. 25.
    Yong, Q., Cheng-Jun, H., Xiu-Chen, J.: Empirical mode decomposition based denoising of partial discharge signals. In: Proceedings of 5th WSEAS/IASME International Conference on Electric Power Systems, High Voltages, Electric Machines (2005)Google Scholar
  26. 26.
    Zhang, Y., Upton, D., Jaber, A., Ahmed, H., Saeed, B., Mather, P., Lazaridis, P., Mopty, A., Tachtatzis, C., Atkinson, R., Judd, M., de Fatima, M., Vieira, Q., Glover, I.: Radiometric wireless sensor network monitoring of partial discharge sources in electrical substations. Int. J. Distrib. Sens. Netw. 11(9), 1–9 (2015)Google Scholar
  27. 27.
    Zhu, M.X., Zhang, J.N., Li, Y., Wei, Y.H., Xue, J.Y., Deng, J.B., Mu, H.B., Zhang, G.J., Shao, X.J.: Partial discharge signals separation using cumulative energy function and mathematical morphology gradient. IEEE Trans. Dielectr. Electr. Insul. 23(1), 482–493 (2016)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Benjamin Schubert
    • 1
    Email author
  • Mauro Palo
    • 1
  • Thomas Schlechter
    • 1
  1. 1.Global Energy Interconnection Research Institute Europe GmbHBerlinGermany

Personalised recommendations