access icon free Time–frequency analysis of PD-induced UHF signal in GIS and feature extraction using invariant moments

© The Institution of Engineering and Technology
Received 06/07/2017, Accepted 09/10/2017, Published 11/10/2017

The ultra-high-frequency (UHF) method is efficient in partial discharges (PDs) detection in gas-insulated switchgear (GIS). The features extraction of UHF signals is significant for propagation characteristics analysis and PD pattern classification. The PD-induced UHF signals are acquired by the internal UHF sensors in an actual 252 kV L-shaped GIS. The short-time Fourier transform method is applied to process UHF signals and describe the propagation characteristics in L-shaped GIS. Hu's invariant moments of energy density distribution are extracted as features in time–frequency plane. The features are utilised to discriminate different PD defect patterns in actual GIS model by the support vector machine classifier and achieve good results. Finally, a novel system of features extraction and classification of UHF signals is summarised.

Inspec keywords: signal classification; signal detection; time-frequency analysis; UHF detectors; gas insulated switchgear; UHF measurement; feature extraction; Fourier transforms; support vector machines; partial discharge measurement

Other keywords: Hu invariant moment; Time–frequency analysis; energy density distribution extraction; ultrahigh-frequency method; gas-insulated switchgear; UHF signal processing; voltage 252 kV; internal UHF sensor; propagation characteristics analysis; partial discharge detection; L-shaped GIS; feature extraction; PD-induced UHF signal detection; support vector machine classifier; UHF signal classification; PD-induced UHF signal acquisition; short-time Fourier transform method; PD pattern classification

Subjects: Dielectric breakdown and discharges; Signal detection; Sensing devices and transducers; Switchgear; Charge measurement; Integral transforms; Microwave circuits and devices; Microwave measurement techniques

References

    1. 1)
      • 7. Hao, L., Lewin, P.L., Hunter, J.A., et al: ‘Discrimination of multiple PD sources using wavelet decomposition and principal component analysis’, IEEE Trans. Dielectr. Electr. Insul., 2011, 18, (5), pp. 1702–1711.
    2. 2)
      • 34. Qian, S.: ‘Introduction to time-frequency and wavelet transforms’ (Prentice Hall, 2002).
    3. 3)
      • 26. Evagorou, D., Kyprianou, A., Lewin, P.L., et al: ‘Feature extraction of partial discharge signals using the wavelet packet transform and classification with a probabilistic neural network’, IET Sci. Meas. Technol., 2010, 4, (3), pp. 177–192.
    4. 4)
      • 38. Pattanachai, N., Covavisaruch, N., Sinthanayothin, C.: ‘Tooth recognition in dental radiographs via Hu's moment invariants’. Int. Conf. Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, 2012, pp. 1–4.
    5. 5)
      • 14. Hikita, M., Ohtsuka, S., Wada, J., et al: ‘Study of partial discharge radiated electromagnetic wave propagation characteristics in an actual 154 kV model GIS’, IEEE Trans. Dielectr. Electr. Insul., 2012, 19, (1), pp. 8–17.
    6. 6)
      • 19. Gao, W., Ding, D., Liu, W.: ‘Research on the typical partial discharge using the UHF detection method for GIS’, IEEE Trans. Power Deliv., 2011, 26, (4), pp. 2621–2629.
    7. 7)
      • 9. Chang, C., Chang, C.S., Jin, J., et al: ‘Source classification of partial discharge for gas insulated substation using waveshape pattern recognition’, IEEE Trans. Dielectr. Electr. Insul., 2005, 12, (2), pp. 374–386.
    8. 8)
      • 8. Rudd, S., Mcarthur, S.D.J., Judd, M.D.: ‘A generic knowledge-based approach to the analysis of partial discharge data’, IEEE Trans. Dielectr. Electr. Insul., 2010, 17, (1), pp. 149–156.
    9. 9)
      • 10. 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., 2010, 17, (3), pp. 846–854.
    10. 10)
      • 13. Li, T., Wang, X., Zheng, C., et al: ‘Investigation on the placement effect of UHF sensor and propagation characteristics of PD-induced electromagnetic wave in GIS based on FDTD method’, IEEE Trans. Dielectr. Electr. Insul., 2014, 21, (3), pp. 1015–1025.
    11. 11)
      • 1. Hikita, M., Ohtsuka, S., Matsumoto, S.: ‘Recent trend of the partial discharge measurement technique using the UHF electromagnetic wave detection method’, IEEJ Trans. Electr. Electron. Eng., 2007, 2, (5), pp. 504–509.
    12. 12)
      • 12. Florkowski, M., Florkowska, B.: ‘Phase-resolved rise-time-based discrimination of partial discharges’, IET Gener. Transm. Distrib., 2009, 3, (1), pp. 115–124.
    13. 13)
      • 35. Sejdić, E., Djurović, I., Jiang, J.: ‘Time–frequency feature representation using energy concentration: an overview of recent advances’, Digit. Signal Process., 2009, 19, (1), pp. 153–183.
    14. 14)
      • 4. Li, X., Wang, X., Yang, A., et al: ‘Propogation characteristics of PD-induced UHF signal in 126 kV GIS with three-phase construction based on time-frequency analysis’, IET Sci. Meas. Technol., 2016, 10, (7), pp. 805–812.
    15. 15)
      • 39. Xu, D., Hu, P., Cao, W., et al: ‘SHREC'08 entry: 3D face recognition using moment invariants’. Int. Conf. Shape Modeling and Applications, 2008, pp. 261–262.
    16. 16)
      • 41. Zhang, X., Huang, R., Yao, S., et al: ‘Mechanical life prognosis of high voltage circuit breakers based on support vector machine’. Int. Conf. Natural Computation, 2015.
    17. 17)
      • 30. Li, T., Rong, M., Liu, D., et al: ‘Experimental research on partial discharge radiated UHF signal attenuation characteristics in GIS’. Tencon IEEE Region 10 Conf., 2013, pp. 1–4.
    18. 18)
      • 6. Sahoo, N.C., Salama, M.M.A., Bartnikas, R.: ‘Trends in partial discharge pattern classification: a survey’,  IEEE Trans. Dielectr. Electr. Insul., 2005, 12, (2), pp. 248–264.
    19. 19)
      • 29. 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., 2009, 16, (1), pp. 199–205.
    20. 20)
      • 32. 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., 2007, 14, (3), pp. 702–709.
    21. 21)
      • 25. Florkowski, M., Florkowska, B.: ‘Wavelet-based partial discharge image denoising’, IET. Gener. Transm. Distrib., 2007, 1, (2), pp. 340–347.
    22. 22)
      • 5. Koo, J.Y., Jung, S.Y., Ryu, C.H., et al: ‘Identification of insulation defects in gas-insulated switchgear by chaotic analysis of partial discharge’, IET Sci. Meas. Technol., 2010, 4, (3), pp. 115–124.
    23. 23)
      • 33. Okabe, S., Yuasa, S., Kaneko, S., et al: ‘Simulation of propagation characteristics of higher order mode electromagnetic waves in GIS’. Int. Conf. Properties and Applications of Dielectric Materials, 2006, pp. 855–861.
    24. 24)
      • 22. Li, J., Sun, C., Grzybowski, S., et al: ‘Partial discharge image recognition using a new group of features’, IEEE Trans. Dielectr. Electr. Insul., 2006, 13, (6), pp. 1245–1253.
    25. 25)
      • 3. Tang, J., Zhou, J., Zhang, X., et al: ‘A transformer partial discharge measurement system based on fluorescent fiber’, Energies, 2012, 5, (12), pp. 1490–1502.
    26. 26)
      • 23. Liao, R., Duan, L., Wang, K., et al: ‘Partial discharge pattern recognition based on time-frequency analysis and 2DNMF’, Electr. Power Autom. Equip., 2013, 33, (03), pp. 20–25.
    27. 27)
      • 28. Hoshino, T., Maruyama, S., Nakajima, T., et al: ‘Development of coaxial-to-waveguide antenna attached outer GIS for detecting partial discharge’. Int. Conf. Condition Monitoring and Diagnosis, 2008, CMD 2008, 2008, pp. 854–858.
    28. 28)
      • 21. Li, J., Sun, C., Grzybowski, S.: ‘Partial discharge image recognition influenced by fractal image compression’, IEEE Trans. Dielectr. Electr. Insul., 2008, 15, (2), pp. 496–504.
    29. 29)
      • 37. Noh, J.S., Rhee, K.H.: ‘Palmprint identification algorithm using Hu invariant moments and Otsu binarization’. Fourth Annual ACIS Int. Conf. Computer and Information Science (ICIS'05), 2005, pp. 94–99.
    30. 30)
      • 15. Salama, M.M.A., Bartnikas, R.: ‘Determination of neural-network topology for partial discharge pulse pattern recognition’, IEEE Trans. Neural Netw., 2002, 13, (2), pp. 446–456.
    31. 31)
      • 2. Judd, M.D., Farish, O., Hampton, B.F.: ‘The excitation of UHF signals by partial discharges in GIS’, IEEE Trans. Dielectr. Electr. Insul., 1996, 3, (2), pp. 213–228.
    32. 32)
      • 31. Li, T., Rong, M., Zheng, C., et al: ‘Development simulation and experiment study on UHF partial discharge sensor in GIS’, IEEE Trans. Dielectr. Electr. Insul., 2012, 19, (4), pp. 1421–1430.
    33. 33)
      • 43. Abe, S.: ‘Support vector machines for pattern classification’ (Springer, London, 2010, 2nd edn.).
    34. 34)
      • 27. Li, J., Cheng, C., Jiang, T., et al: ‘Wavelet de-noising of partial discharge signals based on genetic adaptive threshold estimation’, IEEE Trans. Dielectr. Electr. Insul., 2012, 19, (2), pp. 543–549.
    35. 35)
      • 42. Hao, L., Lewin, P.L.: ‘Partial discharge source discrimination using a support vector machine’, IEEE Trans. Dielectr. Electr. Insul., 2010, 17, (1), pp. 189–197.
    36. 36)
      • 16. Gao, W., Ding, D., Liu, W., et al: ‘Analysis of the intrinsic characteristics of the partial discharge induced by typical defects in GIS’, IEEE Trans. Dielectr. Electr. Insul., 2013, 20, (3), pp. 782–790.
    37. 37)
      • 11. Zeng, F.P., Dong, Y.L., Tang, J.: ‘Feature extraction and severity assessment of partial discharge under protrusion defect based on fuzzy comprehensive evaluation’, IET Gener. Transm. Distrib., 2015, 9, (16), pp. 2493–2500.
    38. 38)
      • 36. Hu, M.: ‘Visual pattern recognition by moment invariants’, IEE Trans. Inf. Theory, 1962, 8, (2), pp. 179–187.
    39. 39)
      • 40. Chang, C.-C., Lin, C.-J.: ‘LIBSVM: a library for support vector machines’, ACM Trans. Intell. Syst. Technol., 2011, 2, (3), pp. 1–27.
    40. 40)
      • 24. Wang, K., Liao, R., Wang, J., et al: ‘Time-frequency features extraction and clustering analysis of partial discharge UHF pulses’, Trans. China Electrotech. Soc., 2015, 30, (02), pp. 211–219.
    41. 41)
      • 20. Sharkawy, R.M., Mangoubi, R.S., Abdel-Galil, T.K., et al: ‘SVM classification of contaminating particles in liquid dielectrics using higher order statistics of electrical and acoustic PD measurements’, IEEE Trans. Dielectr. Electr. Insul., 2007, 14, (3), pp. 669–678.
    42. 42)
      • 17. Li, X., Wang, X., Rong, M., et al: ‘Comparison of different time-frequency analysis methods for sparse representation of PD-induced UHF signal’. 2016 China Int. Conf. Electricity Distribution (CICED), 10–13 August 2016, pp. 1–5.
    43. 43)
      • 18. Li, X., Wang, X., Rong, M., et al: ‘Relationship between time-frequency representation of PD-induced UHF signal and PD current pulse’. 2016 Int. Conf. Condition Monitoring and Diagnosis (CMD), 25–28 September 2016, pp. 234–237.
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