Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

access icon free Localisation of inter-layer partial discharges in transformer windings by logistic regression and different features extracted from current signals

© The Institution of Engineering and Technology
Received 30/04/2020, Accepted 30/11/2020, Revised 29/10/2020, Published 08/01/2021

Partial discharge (PD) investigations can identify and localise incipient failures in power transformers early, thus avoiding considerable financial losses. The feature extraction of PD signals is a fundamental step for the development of such location techniques since it directly influences the performance of a location method. This study presents a detailed comparative analysis of four traditional approaches for the obtaining of attributes towards a better set of signal features for the location of PDs. The approaches were critically compared regarding their ability to locate experimentally generated discharges between adjacent layers of a prototype winding. In order to perform such analysis, a localisation structure based on logistic regression models was elaborated, capable of determining both layers and sections of the winding affected by PDs and easily applicable in practice. The results show energy features of wavelet coefficients, obtained through the decomposition of high-frequency current signals acquired at the winding endings, achieve better performance in the PD localisation, accurately indicating discharge occurrence points among layers and sections of the winding.

Inspec keywords: feature extraction; transformer windings; regression analysis; partial discharges

Other keywords: high-frequency current signals; logistic regression models; PD signals; feature extraction; winding endings; PD localisation; location method; incipient failures; power transformers; detailed comparative analysis; current signals; transformer windings; interlayer partial discharges; discharge occurrence points; localisation structure; prototype winding

Subjects: Dielectric breakdown and discharges; Transformers and reactors

References

    1. 1)
      • 22. Biswas, S., Dey, D., Chatterjee, B., et al: ‘Cross-spectrum analysis based methodology for discrimination and localization of partial discharge sources using acoustic sensors’, IEEE Trans. Dielectr. Electr. Insul., 2016, 23, (6), pp. 3556–3565.
    2. 2)
      • 23. Hussein, R., Shaban, K.B., El.Hag, A.H.: ‘Energy conservation-based thresholding for effective wavelet denoising of partial discharge signals’, IET Sci. Meas. Technol., 2016, 10, (7), pp. 813–822.
    3. 3)
      • 28. Harbaji, M., Shaban, K., El.Hag, A.: ‘Classification of common partial discharge types in oil-paper insulation system using acoustic signals’, IEEE Trans. Dielectr. Electr. Insul., 2015, 22, (3), pp. 1674–1683.
    4. 4)
      • 25. Rodrigo Mor, A., Muñoz, F.A., Wu, J., et al: ‘Automatic partial discharge recognition using the cross wavelet transform in high voltage cable joint measuring systems using two opposite polarity sensors’, Int. J. Electr. Power Energy Syst., 2020, 117, p. 105695, (Article 105489).
    5. 5)
      • 32. Wan, X.: ‘The influence of polynomial order in logistic regression on decision boundary’. IOP Conf. Series: Earth and Environmental Science, Guangzhou, People's Republic of China, 2019, vol. 267, no. (4).
    6. 6)
      • 1. Marques, A., de Jesus Ribeiro, C., Azevedo, C.H., et al: ‘Power transformer disruptions – a case study’, IEEE Electr. Insul. Mag., 2014, 30, (2), pp. 17–21.
    7. 7)
      • 30. Dey, D., Chatterjee, B., Chakravorti, S., et al: ‘Cross-wavelet transform as a new paradigm for feature extraction from noisy partial discharge pulses’, IEEE Trans. Dielectr. Electr. Insul., 2010, 17, (1), pp. 157–166.
    8. 8)
      • 18. Gao, A., Zhu, Y., Cai, W., et al: ‘Pattern recognition of partial discharge based on VMD-CWD spectrum and optimized CNN with cross-layer feature fusion’, IEEE Access, 2020, 8, pp. 151296–151306.
    9. 9)
      • 29. Ma, X., Zhou, C., Kemp, I.J.: ‘Interpretation of wavelet analysis and its application in partial discharge detection’, IEEE Trans. Dielectr. Electr. Insul., 2002, 9, (3), pp. 446–457.
    10. 10)
      • 8. Wu, M., Cao, H., Cao, J., et al: ‘An overview of state-of-the-art partial discharge analysis techniques for condition monitoring’, IEEE Electr. Insul. Mag., 2015, 31, (6), pp. 22–35.
    11. 11)
      • 9. Hettiwatte, S.N., Wang, Z.D., Crossley, P.A.: ‘Investigation of propagation of partial discharges in power transformers and techniques for locating the discharge’, IEE Proc., Sci., Meas. Technol., 2005, 152, (1), pp. 25–30.
    12. 12)
      • 33. 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.
    13. 13)
      • 27. Guzmán, I.C., Oslinger, J.L., Nieto, R.D.: ‘Wavelet denoising of partial discharge signals and their pattern classification using artificial neural networks and support vector machines’, Dyna (Medellin), 2017, 84, (203), pp. 240–248.
    14. 14)
      • 6. Samimi, M.H., Ilkhechi, H.D.: ‘Survey of different sensors employed for the power transformer monitoring’, IET Sci. Meas. Technol., 2020, 14, (1), pp. 1–8.
    15. 15)
      • 10. Naderi, M.S., Vakilian, M., Blackburn, T.R., et al: ‘A hybrid transformer model for determination of partial discharge location in transformer winding’, IEEE Trans. Dielectr. Electr. Insul., 2007, 14, (2), pp. 436–443.
    16. 16)
      • 11. Jeyabalan, V., Usa, S.: ‘Frequency domain correlation technique for PD location in transformer winding’, IEEE Trans. Dielectr. Electr. Insul., 2009, 16, (4), pp. 1160–1167.
    17. 17)
      • 12. Nafar, M., Niknam, T., Gheisari, A.: ‘Using correlation coefficients for locating partial discharge in power transformer’, Electr. Power Energy Syst., 2011, 33, pp. 493–499.
    18. 18)
      • 21. Webb, A.R., Copsey, K.D.: ‘Statistical pattern recognition’ (John Wiley & Sons, West Sussex, United Kinggom, 2011, 3rd edn.).
    19. 19)
      • 14. Homaei, M., Moosavian, S.M., Illias, H.A.: ‘Partial discharge localization in power transformers using neuro-fuzzy technique’, IEEE Trans. Power Deliv., 2014, 29, (5), pp. 2066–2076.
    20. 20)
      • 34. Iorkyase, E.T., Tachtatzis, C., Lazaridis, P., et al: ‘Radio location of partial discharge sources: a support vector regression approach’, IET Sci. Meas. Technol., 2018, 12, (2), pp. 230–236.
    21. 21)
      • 2. Shuai, H., Qingmin, L., Chengrong, L., et al: ‘Electrical and mechanical properties of the oil-paper insulation under stress of the hot spot temperature’, IEEE Trans. Dielectr. Electr. Insul., 2014, 21, (1), pp. 179–185.
    22. 22)
      • 35. Gao, J., Zhu, Y., Jia, Y.: ‘Pattern recognition of unknown partial discharge based on improved SVDD’, IET Sci. Meas. Technol., 2018, 12, (7), pp. 907–916.
    23. 23)
      • 5. Mondal, M., Kumbhar, G.B.: ‘Detection, measurement, and classification of partial discharge in a power transformer: methods, trends, and future research’, IETE Tech. Rev., 2017, 35, pp. 1–11.
    24. 24)
      • 3. Florkowski, M.: ‘Hyperspectral imaging of high voltage insulating materials subjected to partial discharges’, Meas., J. Int. Meas. Confederation, 2020, 164, p. 108070.
    25. 25)
      • 20. Wang, Y., Zhang, X., Jiang, X., et al: ‘Effect of the oil-paper insulation aging on partial discharge characteristics in a hemispherical surface model’, IET Sci. Meas. Technol., 2019, 13, (5), pp. 729–736.
    26. 26)
      • 19. Cui, L., Chen, W., Vaughan, A.S., et al: ‘Comparative analysis of air-gap PD characteristics: vegetable oil/pressboard and mineral oil/pressboard’, IEEE Trans. Dielectr. Electr. Insul., 2017, 24, (1), pp. 137–146.
    27. 27)
      • 17. Duan, L., Hu, J., Zhao, G., et al: ‘Identification of partial discharge defects based on deep learning method’, IEEE Trans. Power Deliv., 2019, 34, (4), pp. 1557–1568.
    28. 28)
      • 24. Suryavanshi, H., Velandy, J., Sakthivel, M.: ‘Wavelet power ratio signature spectrum analysis for prediction of winding insulation defects in transformer and shunt reactor’, IEEE Trans. Dielectr. Electr. Insul., 2017, 24, (4), pp. 2649–2659.
    29. 29)
      • 16. Gonçalves Júnior, A.M., de Paula, H., do Couto Boaventura, W.: ‘Practical partial discharge pulse generation and location within transformer windings using regression models adjusted with simulated signals’, Electr. Power Syst. Res., 2018, 157, pp. 118–125.
    30. 30)
      • 31. James, G., Witten, D., Hastie, T., et al: ‘An Introduction to statistical learning’, vol. 7 (Springer, USA, 2013).
    31. 31)
      • 4. Chan, J.C., Ma, H., Saha, T.K.: ‘Hybrid method on signal de-noising and representation for online partial discharge monitoring of power transformers at substations’, IET Sci. Meas. Technol., 2015, 9, (7), pp. 890–899.
    32. 32)
      • 7. Raymond, W.J.K., Illias, H.A., Bakar, A.H.A., et al: ‘Partial discharge classifications: review of recent progress’, Meas., J. Int. Meas. Confederation, 2015, 68, pp. 164–181.
    33. 33)
      • 15. Rahman, M.S.A., Lewin, P.L., Rapisarda, P.: ‘Autonomous localization of partial discharge sources within large transformer windings’, IEEE Trans. Dielectr. Electr. Insul., 2016, 23, (2), pp. 1088–1098.
    34. 34)
      • 13. Mondal, M., Kumbhar, G.B., Kulkarni, S.V.: ‘Localization of partial discharges inside a transformer winding using a ladder network constructed from terminal measurements’, IEEE Trans. Power Deliv., 2018, 33, (3), pp. 1035–1043.
    35. 35)
      • 26. Morette, N., Castro Heredia, L.C., Ditchi, T., et al: ‘Partial discharges and noise classification under HVDC using unsupervised and semisupervised learning’, Int. J. Electr. Power Energy Syst., 2020, 121, (March), p. 106129.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-smt.2020.0186
Loading

Related content

content/journals/10.1049/iet-smt.2020.0186
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
Print this page
This is a required field
Please enter a valid email address