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access icon openaccess Fractal-based autonomous partial discharge pattern recognition method for MV motors

On-line partial discharge (PD) monitoring is being increasingly adopted to improve the asset management and maintenance of medium-voltage (MV) motors. This study presents a novel method for autonomous analysis and classification of motor PD patterns in situations where a phase-reference voltage waveform is not available. The main contributions include a polar PD (PPD) pattern and a fractal theory-based autonomous PD recognition method. PPD pattern that is applied to convert the traditional phase-resolved PD pattern into a circular form addresses the lack of phase information in on-line PD monitoring system. The fractal theory is then presented in detail to address the task of discrimination of 6 kinds of single source and 15 kinds of multi-source PD patterns related to motors, as outlined in IEC 60034. The classification of known and unknown defects is calculated by a method known as centre score. Validation of the proposed method is demonstrated using data from laboratory experiments on three typical PD geometries. This study also discusses the application of the proposed techniques with 24 sets of on-site PD measurement data from 4 motors in 2 nuclear power stations. The results show that the proposed method performs effectively in recognising not only the single-source PD but also multi-source PDs.

References

    1. 1)
      • 4. Zhou, C., Yi, H., Dong, X.: ‘Review of recent research towards power cable life cycle management’, High Volt., 2017, 2, (3), pp. 179187.
    2. 2)
      • 21. Zhou, C., Hepburn, D.M., Song, X., et al: ‘Application of denoising techniques to PD measurement utilising UHF, HFCT, acoustic sensors and IEC60270’. CIRED 2009 – 20th Int. Conf. and Exhibition on Electricity Distribution – Part 1, Prague, Czech Republic, 2009, pp. 14.
    3. 3)
      • 26. Satish, L., Zaengl, W.S.: ‘Can fractal features be used for recognizing 3-d partial discharge patterns’, IEEE Trans. Dielectr. Electr. Insul., 1995, 2, pp. 352359.
    4. 4)
      • 13. Lalitha, E.M., Satish, L.: ‘Wavelet analysis for classification of multi-source PD patterns’, IEEE Trans. Dielectr. Electr. Insul., 2000, 7, pp. 4047.
    5. 5)
      • 32. Kreuger, F.F.H., Gulski, E., Krivda, A.: ‘Classification of partial discharges’, IEEE Trans. Electr. Insul., 1993, 28, (6), pp. 917931.
    6. 6)
      • 5. Li, S., Nie, Y., Li, J.: ‘Condition monitoring and diagnosis of power equipment: review and prospective’, High Volt., 2017, 2, (2), pp. 8291.
    7. 7)
      • 8. Cacciari, M., Contin, A., Montanari, G.C.: ‘Use of a mixed-Weibull distribution for the identification of PD phenomena [rotating machines]’, IEEE Trans. Dielectr. Electr. Insul., 1995, 2, pp. 614627.
    8. 8)
      • 1. Stone, G.: ‘The use of partial discharge measurements to assess the condition of rotating machine insulation’, IEEE Electr. Insul. Mag., 1996, 12, (4), pp. 2327.
    9. 9)
      • 2. Kheirmand, A., Leijon, M., Gubanski, S.M.: ‘Advances in online monitoring and localization of partial discharges in large rotating machines’, IEEE Trans. Energy Convers., 2004, 19, pp. 5359.
    10. 10)
      • 20. IEC TS 60034-27-2:2012: ‘Rotating electrical machines – part 27-2: on-line partial discharge measurements on the stator winding insulation of rotating electrical machines’, 2012.
    11. 11)
      • 19. Xiaosheng, P., Chengke, Z., Hepburn, D.M., et al: ‘Application of K-means method to pattern recognition in on-line cable partial discharge monitoring’, IEEE Trans. Dielectr. Electr. Insul., 2013, 20, pp. 754761.
    12. 12)
      • 29. Krivda, A., Gulski, E., Satish, L., et al: ‘The use of fractal features for recognition of 3-D discharge patterns’, IEEE Trans. Dielectr. Electr. Insul., 1995, 2, pp. 889892.
    13. 13)
      • 34. IEEE Std 1434-2014: ‘IEEE guide for the measurement of partial discharges in AC electric machinery’, 2014.
    14. 14)
      • 16. Oskuoee, M., Yazdizadeh, A.R., Mahdiani, H.R.: ‘A new feature extraction and pattern recognition of partial discharge in solid material by neural network’. 2012 Eighth Int. Conf. Natural Computation (ICNC), 2012, pp. 183187.
    15. 15)
      • 25. Keller, J.M., Chen, S., Crownover, R.M.: ‘Texture description and segmentation through fractal geometry’, Comput. Vis. Graph. Image Process., 1989, 45, pp. 150166.
    16. 16)
      • 12. Cavallini, A., Montanari, G., Fabiani, D., et al: ‘Advanced technique for partial discharge detection and analysis in power cables’. Int. Conf. Condition Monitoring & Diagnostic Engineering Management of Power Station/Substation Equipment, 2009, pp. 14.
    17. 17)
      • 7. Xiaoxing, Z., Jiangbo, R., Ju, T., et al: ‘Kernel statistical uncorrelated optimum discriminant vectors algorithm for GIS PD recognition’, IEEE Trans. Dielectr. Electr. Insul., 2009, 16, pp. 206213.
    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, pp. 248264.
    19. 19)
      • 23. Mandelbrot, B.: ‘The fractal geometry of nature’ (W.H. Freeman and Company, New York, 1982), pp. 166180.
    20. 20)
      • 17. Kranz, H.-G.: ‘Diagnosis of partial discharge signals using neural networks and minimum distance classification’, IEEE Trans. Electr. Insul., 1993, 28, pp. 10161024.
    21. 21)
      • 18. Salama, M., Bartnikas, R.: ‘Fuzzy logic applied to PD pattern classification’, IEEE Trans. Dielectr. Electr. Insul., 2000, 7, pp. 118123.
    22. 22)
      • 30. Keller, J.M., Balghonaim, A.S.: ‘A maximum likelihood estimate for two-variable fractal surface’, IEEE Trans. Image Process., 1998, 7, (12), pp. 17461753.
    23. 23)
      • 15. Danikas, M., Gao, N., Aro, M.: ‘Partial discharge recognition using neural networks: a review’, Electr. Eng., 2003, 85, (2), pp. 8793.
    24. 24)
      • 22. Song, X., Zhou, C., Hepburn, D.M., et al: ‘Second generation wavelet transform for data denoising in PD measurement’, IEEE Trans. Dielectr. Electr. Insul., 2007, 14, (6), pp. 15311537.
    25. 25)
      • 10. Gulski, E.: ‘Discharge pattern recognition in high voltage equipment’. 1993 Int. Conf. Partial Discharge, Canterbury, 1993, pp. 3638.
    26. 26)
      • 14. Ma, H., Chan, J.C., Saha, T.K., et al: ‘Pattern recognition techniques and their applications for automatic classification of artificial partial discharge sources’, IEEE Trans. Dielectr. Electr. Insul., 2013, 20, pp. 468478.
    27. 27)
      • 9. Gulski, E.: ‘Digital analysis of partial discharges’, IEEE Trans. Dielectr. Electr. Insul., 1995, 2, pp. 822837.
    28. 28)
      • 27. Candela, R., Mirelli, G., Schifani, R.: ‘PD recognition by means of statistical and fractal parameters and a neural network’, IEEE Trans. Dielectr. Electr. Insul., 2000, 7, pp. 8794.
    29. 29)
      • 28. Jian, L., Caixin, S., Grzybowski, S., et al: ‘Partial discharge image recognition using a new group of features’, IEEE Trans. Dielectr. Electr. Insul., 2006, 13, pp. 12451253.
    30. 30)
      • 24. Chen, S.S., Keller, J.M., Crownover, R.M.: ‘On the calculation of fractal features from images’, IEEE Trans. Pattern Anal. Mach. Intell., 1993, 15, (10), pp. 10871090.
    31. 31)
      • 33. Sarkar, N., Chaudhuri, B.B.: ‘An efficient differential box-counting approach to compute fractal dimension of image’, IEEE Trans. Syst. Man Cybern., 1994, 24, pp. 115120.
    32. 32)
      • 3. Krivda, A.: ‘Recognition of discharges: discrimination and classification’ (Delft University Press, Delft, 1995), pp. 4153.
    33. 33)
      • 11. Xiaoxing, Z., Song, X., Na, S., et al: ‘GIS partial discharge pattern recognition based on the chaos theory’, IEEE Trans. Dielectr. Electr. Insul., 2014, 21, pp. 783790.
    34. 34)
      • 31. Li, J., Sun, C., Li, X., et al: ‘Partial discharge pattern recognition using fractal dimension’. Proc. of 2001 Int. Symp. Electrical Insulating Materials, 2001 (ISEIM 2001), 2001, pp. 137140.
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