Online monitoring of transformer through stream clustering of partial discharge signals

IET Science, Measurement and Technology

Volumn 13, Issue 3, 2019, Pages 409-415

  Firuzi, Keyvan ^a   Vakilian, Mehdi ^a   Phung, B Toan ^b   Blackburn, Trevor ^b  

^a SHARIF UNIVERSITY OF TECHNOLOGY   (Iran)

^b UNIVERSITY OF NEW SOUTH WALES   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

CLUSTER ANALYSIS; PARTIAL DISCHARGES; POWER TRANSFORMERS; SEPARATION; SOURCE SEPARATION;

DISCHARGE PATTERNS; ONLINE MONITORING; PARTIAL DISCHARGE SIGNAL; PERFORMANCE OF THE DENSITIES; PROCESSING THE SIGNALS; REAL TIME MONITORING; STREAM CLUSTERING; TRADITIONAL CLUSTERING;

CLUSTERING ALGORITHMS;

EID: 85065076399     PISSN: 17518822     EISSN: None     Source Type: Journal    
DOI: 10.1049/iet-smt.2018.5389     Document Type: Article

References (28)

1. Jain, M.B., Srinivas, M.B., Jain, A.: ‘A novel web based expert system architecture for on-line and off-line fault diagnosis and control (FDC) of transformers’. TENCON 2008–2008 IEEE Region 10 Conf., Hyderabad, India, 2008, pp. 1–5
2. Rusov, V., Zhivodernikov, S.: ‘Transformer condition monitoring’. Int. Conf. Condition Monitoring and Diagnosis, 2008, CMD 2008, Beijing, China, 2008, pp. 1012–1014
3. Naidu, M.S.: ‘High voltage engineering’ (Tata McGraw-Hill Education, New York, UK, 2013)
4. Cavallini, A., Conti, M., Contin, A., et al.: ‘Advanced PD inference in on-field measurements. II. Identification of defects in solid insulation systems’, IEEE Trans. Dielectr. Electr. Insul., 2003, 10, (3), pp. 528–538
5. Allahbakhshi, M., Akbari, A.: ‘A method for discriminating original pulses in online partial discharge measurement’, Measurement, 2011, 44, (1), pp. 148–158
6. Ardila-Rey, J.A., Martínez-Tarifa, J.M., Robles, G., et al.: ‘Partial discharge and noise separation by means of spectral-power clustering techniques’, IEEE Trans. Dielectr. Electr. Insul., 2013, 20, (4), pp. 1436–1443
7. Darabad, V.P., Vakilian, M., Phung, B.T., et al.: ‘An efficient diagnosis method for data mining on single PD pulses of transformer insulation defect models’, IEEE Trans. Dielectr. Electr. Insul., 2013, 20, (6), pp. 2061–2072
8. Firuzi, K., Vakilian, M.: ‘A novel method for differentiating and clustering multiple partial discharge sources using S transform and bag of words feature’
9. Lin, Y.-H.: ‘Using k-means clustering and parameter weighting for partial-discharge noise suppression’, IEEE Trans. Power Deliv., 2011, 26, (4), pp. 2380–2390
10. Peng, X., Zhou, C., 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, (3), pp. 754–761
11. Li, J., Liao, R., Grzybowski, S., et al.: ‘Oil-paper aging evaluation by fuzzy clustering and factor analysis to statistical parameters of partial discharges’, IEEE Trans. Dielectr. Electr. Insul., 2010, 17, (3), pp. 756–763
12. 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., 2008, 23, (4), pp. 2008–2016
13. Zhu, M.-X., Zhang, J.N., Li, Y., et al.: ‘Partial discharge signals separation using cumulative energy function and mathematical morphology gradient’, IEEE Trans. Dielectr. Electr. Insul., 2016, 23, (1), pp. 482–493
14. Chan, J.C., Ma, H., Saha, T.K.: ‘Time-frequency sparsity map on automatic partial discharge sources separation for power transformer condition assessment’, IEEE Trans. Dielectr. Electr. Insul., 2015, 22, (4), pp. 2271–2283
15. Wang, K., Li, J., Zhang, S., et al.: ‘A hybrid algorithm based on s transform and affinity propagation clustering for separation of two simultaneously artificial partial discharge sources’, IEEE Trans. Dielectr. Electr. Insul., 2015, 22, (2), pp. 1042–1060
16. 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, (2), pp. 468–478
17. Rostaminia, R., Saniei, M., Vakilian, M., et al.: ‘Accurate power transformer PD pattern recognition via its model’, IET Sci. Meas. Technol., 2016, 10, (7), pp. 745–753
18. Guha, S., Meyerson, A., Mishra, N., et al.: ‘Clustering data streams: theory and practice’, IEEE Trans. Knowl. Data Eng., 2003, 15, (3), pp. 515–528
19. Muthukrishnan, S.: ‘Data streams: algorithms and applications’, Found. Trends® Theor. Comput. Sci., 2005, 1, (2), pp. 117–236
20. Andrade, H.C.M., Gedik, B., Turaga, D.S.: ‘Fundamentals of stream processing: application design, systems, and analytics’ (Cambridge University Press, Cambridge, UK, 2014)
21. Garofalakis, M., Gehrke, J., Rastogi, R.: ‘Data stream management: processing high-speed data streams’ (Springer, New York, USA, 2016)
22. Guha, S., Mishra, N.: ‘Clustering data streams’, in Garofalakis, M. (Ed.): ‘Data stream management’, (Springer, New York, USA, 2016), pp. 169–187
23. Gaber, M.M., Zaslavsky, A., Krishnaswamy, S.: ‘Mining data streams: a review’, ACM Sigmod Rec., 2005, 34, (2), pp. 18–26
24. Aggarwal, C.C.: ‘Data streams: models and algorithms’, vol. 31 (Springer Science & Business Media, New York, USA, 2007)
25. Kim, H.G., Kim, M.H.: ‘A review of window query processing for data streams’, J. Comput. Sci. Eng., 2013, 7, (4), pp. 220–230
26. Cao, F., Estert, M., Qian, W., et al.: ‘Density-based clustering over an evolving data stream with noise’. Proc. 2006 SIAM Int. Conf. Data Mining, Hong Kong, 2006, pp. 328–339
27. Firuzi, K., Vakilian, M.: ‘Partial discharges pattern recognition of power transformer defect model by LBP & HOG features’
28. Rokach, L., Maimon, O. (Eds.): ‘Clustering methods’, in ‘Data mining and knowledge discovery handbook’, (Springer, New York, USA, 2005), pp. 321–352