Application of support vector machines for fault diagnosis in power transmission system

IET Generation, Transmission and Distribution

Volumn 2, Issue 1, 2008, Pages 119-130

  Ravikumar, B ^a   Thukaram, D ^a   Khincha, H P ^a  

^a INDIAN INSTITUTE OF SCIENCE   (India)

Author keywords

[No Author keywords available]

Indexed keywords

ELECTRIC POWER TRANSMISSION; ELECTRIC SUBSTATIONS; INTELLIGENT AGENTS; KNOWLEDGE BASED SYSTEMS; NEURAL NETWORKS; REGRESSION ANALYSIS; SUPPORT VECTOR MACHINES;

MAL OPERATION; POWER SYSTEM DEGRADATION; PROTECTION SYSTEMS;

FAILURE ANALYSIS;

EID: 37249018157     PISSN: 17518687     EISSN: None     Source Type: Journal    
DOI: 10.1049/iet-gtd:20070071     Document Type: Article

References (39)

1. Phadke, A.G., and Thorp, J.S.: ' Expose hidden failures to prevent cascading outages ', Comput. Appl. Power IEEE, 1996, p. 20-23
2. ftp://www.nerc.com/pub/sys/all_updl/docs/blackout/BlackoutTable.pdf
3. ftp://www.nerc.com/pub/sys/all_updl/docs/blackout/ch7-10.pdf
4. http://www.eecs.wsu.edu/~mani/blackouts.PDF
5. http://www.columbia.edu/itc/sociology/watts/w3233/client_edit/ power_outages.html
6. Thorp, J.S., and Phadke, A.G.: ' Protecting power systems in the post-restructuring era ', Comput. Appl. Power IEEE, 1999, 12, (1), p. 33-37
7. Horowitz, S.H., and Phadke, A.G.: ' Boosting immunity to blackouts ', Power Energy Mag. IEEE, 2003, 1, (5), p. 47-53
8. Phadke, A.G., and Horowitz, S.H.: ' Adaptive relaying ', Comput. Appl. Power IEEE, 1990, 3, (3), p. 47-51
9. Karlsson, D., Morten, H., and Lindhal, S.: ' Wide area system monitoring and control ', IEEE Power Energy Syst., 2004, 2, p. 68-76
10. Begovic, M., Novosel, D., Karlsson, D., Henville, C., and Michel, G.: ' Wide-area protection and emergency control ', Proc. IEEE, 2005, 93, (5), p. 876-891 10.1109/JPROC.2005.847258 0018-9219
11. Thukaram, D., Khincha, H.P., and Vijaynarasimha, H.P.: ' Artificial neural network and support vector machine approach for locating faults in radial distribution systems ', Trans. Power Deliv. IEEE, 2005, 20, (2), p. 710-721
12. Vapnik, V.N.: ' The nature of statistical learning theory ', (Springer-Verlag, New York, 1995)
13. Salat, R., and Osowski, S.: ' Accurate fault location in the power transmission line using support vector machine approach ', Power Syst., IEEE Trans., 2004, 19, (2), p. 979-986
14. Janik, P., and Lobos, T.: ' Automated classification of power-quality disturbances using SVM and RBF networks ', Power Deliv. IEEE Trans., 2006, 21, (3), p. 1663-1669
15. Janik, P., Lobos, T., and Schegner, P.: Eighth IEE International Conf. on Developments in Power System Protection, April, 5-8, 2004, 2, p. 768-771
16. Moulin, L.S., da Silva, A.P.A., El-Sharkawi, M.A., and Marks, R.J.: ' Support vector machines for transient stability analysis of large-scale power systems ', IEEE Trans. Power Syst., 2004, 19, (2), p. 818-825 10.1109/TPWRS.2004.826018 0885-8950
17. Moulin, L.S., da Silva, A.P.A., El-Sharkawi, M.A., and Marks, R.J.: ' Neural networks and support vector machines applied to power systems transient stability analysis ', Int. J. Eng. Intell. Syst., 2001, 9, (4), p. 205-211
18. Schölkopf, B., Burges, C., and Smola, A.: ' Advances in kernel methods - support vector learning ', (MIT Press, Cambridge, MA, 1999)
19. Vapnik, V.: ' Statistical learning theory ', (Wiley, New York, NY, 1998)
20. Sastry, P.S.: ' An introduction to support vector machine, chapter from computing and information sciences: recent trends ', (Narosa Publishing House, New Delhi, 2003)
21. Burges, C.J.C.: ' A tutorial on support vector machines for pattern recognition ', Data Mining Knowl. Discov., 1998, 2, (2), p. 955-974
22. Joachims, T.: ' Making large-scale SVM learning practical. Advances in Kernel Methods - Support Vector Learning ', (MIT Press, Cambridge, MA, 1998)
23. Platt, J.: ' Fast training of support vector machines using sequential minimal optimization, advance in kernel methods: support vector learning ', (MIT Press, Cambridge, MA, 1999), p. 185-208, available at: http://www.research. microsoft.com/users/jplatt/smo-book.pdf
24. ' Multi-class support vector machines ', Technical Report CSD-TR-98-04, Royal Holloway, University of London, Department of Computer Science, 1998, Egham, TW20 0EX, UK, available at: http://citeseer.ist.psu.edu/article/ weston98multiclass.html
25. Platt, J., Cristianini, N., and Shawe-Taylor, J.: ' Large margin dags for multiclass classification ', Advances in neural information processing systems, 12th (MIT Press, Cambridge, MA, 2000)
26. ' A tutorial on support vector regression ', NeuroCOLT2 Tech Report, NC2-TR-1998-030, 1998
27. Schölkopf, B., Smola, A., Williamson, R.C., and Bartlett, P.L.: ' New support vector algorithms ', Neural Comput., 2000, 12, p. 1207-1245 10.1162/089976600300015565 0899-7667
28. Campbell, C.: ' Kernel methods: a survey of current techniques ', Neuro Comput., 2002, 48, p. 63-84
29. Keerthi, S.S., and Lin, C.-J.: ' Asymptotic behaviors of support vector machines with Gaussian kernel ', Neural Comput., 2003, 15, (7), p. 1667-1689 10.1162/089976603321891855 0899-7667
30. ' A study on sigmoid kernels for SVM and the training of non-PSD kernels by SMO-type methods ', Technical report, National Taiwan University, Department of Computer Science and Information Engineering, 2003, available at: http://www.csie.ntu.edu.tw/~cjlin/papers/tanh.pdf
31. Buhmann, M.D., and Ablowitz, M.J.: ' Radial basis functions: theory and implementations ', (Cambridge University, 2003)
32. Haykin, S.: ' Neural networks: a comprehensice foundation ', 2nd (Prentice-Hall, Upper Saddle River, NJ, 1999)
33. Dommel, H.W.: ' Techniques for analyzing electromagnetic transients ', IEEE Comput. Appl. Power, 1997, 10, p. 18-21 0895-0156
34. Dommel, H.W.: ' Digital computer solution of electromagnetic transient in single and multiphase networks ', IEEE Trans., 1969, PAS-88, p. 388-398 0018-9510
35. Raychaudhuri, S., Stuart, J.M., and Altman, R.B.: ' Principal components analysis to summarize microarray experiments: application to sporulation time series ', Pac. Symp. Biocomput., 2000, p. 455-66, available at: http://citeseer.ist.psu.edu/raychaudhuri00principal.html
36. http://csnet.otago.ac.nz/cosc453/student_tutorials/principal_components. pdf
37. ' LIBSVM: a library for support vector machines ', 2001, available at: http://www.csie.ntu.edu.tw/~cjlin/libsvm
38. http://www.csie.ntu.edu.tw/~cjlin/papers/libsvm.pdf
39. Kim, K.H., and Park, J.K.: ' Application of hierarchical neural networks to fault diagnosis of power systems ', Int. J. Elect. Power Energy Syst., 1993, 15, (2), p. 65-70