Wavelet-based feature extraction and selection for classification of power system disturbances using support vector machines

Electric Power Systems Research

Volumn 80, Issue 7, 2010, Pages 743-752

  Erişti, Hüseyin ^a   Uçar, Ayşegül ^b   Demir, Yakup ^b  

^a TUNCELI UNIVERSITY   (Turkey)

^b FIRAT UNIVERSITY   (Turkey)

Author keywords

Classification; Feature selection technique; Power system disturbances; Support vector machines; Wavelet transform

Indexed keywords

CLASSIFICATION ACCURACY; CLASSIFICATION FEATURES; CROSS VALIDATION; FEATURE EXTRACTION TECHNIQUES; FEATURE SELECTION; FEATURE SELECTION TECHNIQUE; INPUT VECTOR; MISCLASSIFICATION ERROR; NEW APPROACHES; PARAMETER SET; PENALTY PARAMETERS; POWER SYSTEM DISTURBANCES; SEQUENTIAL FORWARD SELECTION; SVM CLASSIFIERS; WAVELET-BASED FEATURE;

CLASSIFICATION (OF INFORMATION); CLASSIFIERS; ELECTRIC POWER SYSTEMS; SUPPORT VECTOR MACHINES; TRANSIENTS; VECTORS; WAVELET TRANSFORMS;

FEATURE EXTRACTION;

EID: 77950863635     PISSN: 03787796     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.epsr.2009.09.021     Document Type: Article

References (29)

1. Gaing Z.L. Wavelet-based neural network for power disturbance recognition and classification. IEEE Trans. Power Deliv. 19 4 (2004) 1560-1568
2. Axelberg P.G.V., Gu I.Y., and Bollen M.H.J. Support vector machine for classification of voltage disturbances. IEEE Trans. Power Deliv. 22 3 (2007) 1297-1303
3. Heydt G.T., Fjeldt P.S., Liu C.C., Pierce D., Tu L., and Hensley G. Applications of the windowed FFT to electric power quality assessment. IEEE Trans. Power Deliv. 14 4 (1999) 1411-1416
4. Robertson D.C., Camps O.I., Mayer J.S., and Gish W.B. Wavelets and electromagnetic power system transients. IEEE Trans. Power Deliv. 11 2 (1996) 1050-1058
5. Gaouda A.M., Salama M.M.A., Sultan M.R., and Chikhani A.Y. Power quality detection and classification using wavelet multiresolution signal decomposition. IEEE Trans. Power Deliv. 14 4 (1999) 1469-1476
6. Huang S.J., Hsieh C.T., and Huang C.L. Application of wavelets to classify power system disturbances. Electr. Power Syst. Res. 47 2 (1998) 87-93
7. Lin C.H., and Tsao M.C. Power quality detection with classification enhancible wavelet-probabilistic network in a power system. IEE Proc. Gen. Transm. Distrib. 152 6 (2005) 969-976
8. Cesar D.G., Valdomiro V.G., and Gabriel O.P. Automatic power quality disturbances detection and classification based on discrete wavelet transform and artificial intelligence, TDC06. IEEE/PES Latin Am. (2006) 1-6
9. Meher S.K., Pradhan A.K., and Panda G. An integrated data compression scheme for power quality events using spline wavelet and neural network. Electr. Power Syst. Res. 69 2-3 (2004) 213-220
10. Gaouda A.M., Kanoum S.H., and Salama M.M.A. On-line disturbance classification using nearest neighbor rule. Electr. Power Syst. Res. 57 1 (2001) 1-8
11. Liao Y., and Lee J.B. A fuzzy-expert system for classification power quality disturbances. Electr. Power Energy Syst. 26 3 (2004) 199-205
12. Dash P.K., Nayak M., Senepati M.R., and Lee I.W.C. Mining for similarities in time series data using wavelet-based feature vectors and neural networks. Eng. Appl. Artif. Intell. 20 2 (2007) 185-201
13. Monedero I., Leon C., Ropero J., Garcia A., Elena J.M., and Montano J.C. Classification of electrical disturbances in real time using neural networks. IEEE Trans. Power Deliv. 22 3 (2007) 1288-1296
14. Vapnik V. Statistical Learning Theory (1998), Wiley, New York
15. Janik P., and Lobos T. Automated classification of power quality disturbances using SVM and RBF Networks. IEEE Trans. Power Deliv. 21 3 (2006) 1663-1669
16. He H., and Starzyk J.A. A self-organizing learning array system for power quality classification based on wavelet transform. IEEE Trans. Power Deliv. 21 1 (2006) 286-295
17. Koley C., Purkait P., and Chakravorti S. Wavelet-aided SVM tool for impulse fault identification in transformers. IEEE Trans. Power Deliv. 21 3 (2006) 1283-1290
18. Chen S., and Zhu H.Y. Wavelet transform for processing power quality disturbances. EURASIP J. Adv. Signal Process. 47695 (2007) 20
19. Mallat S.G. A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans. Pattern Anal. Mach. Intell. 11 7 (1989) 674-693
20. Jain A.K., Duin R.P.W., and Mao J. Statistical pattern recognition: a review. IEEE Trans. Pattern Anal. Mach. Intell. 22 1 (2000) 4-37
21. Cortes C., and Vapnik V.N. Support vector networks. Mach. Learn. 20 (1995) 273-297
22. Bertsekas D.P. Nonlinear Programming. second ed. (1999), Athena Scientific
23. Abe S. Support Vector Machines for Pattern Classification (2005), Springer-Verlag
24. Abdel-Galil T.K., Kamel M., Youssef A.M., El-Saadany E.F., and Salama M.M.A. Power quality disturbance classification using inductive inference approach. IEEE Trans. Power Deliv. 19 4 (2004) 1812-1818
25. Joachims T. Making Large-scale SVM Learning Practical, Advances in Kernel Methods-Support Vector Learning (1999), MIT Press, Massachusetts pp. 169-184
26. Franc V., and Hlavac V. Statistical Pattern Recognition Toolbox for Matlab (2004)
27. Gargoom A.M., Ertugrul N., and Soong W.L. A comparative study on effective signal processing tools for optimum feature selection in automatic power quality events clustering. In Industry Appl. Conf. 40th IAS Annual Meeting, vol. 1 (2005) 52-58
28. Dash P.K., and Chilukuri M.V. Hybrid S-transform and Kalman filtering approach for detection and measurement of short duration disturbances in power networks. IEEE Trans. Inst. Meas. 53 2 (2004) 588-596
29. Hu G.S., Zhu F.F., and Ren Z. Power quality disturbance identification using wavelet packet energy entropy and weighted support vector machines. Expert Syst. with Appl. 35 1-2 (2008) 143-149