Power system on-line static security assessment by using multi-class support vector machines

Journal of Applied Sciences

Volumn 8, Issue 12, 2008, Pages 2226-2233

  Mohammadi, M ^a   Gharehpetian, G B ^a  

^a AMIRKABIR UNIVERSITY OF TECHNOLOGY   (Iran)

Author keywords

Machine learning; Multi class Support Vector Machines (SVM); Power system security

Indexed keywords

CONFIDENCE MEASURE; DATA SELECTION; FEATURE SELECTION ALGORITHM; LARGE-SCALE POWER SYSTEMS; MULTI-CLASS SUPPORT VECTOR MACHINES; POWER SYSTEM SECURITY; STATIC SECURITY ASSESSMENT; SYSTEM SECURITY;

FACE RECOGNITION; LEARNING SYSTEMS;

SUPPORT VECTOR MACHINES;

EID: 67349167145     PISSN: 18125654     EISSN: 18125662     Source Type: Journal    
DOI: 10.3923/jas.2008.2226.2233     Document Type: Article

References (23)

1. Guyon, I. and A. Elisseeff, 2003. An introduction to variable and feature selection. J. Mach. Learning Res., 3: 1157-1182.
2. Hatziargyriou, N., G.C. Contaxis and N.C. Sideris, 1994. A decision tree method for on-line steady state security assessment IEEE Trans. Power Syst., 9: 1052-1061.
3. Hsu, C.W. and C.J. Lin, 2002. A comparison of methods for multi-class support vector machines. IEEE Trans. Neural Networks, 13: 415-425.
4. Huanga, J.J., G.H. Tzeng and C.S. Ong, 2007. Marketing segmentation using support vector clustering. Expert Syst. Appl., 32: 313-317.
5. Jensen, C.A., M.A. EI-Sharkawi and R.J. Marks, 2001. Power system security assessment using neural networks: Feature selection using fisher discrimination. IEEE Trans. Power Syst., 16: 757-763.
6. Kim, H. and C. Singh, 2005. Power system probabilistic security assessment using bayes classifier. Elect. Power Syst. Res., 74: 157-165.
7. Kirschen, D.S., 2002. Power system security. Power Eng., 16: 241-248.
8. Krebel, U., 1999. Pairwise Classification and Support Vector Machines. 1st Edn. MIT Press, Cambridge.
9. Lei, H.S. and V. Govindaraju, 2005. Half-against-half multiclass support vector machines. Proceeding of Multiple Classifier Systems'2005, 13-15 June, California, USA, pp: 156-164.
10. Moulin, L.S., A.P. Alves da Silva, M.A. EI-Sharkawi and R.J. Marks, 2004. Support vector machines for transient stability analysis of large-scale power systems. IEEE Trans. Power Syst., 19: 818-825.
11. Neibur, D. and A. Germond, 1991. Power system static security assessment using kohonen neural network classifier. IEEE Trans. Power Syst., 7: 865-872.
12. Platt J., 1998. How to implement SVMs. IEEE Intell. Syst., 13: 26-28.
13. Plalt, J., N. Cristianini and J. Shawe-Taylor, 1999. Large margin DAGs for multiclass classification. In: Proceeding of Neural Information Processing Systems, 12: 547-553.
14. Scholkopf, B., 1998. SVMs-a practical consequence of learning theory. IEEE Intell. Syst., 13: 18-19.
15. Shanti, K.S., 2008. Artificial neural network using pattern recognition for security assessment and analysis. Neurocomputing, 71: 983-998.
16. Vapnik, V.N., 1998. Statistical Learning Theory. 1st Edn. John Wiley and Sons, New York.
17. Vapnik, V.N., 1999. An overview of statistical learning theory. IEEE Trans. Neural Networks, 10: 988-1000.
18. Wahab, N.I.A., A. Mohamed and A. Hussain, 2007. Transient stability assessment of a power system using PNN and LS-SVM methods. J. Applied Sci., 7: 3208-3216.
19. Wang, J., P. Neskovic and L.N. Cooper, 2007. Selecting data for fast support vector machine training. Stud. Comput. Intell., 35: 61-84.
20. Wehenkel, L.A., 1998. Automatic learning techniques in power systems. 1st Edn. Kluwer Academic, Boston.
21. Yang, J. and V. Honavar, 1998. Feature subset selection using agenetic algorithm. IEEE Intell. Syst., 13: 44-49.
22. Yeung, D.S., D. Wang and E.C. Tsang, 2007. Weighted mahalanobis distance kernels for support vector machines. IEEE Trans. Neural Networks, 18: 1453-1462.
23. Zhou, Q., J. Davidson and A.A. Fouad, 1994. Application of artificial neural networks in power system security and vulnerability assessment. IEEE Trans. Power Syst., 9: 525-532.