On-line voltage security assessment of power systems using core vector machines

Engineering Applications of Artificial Intelligence

Volumn 22, Issue 4-5, 2009, Pages 695-701

  Mohammadi, M ^a   Gharehpetian, G B ^a  

^a AMIRKABIR UNIVERSITY OF TECHNOLOGY   (Iran)

Author keywords

Core vector machines (CVM); Data selection; Feature selection; Machine learning; Minimum enclosing ball; Multi class classification; Voltage security assessment

Indexed keywords

CORE VECTOR MACHINES (CVM); DATA SELECTION; FEATURE SELECTION; MACHINE LEARNING; MINIMUM ENCLOSING BALL; MULTI-CLASS CLASSIFICATION; VOLTAGE SECURITY ASSESSMENT;

ALGORITHMS; DATA REDUCTION; DECISION TREES; ELECTRIC POWER TRANSMISSION NETWORKS; NEURAL NETWORKS; POWER TRANSMISSION; ROBOT LEARNING; SUPPORT VECTOR MACHINES;

VECTORS;

EID: 67349086893     PISSN: 09521976     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.engappai.2009.02.001     Document Type: Article

References (35)

1. Berizzi A., Finazzi P., Dosi D., and Corsi S. First and second order methods for voltage collapse assessment and security enhancement. IEEE Transactions on Power Systems 13 2 (1998) 543-551
2. Berizzi A., Zeng Y.G., Marannino P., Vacarrini A., and Scarpellini A. A second order method for contingency severity assessment with respect to voltage collapse. IEEE Transactions on Power Systems 15 1 (2000) 81-87
3. Breiman L., Friedman J.H., Olshen R., and Stone C.J. Classification and Regression Tree, Pacific California (1984), Wadsworth and Brooks/Cole Advanced Books and Software, Belmont, CA
4. Burges C.J.C. A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 2 2 (1998) 121-167
5. Chang C.S., and Huang J.S. Worst case identification of reactive power margin and local weakness of power systems. Electric Power System Research 44 2 (1998) 77-83
6. El-Keib A.A., and Ma X. Application of artificial neural network in voltage stability assessment. IEEE Transactions on Power Systems 10 4 (1995) 1890-1896
7. Flatabo N., Fosso O.B., Ognedal R., Carlsen T., and Heggland K.R. A method for calculation of margins to voltage instability applied on the Norwegian system for maintaining required security level. IEEE Transactions on Power Systems 8 3 (1993) 920-936
8. Gao B., Morison G.K., and Kundur P. Voltage stability evaluation using modal analysis. IEEE Transactions on Power Systems 7 4 (1992) 1529-1542
9. Greene S., Dobson I., and Alyarado F.L. Contingency ranking for voltage collapse via sensitivities from a single nose curve. IEEE Transactions on Power Systems 14 1 (1999) 232-240
10. Guyon I., and Elisseeff A. An introduction to variable and feature selection. Journal of Machine Learning Research 3 7 (2003) 1157-1182
11. Jensen C.A., El-Sharkawi M.A., and Marks R.J. Power system security assessment using neural networks: feature selection using fisher discrimination. IEEE Transactions on Power Systems 16 4 (2001) 757-763
12. Jeyasurya B. Artificial neural networks for power system steady-state voltage instability evaluation. Electric Power System Research 29 2 (1994) 85-90
13. Julian B., Tomas G., and Luis P.F. Estimating the loading limit margin taking into account voltage collapse areas. IEEE Transactions on Power Systems 10 4 (1995) 1952-1962
14. Kessel P., and Glavitsch H. Estimating the voltage stability of a power system. IEEE Transactions on Power Delivery 1 3 (1986) 346-354
15. Kim H., and Singh C. Power system probabilistic security assessment using Bayes classifier. Electric Power Systems Research 74 1 (2005) 157-165
16. Lei, H.S., Govindaraju, V., 2005. Half-against-half multi-class support vector machines. In: Proceedings of the Multiple Classifier Systems'2005, CA, USA, June, pp. 156-164.
17. Lo K.L., Peng L.J., Macqueen J.F., Ekwue A.O., and Cheng D.T.Y. Fast real power contingency ranking using a counter-propagation network. IEEE Transactions on Power Systems 13 4 (1997) 1259-1264
18. Marks R.J., Moulin L.S., Alves da Silva A.P., and El-Sharkawi M.A. Neural networks and support vector machines applied to power systems transient stability analysis. International Journal of Engineering Intelligent Systems for Electrical Engineering and Communications 9 4 (2001) 205-212
19. Megiddo N. Linear-time algorithms for linear programming in R3 and related problems. SIAM Journal on Computing 12 4 (1983) 759-776
20. Mohammadi M., and Gharehpetian G.B. Power system on-line static security assessment by using multi-class support vector machines. Journal of Applied Sciences 8 12 (2008) 2226-2233
21. Moulin L.S., Alves da Silva A.P., El-Sharkawi M.A., and Marks II R.J. Support vector machines for transient stability analysis of large-scale power systems. IEEE Transactions on Power Systems 19 2 (2004) 818-825
22. Pandit M., Srivastava L., and Sharma J. Contingency ranking for voltage collapse using parallel self-organizing hierarchical neural network. International Journal of Electrical Power and Energy Systems 23 5 (2001) 369-379
23. Salatino D., Sibrizzai R., Trovato M., and La Scala M. Online voltage stability assessment of load centers by using neural networks. Electric Power Systems Research 32 3 (1995) 165-173
24. Sauer P.W., and Pai M.A. Power System Dynamics and Stability (1998), Prentice-Hall, Upper Saddle River, NJ, USA
25. Song Y.H., Wan H.B., and Johns A.T. Kohonen neural network based approach to voltage weak buses/areas identification. IEE Proceedings on Generation, Transmission and Distribution 144 (1997) 340-344
26. Srivastava L., Singh S.N., and Sharma J. A hybrid neural network model for fast voltage contingency screening and ranking. International Journal of Electrical Power and Energy Systems 22 1 (2000) 35-42
27. Tsang I., Kocsor A., and Kwok J. Large-scale maximum margin discriminant analysis using core vector machines. IEEE Transactions on Neural Networks 19 4 (2008) 610-624
28. Tsang, I., Kwok, J., Lai, K., 2005a. Core vector regression for very large regression problems. In: Proceedings of the Twenty-Second International Conference on Machine Learning, Bonn, Germany, August, pp. 913-920.
29. Tsang I., Kwok J., and Cheung P. Core vector machines: fast SVM training on very large data sets. Journal of Machine Learning Research 6 1 (2005) 363-392
30. Tsang I., Kwok J., and Zurada J. Generalized core vector machines. IEEE Transactions on Neural Networks 17 5 (2006) 1126-1140
31. Tuan T.Q., Fandino J., Hadjsaid N., Sabonnadiere J.C., and Vu H. Emergency load shedding to avoid risks of voltage instability using indicators. IEEE Transactions on Power Systems 9 1 (1994) 341-351
32. Van Cutsem T., Moisse C., and Mailhot R. Determination of secure operating limits with respect of voltage collapse. IEEE Transactions on Power Systems 14 1 (1999) 327-335
33. Van Cutsem T., Wehenkel L., Pavella M., Heilbronn B., and Goubin M. Decision tree approaches to voltage security assessment. IEE Proceedings-Part C 140 3 (1993) 189-198
34. Wan H.B., and Song Y.H. Hybrid supervised and unsupervised neural network approach to voltage stability analysis. Electric Power Systems Research 47 2 (1998) 115-122
35. Yang J., and Honavar V. Feature subset selection using a genetic algorithm. IEEE Intelligent Systems 13 2 (1998) 44-49