Application of core vector machines for on-line voltage security assessment using a decision-tree-based feature selection algorithm

IET Generation, Transmission and Distribution

Volumn 3, Issue 8, 2009, Pages 701-712

  Mohammadi, M ^a   Gharehpetian, G B ^a  

^a AMIRKABIR UNIVERSITY OF TECHNOLOGY   (Iran)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL NEURAL NETWORKS; BUS POWER SYSTEM; DECISION-TREE; FEATURE SELECTION; FEATURE SELECTION ALGORITHM; LINE VOLTAGE; MACHINE LEARNING METHODS; NEW ENGLAND; POWER SYSTEMS; SECURITY ASSESSMENT; SIMULATION RESULT; SMALL TRAINING; SUPPORT VECTOR; SYSTEM SECURITY; VECTOR MACHINES;

DECISION TREES; ELECTRIC POTENTIAL; ELECTRIC POWER TRANSMISSION NETWORKS; NEURAL NETWORKS; POWER TRANSMISSION; SUPPORT VECTOR MACHINES; VECTORS;

ALGORITHMS;

EID: 68649089765     PISSN: 17518687     EISSN: None     Source Type: Journal    
DOI: 10.1049/iet-gtd.2008.0374     Document Type: Article

References (36)

1. Gao, B., Morison, G.K., and Kundur, P.: ' Voltage stability evaluation using modal analysis ', IEEE Trans. Power Syst., 1992, 7, (4), p. 1529-1542 10.1109/59.207377 0885-8950 (Pubitemid 23568721)
2. Julian, B., Tomas, G., and Luis, P.F.: ' Estimating the loading limit margin taking into account voltage collapse Areas ', IEEE Trans. Power Syst., 1995, 10, (4), p. 1952-1962 0885-8950
3. 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 Trans. Power Syst., 1993, 8, (3), p. 920-936 0885-8950
4. Chang, C.S., and Huang, J.S.: ' Worst case identification of reactive power margin and local weakness of power systems ', Electr. Power Syst. Res., 1998, 44, (2), p. 77-83 0378-7796
5. Berizzi, A., Finazzi, P., Dosi, D., and Corsi, S.: ' First and second order methods for voltage collapse assessment and security enhancement ', IEEE Trans. Power Syst., 1998, 13, (2), p. 543-551 10.1109/59.667380 0885-8950
6. 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 Trans. Power Syst., 2000, 15, (1), p. 81-87 0885-8950
7. Wu, G.Y., Chung, C.Y., Wong, K.P., and Yu, C.W.: ' Voltage stability constrained optimal dispatch in deregulated power systems ', IET Gener. Transm. Distrib., 2007, 1, (5), p. 761-768
8. Van Cutsem, T., Moisse, C., and Mailhot, R.: ' Determination of secure operating limits with respect of voltage collapse ', IEEE Trans. Power Syst., 1999, 14, (1), p. 327-335 10.1109/59.744551 0885-8950
9. Jeyasurya, B.: ' Artificial neural networks for power system steady-state voltage instability evaluation ', Electr. Power Syst. Res., 1994, 29, (2), p. 85-90 0378-7796
10. Gu, X., and Canizares, C.A.: ' Fast prediction of loadability margins using neural networks to approximate security boundaries of power systems ', IET Gener. Transm. Distrib., 2007, 1, (3), p. 466-475 10.1049/iet-gtd:20060265 (Pubitemid 46718684)
11. El-Keib, A.A., and Ma, X.: ' Application of artificial neural network in voltage stability assessment ', IEEE Trans. Power Syst., 1995, 10, (4), p. 1890-1896 0885-8950
12. Song, Y.H., Wan, H.B., and Johns, A.T.: ' Kohonen neural network based approach to voltage weak buses/areas identification ', IEE Proc., Gener. Transm. Distrib., 1997, 144, (4), p. 340-344 1350-2360
13. 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 Trans. Power Syst., 1997, 13, (4), p. 1259-1264 10.1109/59.736256 0885-8950
14. Pandit, M., Srivastava, L., and Sharma, J.: ' Contingency ranking for voltage collapse using parallel self-organizing hierarchical neural network ', Int. J. Electr. Power Energy Syst., 2001, 23, (5), p. 369-379 0142-0615
15. Srivastava, L., Singh, S.N., and Sharma, J.: ' A hybrid neural network model for fast voltage contingency screening and ranking ', Int. J. Electr. Power Energy Syst., 2000, 22, (1), p. 35-42 0142-0615
16. Wan, H.B., and Song, Y.H.: ' Hybrid supervised and unsupervised neural network approach to voltage stability analysis ', Electr. Power Syst. Res., 1998, 47, (2), p. 115-122 0378-7796
17. Van Cutsem, T., Wehenkel, L., Pavella, M., Heilbronn, B., and Goubin, M.: ' Decision tree approaches to voltage security assessment ', IEE Proc. - C, Gener. Transm. Distrib., 1993, 140, (3), p. 189-198
18. Kim, H., and Singh, C.: ' Power system probabilistic security assessment using Bayes classifier ', Electr. Power Syst. Res., 2005, 74, (1), p. 157-165 10.1016/j.epsr.2004.10.004 0378-7796 (Pubitemid 40245131)
19. Moulin, L.S., Alves da Silva, A.P., 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
20. 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 ', Int. J. Eng. Intell. Syst. Electr. Eng. Commun., 2001, 9, (4), p. 205-212 0969-1170
21. Mohammadi, M., and Gharehpetian, G.B.: ' Power system on-line static security assessment by using multi-class support vector machines ', J. Appl. Sci. (JAS), 2008, 8, (12), p. 2226-2233
22. Burges, C.J.C.: ' A tutorial on support vector machines for pattern recognition ', Data Min. Knowl. Discov., 1998, 2, (2), p. 121-167 10.1023/A:1009715923555 1384-5810 (Pubitemid 128126769)
23. Tsang, I., Kwok, J., and Cheung, P.: ' Core vector machines: fast SVM training on very large data sets ', J. Mach. Learn. Res., 2005, 6, (1), p. 363-392 1532-4435
24. Tsang, I., Kocsor, A., and Kwok, J.: ' Large-scale maximum margin discriminate analysis using core vector machines ', IEEE Trans. Neural Netw., 2008, 19, (4), p. 610-624 1045-9227
25. Tsang, I., Kwok, J., and Zurada, J.: ' Generalized core vector machines ', IEEE Trans. Neural Netw., 2006, 17, (5), p. 1126-1140 10.1109/TNN.2006.878123 1045-9227 (Pubitemid 46445172)
26. Tsang, I., Kwok, J., and Lai, K.: ' Core vector regression for very large regression problems ', Proc. Twenty-Second Int. Conf. Machine Learning, Bonn, Germany, August, 2005, p. 913-920 (Pubitemid 43183422)
27. www.cse.ust.hk/ivor/cvm.html,accessed March 5, 2008
28. 3 and related problems ', SIAM J. Comput., 1983, 12, (4), p. 759-776 0097-5397
29. Kessel, P., and Glavitsch, H.: ' Estimating the voltage stability of a power system ', IEEE Trans. Power Deliv., 1986, 1, (3), p. 346-354 0885-8977
30. 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 Trans. Power Syst., 1994, 9, (1), p. 341-351 0885-8950
31. Guyon, I., and Elisseeff, A.: ' An introduction to variable and feature selection ', J. Mach. Learn. Res., 2003, 3, (7), p. 1157-1182 10.1162/153244303322753616 1532-4435
32. Jensen, C.A., El-Sharkawi, M.A., and Marks, R.J.: ' Power system security assessment using neural networks: feature selection using fisher discrimination ', IEEE Trans. Power Syst., 2001, 16, (4), p. 757-763 0885-8950
33. Yang, J., and Honavar, V.: ' Feature subset selection using a genetic algorithm ', IEEE Intell. Syst., 1998, 13, (2), p. 44-49 1541-1672
34. Breiman, L., Friedman, J.H., Olshen, R., and Stone, C.J.: ' Classification and regression tree ', (Wadsworth & Brooks/Cole Advanced Books & Software, Pacific California, 1984)
35. Sauer and, P.W., and Pai, M.A.: ' Power system dynamics and stability ', (Prentice-Hall, Upper Saddle River, NJ, USA, 1998)
36. Lei, H.S., and Govindaraju, V.: ' Half-against-half multi-class support vector machines ', Proc. Multiple Classifier Systems′ 2005, California, USA, June, 2005, p. 156-164