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1. L. Srivastava, S. N. Singh, and J. Sharma, “Estimation of loadability margin using parallel self-organizing hierarchical neural network,” Computers and Electrical Engineering, vol. 26, no. 2, pp. 151–167, 2000. View at Publisher · View at Google Scholar
2. P. Kundur, Power System Stability and Control, McGraw-Hill, New York, NY, USA, 1994.
3. V. Ajjarapu and C. Christy, “The continuation power flow: a tool for steady state voltage stability analysis,” IEEE Transactions on Power Systems, vol. 7, no. 1, pp. 416–423, 1992. View at Publisher · View at Google Scholar
4. C. A. Canizares and F. L. Alvarado, “Point of collapse and continuation methods for large AC/DC systems,” IEEE Transactions on Power Systems, vol. 8, no. 1, pp. 1–8, 1993. View at Publisher · View at Google Scholar
5. C. A. Canizares, “On bifurcations, voltage collapse and load modeling,” IEEE Transactions on Power Systems, vol. 10, no. 1, pp. 512–522, 1995. View at Publisher · View at Google Scholar
6. T. J. Overbye and C. L. DeMarco, “Improved techniques for power system voltage stability assessment using energy methods,” IEEE Transactions on Power Systems, vol. 6, no. 4, pp. 1446–1452, 1991. View at Publisher · View at Google Scholar
7. P. A. Löf, T. Smed, G. Andersson, and D. J. Hill, “Fast calculation of a voltage stability index,” IEEE Transactions on Power Systems, vol. 7, no. 1, pp. 54–64, 1992. View at Publisher · View at Google Scholar
8. B. Jeyasurya, “Artificial neural networks for power system steady-state voltage instability evaluation,” Electric Power Systems Research, vol. 29, no. 2, pp. 85–90, 1994.
9. D. Salatino, R. Sbrizzai, M. Trovato, and M. La Scala, “Online voltage stability assessment of load centers by using neural networks,” Electric Power Systems Research, vol. 32, no. 3, pp. 165–173, 1995.
10. A. A. El-Keib and X. Ma, “Application of artificial neural networks in voltage stability assessment,” IEEE Transactions on Power Systems, vol. 10, no. 4, pp. 1890–1896, 1995. View at Publisher · View at Google Scholar
11. H. P. Schmidt, “Application of artificial neural networks to the dynamic analysis of the voltage stability problem,” IEE Proceedings: Generation, Transmission & Distribution, vol. 144, pp. 371–376, 1997.
12. A. Mohamed and G. B. Jasmon, “Neural network approach to dynamic voltage stability prediction,” Electric Machines and Power Systems, vol. 25, no. 5, pp. 509–523, 1996.
13. V. R. Dinavahi and S. C. Srivastava, “ANN based voltage stability margin prediction,” in Proceedings of the IEEE Power Engineering Society Summer Meeting, vol. 2, pp. 1275–1279, Vancouver, Canada, July 2001.
14. S. Kamalasadan, D. Thukaram, and A. K. Srivastava, “A new intelligent algorithm for online voltage stability assessment and monitoring,” International Journal of Electrical Power & Energy Systems, vol. 31, no. 2-3, pp. 100–110, 2009. View at Publisher · View at Google Scholar
15. S. Chauhan and M. P. Dave, “Kohonen neural network classifier for voltage collapse margin estimation,” Electric Machines and Power Systems, vol. 25, no. 6, pp. 607–619, 1996. View at Scopus
16. Y. H. Song, H. B. Wan, and A. T. Johns, “Kohonen neural network based approach to voltage weak buses/areas identification,” IEE Proceedings: Generation, Transmission & Distribution, vol. 144, pp. 340–344, 1997.
17. W. Nakawiro and I. Erlich, “Online voltage stability monitoring using artificial neural network,” in Proceedings of the 3rd International Conference on Deregulation and Restructuring and Power Technologies (DRPT '08), pp. 941–947, Nanjing, China, April 2008. View at Publisher · View at Google Scholar · View at Scopus
18. S. N. Pandey, S. Tapaswi, and L. Srivastava, “Integrated evolutionary neural network approach with distributed computing for congestion management,” Applied Soft Computing Journal, vol. 10, no. 1, pp. 251–260, 2010. View at Publisher · View at Google Scholar · View at Scopus
19. P. P. Palmes, T. Hayasaka, and S. Usui, “Mutation-based genetic neural network,” IEEE Transactions on Neural Networks, vol. 16, no. 3, pp. 587–600, 2005. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
20. S. Rajasekaran and G. A. V. Pai, Neural Networks, Fuzzy Logic and Genetic Algorithms—Synthesis and Applications, Prentice-Hall Press, New Delhi, India, 2006.
21. P. Ramasubramanian and A. Kannan, “A genetic-algorithm based neural network short-term forecasting framework for database intrusion prediction system,” Soft Computing, vol. 10, no. 8, pp. 699–714, 2006. View at Publisher · View at Google Scholar · View at Scopus
22. S. K. Oh and W. Pedrycz, “Multi-layer self-organizing polynomial neural networks and their development with the use of genetic algorithms,” Journal of the Franklin Institute, vol. 343, no. 2, pp. 125–136, 2006. View at Publisher · View at Google Scholar · View at Scopus
23. S. K. Oh, W. Pedrycz, and S. B. Roh, “Genetically optimized hybrid fuzzy set-based polynomial neural networks,” Journal of the Franklin Institute, vol. 348, pp. 415–425, 2011. View at Publisher · View at Google Scholar · View at Scopus
24. Y.-Y. Hsu, C.-R. Chen, and C.-C. Su, “Analysis of electromechanical modes using an artificial neural network,” IEE Proceedings: Generation, Transmission & Distribution, vol. 141, no. 3, pp. 198–204, 1994. View at Publisher · View at Google Scholar
25. S. Sharma and L. Srivastava, “Prediction of transmission line overloading using intelligent technique,” Applied Soft Computing Journal, vol. 8, no. 1, pp. 626–633, 2008. View at Publisher · View at Google Scholar · View at Scopus
26. L. L. Freris and A. M. Sasson, “Investigation of the load-flow problem,” Proceedings of the Institution of Electrical Engineers, vol. 115, no. 10, pp. 1459–1470, 1968. View at Publisher · View at Google Scholar
27. C. A. Canizares and F. L. Alvarado, “UWPFLOW Program,” University of Waterloo, 2000, http://www.power.uwaterloo.ca/.