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Using Support Vector Machine for Prediction Dynamic Voltage Collapse in an Actual Power System
This paper presents dynamic voltage collapse prediction on an actual power system using support vector machines. Dynamic voltage collapse prediction is first determined based on the PTSI calculated from information in dynamic simulation output. Simulations were carried out on a practical 87 bus test system by considering load increase as the contingency. The data collected from the time domain simulation is then used as input to the SVM in which support vector regression is used as a predictor to determine the dynamic voltage collapse indices of the power system. To reduce training time and improve accuracy of the SVM, the Kernel function type and Kernel parameter are considered. To verify the effectiveness of the proposed SVM method, its performance is compared with the multi layer perceptron neural network (MLPNN). Studies show that the SVM gives faster and more accurate results for dynamic voltage collapse prediction compared with the MLPNN.
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[1] M. Hasani and M. Parniani, "Method of Combined Static and Dynamic Analysis of Voltage collapse in Voltage Stability Assessment," IEEE/PES Transmission and Distribution Conference and Exhibition, China, 2005.
[2] V. Balamourougan,T.S. Sidhu and M.S. Sachdev, "Technique For Online Prediction of Voltage Collapse," IEE Proc-Generation, Transmission, Distribution, vol.151, no 4: pp. 453-460, 2004.
[3] M. Nizam, A. Mohamed and A. Hussain, "Performance Evaluation of Voltage Stability Indices for Dynamic Voltage Collapse Prediction", Journal of Applied Science, vol.6, no.5, pp1104-1113, 2006.
[4] A.W. N. Izzri, A. Mohamed and I. Yahya, "A New Method Of Transient Stability Assessment In Power System Using LS-SVM", IEEE Student Conference on Research and Development, Malaysia, 11-12 December 2007.
[5] I. Musirin and T.K.A Rahman, 2004, "Voltage stability based weak area clustering technique in power system", National Power & Energy Conference (PECon 2004), Kuala Lumpur, pp.235-240, 2004.
[6] G. Celli, M. Loddo and F. Pilo, "Voltage Collapse Prediction with Locally Recurrent Neural Network", International Conference of, pp. 1130-1135, 2002.
[7] S. Krishna and K.R. Padiyar, "Transient Stability Assessment Using Artificial Neural Networks" Proceedings of IEEE International Conference on Industrial Technology, vol.1, pp.627-632, 2000.
[8] B. Ravikumar, D. Thukaram and H. P. Khincha, "Application Of Support Vector Machines For Fault Diagnosis In Power Transmission System," Generation, Transmission & Distribution, IET, pp. 119-130, 2008.
[9] K. Pelckmans, J. A. K. Suykens, T. Van Gestel, J. De Brabanter, L. Lukas, B. Hamers, B. De Moor and J. Vandewalle, "LS-SVMlab Toolbox User-s Guide," ESAT-SCD-SISTA Technical Report 02-145, Katholieke Universiteit Leuven, 2003.
[10] A.J. Smola and B. Scholkopf, "On a Kernel-Based Method for Pattern Recognition, Regression, Approximation And Operator Inversion," Algorithmica, vol.22, pp.211-231, 1998.
[11] D. Z. Gang, N. Lin and Z. J. Guo, "Application of Support Vector Regression Model Based on Phase Space Reconstruction to Power SystemWide-area Stability Prediction", International Power and Energy Conference (IPEC), Singapore, pp. 1880-1885, 2007.
[12] S. R. Gunn, "Support Vector Machines for Classification and Regression," Technical Report, Image Speech and Intelligent Systems Research Group, University of Southampton, 1997.
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