Journal of Applied Sciences1812-56541812-5662Asian Network for Scientific Information10.3923/jas.2007.3208.3216Abdul WahabNoor Izzri MohamedAzah HussainAini 122007721This study presents transient stability assessment of electrical power system using two artificial neural network techniques which are Probabilistic Neural Network (PNN) and Least Squares Support Vector Machine (LS-SVM). Transient stability of a power system is first determined based on the generator relative rotor angles obtained from time domain simulation outputs. Simulations were carried out on the IEEE 9-bus test system considering three phase faults on the system. The data collected from the time domain simulations are then used as inputs to the PNN and LS-SVM. Both networks are used as a classifier to determine whether the power system is stable or unstable. To verify the effectiveness of the proposed PNN and LS-SVM methods, they are compared with the Multi Layer Perceptron Neural Network (MLPNN). Results show that the PNN gives faster and more accurate transient stability assessment compared to the LS-SVM network and MLPNN in terms of classification results.]]>Anderson, P.M. and A.A. Fouad,20022nd Edn.,pp: 672pp: 672Bettiol, A.L., A. Souza, J.L. Todesco and J.R. Tesch Jr.,2003366Boudour, M. and A. Hellal,200518673683Burrascano, P.,19912458461Chen, S., C.F.N. Cowan and P.M. Grant,19912302309Krishna, S. and K.R. Padiyar,20002000627632Milano, F.,20052011991206Milano, F.,20072007Moulin, L.S., A.P. Alves da Silva, M.A. El-Sharkawi and R.J. Marks,200419818825Pothisarn, C. and S. Jiriwibhakorn,20032731736Riedmiller, M. and H. Braun,19931586591Sanyal, K.K.,20042004Sawhney, H. and B. Jeyasurya,20042004pp: 7680Silveira, M.C.G., A.D.P. Lotufo and C.R. Minussi,2003377Specht, D.F.,19921525532Suykens, J.A.K. and J. Vandewalle,19999293300Suykens, J.A.K., T. Van Gestel, J. De Brabanter, B. De Moor and J. Vandewalle,2002Wang, X., S. Wu, Q. Li and X. Wang,20052005pp: 356363