Open Science Research Excellence
@article{(International Science Index):http://waset.org/publications/11531,
  title    = {Using Support Vector Machine for Prediction Dynamic Voltage Collapse in an Actual Power System},
  author    = {Muhammad Nizam and  Azah Mohamed and  Majid Al-Dabbagh and  Aini Hussain},
  country   = {},
  institution={},
  abstract  = {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.},
    journal   = {International Journal of Electrical, Computer, Energetic, Electronic and Communication Engineering},  volume    = {2},
  number    = {5},
  year      = {2008},
  pages     = {1037 - 1042},
  ee        = {http://waset.org/publications/11531},
  url       = {http://waset.org/Publications?p=17},
  bibsource = {http://waset.org/Publications},
  issn      = {eISSN:1307-6892},
  publisher = {World Academy of Science, Engineering and Technology},
  index     = {International Science Index 17, 2008},
}