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Journal of Applied Sciences
  Year: 2007 | Volume: 7 | Issue: 21 | Page No.: 3208-3216
DOI: 10.3923/jas.2007.3208.3216
 
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Transient Stability Assessment of a Power System Using PNN and LS-SVM Methods

Noor Izzri Abdul Wahab, Azah Mohamed and Aini Hussain

Abstract:
This 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.
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How to cite this article:

Noor Izzri Abdul Wahab, Azah Mohamed and Aini Hussain, 2007. Transient Stability Assessment of a Power System Using PNN and LS-SVM Methods. Journal of Applied Sciences, 7: 3208-3216.

DOI: 10.3923/jas.2007.3208.3216

URL: https://scialert.net/abstract/?doi=jas.2007.3208.3216

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