Transient stability assessment of a power system using PNN and LS-SVM methods. | [Journal of Applied Sciences • 2007]

Author(s):

1. Noor Izzri Abdul-Wahab: Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan, Malaysia

2. Azah Mohamed: Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan, Malaysia

3. Aini Hussain: Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan, Malaysia

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.

Page(s): 3208-3216

DOI: DOI not available

Published: Journal: Journal of Applied Sciences, Volume: 7, Issue: 21, Year: 2007

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