Transient stability assessment of a power system using PNN and LS-SVM methods

Noor Izzri Abdul Wahab, Azah Mohamed, Aini Hussain

Research output: Contribution to journalArticle

18 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)3208-3216
Number of pages9
JournalJournal of Applied Sciences
Volume7
Issue number21
Publication statusPublished - 1 Nov 2007

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Support vector machines
Neural networks
Multilayer neural networks
Classifiers
Rotors

Keywords

  • Artificial neural network
  • Least squares support vector machines
  • Probabilistic neural network
  • Transient stability assessment

ASJC Scopus subject areas

  • General

Cite this

Transient stability assessment of a power system using PNN and LS-SVM methods. / Abdul Wahab, Noor Izzri; Mohamed, Azah; Hussain, Aini.

In: Journal of Applied Sciences, Vol. 7, No. 21, 01.11.2007, p. 3208-3216.

Research output: Contribution to journalArticle

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