Comparing least squares support vector machine and probabilistic neural network in transient stability assessment of a large-scale power system

Noor Izzri Abdul Wahab, Azah Mohamed

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Citations (Scopus)

Abstract

This paper presents transient stability assessment of a large practical power system using two artificial neural network techniques which are the probabilistic neural network (PNN) and the least squares support vector machine (LS-SVM). The large power system is divided into five smaller areas depending on the coherency of the areas when subjected to disturbances. This is to reduce the number of data sets collected for the respective areas. Transient stability of the power system is first determined based on the generator relative rotor angles obtained from time domain simulation outputs. Simulations were carried out on the test system considering three phase faults at different loading conditions. The data collected from the time domain simulations are then used as inputs to the PNN and LS-SVM. Both networks are used as classifiers to determine whether the power system is stable or unstable. Classification results show that the PNN gives faster and more accurate transient stability assessment compared to the LS-SVM.

Original languageEnglish
Title of host publicationPECon 2008 - 2008 IEEE 2nd International Power and Energy Conference
Pages485-489
Number of pages5
DOIs
Publication statusPublished - 2008
Event2008 IEEE 2nd International Power and Energy Conference, PECon 2008 - Johor Baharu
Duration: 1 Dec 20083 Dec 2008

Other

Other2008 IEEE 2nd International Power and Energy Conference, PECon 2008
CityJohor Baharu
Period1/12/083/12/08

Fingerprint

Support vector machines
Neural networks
Classifiers
Rotors

Keywords

  • Least squares support vector machine
  • Probabilistic neural network
  • Transient stability assessment

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering

Cite this

Wahab, N. I. A., & Mohamed, A. (2008). Comparing least squares support vector machine and probabilistic neural network in transient stability assessment of a large-scale power system. In PECon 2008 - 2008 IEEE 2nd International Power and Energy Conference (pp. 485-489). [4762523] https://doi.org/10.1109/PECON.2008.4762523

Comparing least squares support vector machine and probabilistic neural network in transient stability assessment of a large-scale power system. / Wahab, Noor Izzri Abdul; Mohamed, Azah.

PECon 2008 - 2008 IEEE 2nd International Power and Energy Conference. 2008. p. 485-489 4762523.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Wahab, NIA & Mohamed, A 2008, Comparing least squares support vector machine and probabilistic neural network in transient stability assessment of a large-scale power system. in PECon 2008 - 2008 IEEE 2nd International Power and Energy Conference., 4762523, pp. 485-489, 2008 IEEE 2nd International Power and Energy Conference, PECon 2008, Johor Baharu, 1/12/08. https://doi.org/10.1109/PECON.2008.4762523
Wahab, Noor Izzri Abdul ; Mohamed, Azah. / Comparing least squares support vector machine and probabilistic neural network in transient stability assessment of a large-scale power system. PECon 2008 - 2008 IEEE 2nd International Power and Energy Conference. 2008. pp. 485-489
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