Assessment of the risk of voltage collapse in a power system using intelligent techniques

Research output: Contribution to journalArticle

Abstract

This paper describes the implementation of a fast and easy-to-use, intelligence-based algorithm to assess the risk of voltage collapse when risk is defined as the product of the event likelihood and a severity function. In the event likelihood, the effect of weather is taken into account; the failure rate of each transmission line under different weather conditions is calculated using real historical outage data. A new severity function model that utilises the voltage collapse prediction index is proposed in this paper. Two intelligent techniques, i.e., support vector machines and a generalised regression neural network are studied, and their performances are evaluated using mean absolute and mean square error. The proposed methodology has been applied in a real power system network. Simulation results show that a generalized regression neural network provides the lowest mean absolute and mean square error.

Original languageEnglish
Pages (from-to)1167-1179
Number of pages13
JournalAustralian Journal of Basic and Applied Sciences
Volume5
Issue number6
Publication statusPublished - Jun 2011

Fingerprint

Intelligent systems
Mean square error
Neural networks
Electric potential
Outages
Support vector machines
Electric lines

Keywords

  • GRNN
  • Risk
  • SVM
  • Voltage collapse

ASJC Scopus subject areas

  • General

Cite this

Assessment of the risk of voltage collapse in a power system using intelligent techniques. / Marsadek, Marayati; Mohamed, Azah; Mohd Nopiah, Zulkifli.

In: Australian Journal of Basic and Applied Sciences, Vol. 5, No. 6, 06.2011, p. 1167-1179.

Research output: Contribution to journalArticle

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