Vulnerability assessment of a large sized power system using neural network considering various feature extraction methods

Ahmed M.A. Haidar, Azah Mohamed, Aini Hussian

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

Vulnerability assessment of power systems is important so as to determine their ability to continue to provide service in case of any unforeseen catastrophic contingency such as power system component failures, communication system failures, human operator error, and natural calamity. An approach towards the development of on-line power system vulnerability assessment is by means of using an artificial neural network (ANN), which is being used successfully in many areas of power systems because of its ability to handle the fusion of multiple sources of data and information. An important consideration when applying ANN in power system vulnerability assessment is the proper selection and dimension reduction of training features. This paper aims to investigate the effect of using various feature extraction methods on the performance of ANN as well as to evaluate and compare the efficiency of the proposed feature extraction method named as neural network weight extraction. For assessing vulnerability of power systems, a vulnerability index based on power system loss is used and considered as the ANN output. To illustrate the effectiveness of ANN considering various feature extraction methods for vulnerability assessment on a large sized power system, it is verified on the IEEE 300-bus test system.

Original languageEnglish
Pages (from-to)167-176
Number of pages10
JournalJournal of Electrical Engineering and Technology
Volume3
Issue number2
Publication statusPublished - 2008

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Feature extraction
Neural networks
Communication systems
Fusion reactions

Keywords

  • Artificial neural network
  • Contingency analysis
  • Feature extraction
  • Power system loss
  • Vulnerability assessment

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

Cite this

Vulnerability assessment of a large sized power system using neural network considering various feature extraction methods. / Haidar, Ahmed M.A.; Mohamed, Azah; Hussian, Aini.

In: Journal of Electrical Engineering and Technology, Vol. 3, No. 2, 2008, p. 167-176.

Research output: Contribution to journalArticle

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