Vulnerability assessment of a large sized power system using radial basis function neural network

Ahmed M A Haidar, Azah Mohamed, Aini Hussain

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

2 Citations (Scopus)

Abstract

Vulnerability assessment of a power system has been of great concern due to the continual blackouts in recent years which indicate that a power system today is too vulnerable to withstand an unforeseen catastrophic contingency. This paper presents a new approach to assess vulnerability of a power system based on radial basis function neural network. A new feature extraction method named as the neural network weight extraction is also proposed to reduce the number of input features to the neural network. The effectiveness of the proposed approach has been demonstrated on a large sized IEEE 300-bus system. Test results prove that the radial basis function neural network accurately predicts the vulnerability of the power system.

Original languageEnglish
Title of host publication2007 5th Student Conference on Research and Development, SCORED
DOIs
Publication statusPublished - 2007
Event2007 5th Student Conference on Research and Development, SCORED - Selangor
Duration: 11 Dec 200712 Dec 2007

Other

Other2007 5th Student Conference on Research and Development, SCORED
CitySelangor
Period11/12/0712/12/07

Fingerprint

neural network
vulnerability
contingency
Neural networks
Radial basis function
Vulnerability
Power system

Keywords

  • Radial basis function neural network
  • Vulnerability assessment
  • Vulnerability index

ASJC Scopus subject areas

  • Education
  • Management Science and Operations Research

Cite this

Vulnerability assessment of a large sized power system using radial basis function neural network. / Haidar, Ahmed M A; Mohamed, Azah; Hussain, Aini.

2007 5th Student Conference on Research and Development, SCORED. 2007. 4451385.

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

Haidar, AMA, Mohamed, A & Hussain, A 2007, Vulnerability assessment of a large sized power system using radial basis function neural network. in 2007 5th Student Conference on Research and Development, SCORED., 4451385, 2007 5th Student Conference on Research and Development, SCORED, Selangor, 11/12/07. https://doi.org/10.1109/SCORED.2007.4451385
Haidar, Ahmed M A ; Mohamed, Azah ; Hussain, Aini. / Vulnerability assessment of a large sized power system using radial basis function neural network. 2007 5th Student Conference on Research and Development, SCORED. 2007.
@inproceedings{13d4d6813240453eade69a48b7770226,
title = "Vulnerability assessment of a large sized power system using radial basis function neural network",
abstract = "Vulnerability assessment of a power system has been of great concern due to the continual blackouts in recent years which indicate that a power system today is too vulnerable to withstand an unforeseen catastrophic contingency. This paper presents a new approach to assess vulnerability of a power system based on radial basis function neural network. A new feature extraction method named as the neural network weight extraction is also proposed to reduce the number of input features to the neural network. The effectiveness of the proposed approach has been demonstrated on a large sized IEEE 300-bus system. Test results prove that the radial basis function neural network accurately predicts the vulnerability of the power system.",
keywords = "Radial basis function neural network, Vulnerability assessment, Vulnerability index",
author = "Haidar, {Ahmed M A} and Azah Mohamed and Aini Hussain",
year = "2007",
doi = "10.1109/SCORED.2007.4451385",
language = "English",
isbn = "1424414709",
booktitle = "2007 5th Student Conference on Research and Development, SCORED",

}

TY - GEN

T1 - Vulnerability assessment of a large sized power system using radial basis function neural network

AU - Haidar, Ahmed M A

AU - Mohamed, Azah

AU - Hussain, Aini

PY - 2007

Y1 - 2007

N2 - Vulnerability assessment of a power system has been of great concern due to the continual blackouts in recent years which indicate that a power system today is too vulnerable to withstand an unforeseen catastrophic contingency. This paper presents a new approach to assess vulnerability of a power system based on radial basis function neural network. A new feature extraction method named as the neural network weight extraction is also proposed to reduce the number of input features to the neural network. The effectiveness of the proposed approach has been demonstrated on a large sized IEEE 300-bus system. Test results prove that the radial basis function neural network accurately predicts the vulnerability of the power system.

AB - Vulnerability assessment of a power system has been of great concern due to the continual blackouts in recent years which indicate that a power system today is too vulnerable to withstand an unforeseen catastrophic contingency. This paper presents a new approach to assess vulnerability of a power system based on radial basis function neural network. A new feature extraction method named as the neural network weight extraction is also proposed to reduce the number of input features to the neural network. The effectiveness of the proposed approach has been demonstrated on a large sized IEEE 300-bus system. Test results prove that the radial basis function neural network accurately predicts the vulnerability of the power system.

KW - Radial basis function neural network

KW - Vulnerability assessment

KW - Vulnerability index

UR - http://www.scopus.com/inward/record.url?scp=50449099594&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=50449099594&partnerID=8YFLogxK

U2 - 10.1109/SCORED.2007.4451385

DO - 10.1109/SCORED.2007.4451385

M3 - Conference contribution

SN - 1424414709

SN - 9781424414703

BT - 2007 5th Student Conference on Research and Development, SCORED

ER -