By Topic

Vulnerability Assessment of a Large Sized Power System Using Radial Basis Function Neural Network

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Haidar, A.M.A. ; Nat. Univ. of Malaysia (UKM), Bangi ; Mohamed, A. ; Hussain, A.

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.

Published in:

Research and Development, 2007. SCOReD 2007. 5th Student Conference on

Date of Conference:

12-11 Dec. 2007