Cart (Loading....) | Create Account
Close category search window

Neural network based pattern recognition for power system security assessment

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

2 Author(s)
Swarup, K.S. ; Dept. of Electr. Eng., Indian Inst. of Technol., Madras, India ; Prasad Reddy, K.V.

Artificial neural networks (ANN) based pattern recognition for steady state (SSA), transient (TSA) and dynamic security assessment (DSA) of the power systems is presented. Conventional methods for security assessment take lot of time to assess the security of power systems when all contingencies are considered. Artificial neural networks based pattern recognition is used to solve SSA, TSA and DSA and its effectiveness against conventional methods is presented. In addition contingency ranking and generator severity ranking is used to measure the severity of various contingencies which allows an operator to take preventive control (generator rescheduling) or to be ready for corrective actions. Direct methods are fast compared to numerical methods but proper choice of Lyapunov function is difficult. ANN based PR for SSA, TSA and DSA effectiveness is compared with other conventional methods like pattern recognition which confirms that ANN not only gives accurate results but also is adaptive to the topology changes of the power system. In addition contingency ranking and generator severity ranking has been addressed which allows the operator to take preventive control actions like generator rescheduling.

Published in:

Intelligent Sensing and Information Processing, 2005. Proceedings of 2005 International Conference on

Date of Conference:

4-7 Jan. 2005

Need Help?

IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.