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

Security Assessment of Power Systems Using Artificial Neural Networks - A Comparison between Euclidean Distance Based Learning and Supervised Learning Algorithms

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)

This paper presents a research work on artificial neural networks (ANN) to examine whether the Power Systems is secured under steady-state operating conditions. ANN determines the minimum bus voltages and maximum ratio of line-flow to permissible line- flow. Detailed load flow study is avoided if the values supplied by ANN satisfy these operating constraints. For training, fast decoupled load flow data are used. The Artificial Neural Networks used are Multilayer feed forward Network with error Backpropagation Algorithm and a Counterpropagation Neural Network. The CPNN is a network which can obtain a mapping from inputs to outputs by competitive learning and supervised learning. Extensive studies have been made by varying the network parameters of both the networks. The hidden neurons are also varied to fix the optimum architecture for the problem to be solved. The input variables to the network are the active Power of the load buses, Power factor of the loads and the net generated Power of the generating buses. The generating bus voltages and tap settings of the transformer are assumed to be constant (fixed). The algorithms are tested for security assessment on an IEEE-14 bus Systems and the test results are presented. Results of the both the ANN closely agrees with that obtained by fast decoupled load flow. The computation time of both the ANN is much smaller than that by fast decoupled load-flow. However, comparing the training time and suitability for online application in Power Systems, CPNN is best suited due to its fast learning based on Euclidean distance calculations.

Published in:

Conference on Computational Intelligence and Multimedia Applications, 2007. International Conference on  (Volume:1 )

Date of Conference:

13-15 Dec. 2007

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.