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

Composite Counterpropagation Neural Networks for Solving Power Flow Problem

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)
Rathinam, A. ; SRM Univ., Kattankulathur ; Padmini, S.

Power flow study is performed to determine the power systems static states at each bus to find the steady state operating conditions of the systems. Power flow study is the most frequently carried out study performed by power utilities and it is required in almost all the stages of power systems planning, operation and control. In this paper, two modules of counterpropagation neural networks (CPNN) are proposed to solve power flow problem under different loading/contingency conditions for computing bus voltage magnitudes and angles of the given power systems. It implements a pattern mapping task. Due to its fast training, the proposed CPNN will be particularly useful for power systems planning studies, as a number of combinations can be tried using it within a small time frame. The mathematical model of power flow comprises a set of non-linear algebraic equations conventionally solved with the Newton-Raphson method. The effectiveness of the proposed CPNN based approach for solving power flow is demonstrated by computation of bus voltage magnitudes and voltage angles for different loading conditions and single line-outage contingencies in IEEE 14-bus systems.

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.