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

SOFM based support vector regression model for prediction and its application in power system transient stability forecasting

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)
Li, D.H. ; Huazhong Univ. of Sci. & Technol., Wuhan ; Cao, Y.J.

This paper proposes a real time prediction method that may be applied in power system transient stability forecasting which can predict the future behavior by using SVR (support vector regression) and the data coming from PMUs (phasor measurement units). With a view to improving the training efficiency of SVR and the prediction accuracy, the proposed method is based on the self-organizing feature map (SOFM) that can discover the similar training input data and cluster them into several classes in which input data have approximate trend. Then, the similar data are used as input data for a SVR predictor. Because the SOFM extracts similar data from learning data as a preprocessor, which decreases the size of the sample set for one SVR, and also reduces the mutual influences of other learning data that are not related to the similar data of one class, the method not only enhances the training speed but also can forecast with high accuracy under different conditions. Forecasting results of simulation on New England 10 generators system prove the feasibility of this model

Published in:

Power Engineering Conference, 2005. IPEC 2005. The 7th International

Date of Conference:

Nov. 29 2005-Dec. 2 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.