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

Study on the classification method of power disturbances based on the combination of s transform and SVM multi-class classifier with binary tree

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)
Liu ShangWei ; Sch. of Electr. Eng. & Autom., Tianjin Univ., Tianjin ; Sun Yarning

A new method based on the combination of the S transform and support vector machine (SVM) multi-class classifier for the classification and recognition of power quality disturbance signals in power system is presented in this paper. The proposed method consists of time-frequency analysis, feature extraction, and pattern classification. In the first stage, S transform is applied to extract a set of optimal feature vectors for the classification of power quality disturbance signals. Different power quality disturbances have distinct characteristics such as maximum standard deviation, local maximum, and duration time, etc. By analyzing the complex matrixes generated by S transform of signals, five features were extracted, through which six types of power quality disturbance signals can be classified accurately , therefore the dimension of the feature vectors is decreased greatly. In stage two, the power quality disturbance types are classified through the multi-class classifier based on SVM. The features extracted from S transform are used as the input to a SVM multi-classifier. Combining decision-making method of binary tree with SVM binary classifier, the SVM multi-classifier is formed. It reduces the number of SVM classifiers greatly. The simulation results show that the method presented in this paper has good performance on classification accuracy and computing speed, compared with the one-against-one model.

Published in:

Electric Utility Deregulation and Restructuring and Power Technologies, 2008. DRPT 2008. Third International Conference on

Date of Conference:

6-9 April 2008

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.