By Topic

A Combination Approach for Transient Power Quality Disturbance Recognition

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
$33 $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)
Tongyu Xu ; Sch. of Inf. & Electr. Eng., Shenyang Agric. Univ., Shenyang, China ; Wei Zheng

A combination approach of wavelet transformation and neural network is applied to realize transient power quality disturbance signals' recognition. Firstly, the mathematical models of five kinds of transient disturbance signals, such as voltage surging, voltage sag, voltage interruption, transient pulse and transient oscillation are founded. Then, using time-frequency characteristic of wavelet, the sample signal's feature vectors are extracted. At last these feature vectors are input into BP neural network. Using self-learning ability, the disturbance signals can be classified and recognized. The examples show that the method has a higher discrimination. It's effective to resolve transient power quality problem.

Published in:

2012 Asia-Pacific Power and Energy Engineering Conference

Date of Conference:

27-29 March 2012