By Topic

Wavelet Neural Network based fault detection method in power system

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

4 Author(s)
Yang Xiaohua ; Shandong Province Key Lab. of horticultural Machineries & Equipments, Shandong Agric. Univ., Tai''an, China ; Zhang Yadong ; China Zhao Faqi ; Xi Zhongmei

Wavelet Neural Network combined the advantages of wavelet transform and neural network, It is a knowledge-based fault diagnosis method It doesn't need accurate mathematical model, both have good time-frequency localization properties and better self-learning ability and fault tolerance. This article describes the natural network in power system fault detection, the simulation results show that, compared with the traditional artificial neural network, the wavelet neural network has the characteristics of fast convergence. So wavelet neural network can be applied to power system fault detection.

Published in:

Mechanic Automation and Control Engineering (MACE), 2011 Second International Conference on

Date of Conference:

15-17 July 2011