DocumentCode
2798567
Title
Wavelet Neural Network based fault detection method in power system
Author
Xiaohua, Yang ; Yadong, Zhang ; Faqi, Zhao ; Zhongmei, Xi
Author_Institution
Shandong Province Key Lab. of horticultural Machineries & Equipments, Shandong Agric. Univ., Tai´´an, China
fYear
2011
fDate
15-17 July 2011
Firstpage
1864
Lastpage
1867
Abstract
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.
Keywords
fault diagnosis; fault tolerance; knowledge based systems; neural nets; power system analysis computing; wavelet transforms; fault tolerance; knowledge-based fault diagnosis method; power system fault detection; self-learning ability; time-frequency localization; wavelet neural network; wavelet transform; Biological neural networks; Circuit faults; Neurons; Power systems; Wavelet analysis; Wavelet domain; Wavelet transforms; Wavelet neural network; electric power system; failure testing;
fLanguage
English
Publisher
ieee
Conference_Titel
Mechanic Automation and Control Engineering (MACE), 2011 Second International Conference on
Conference_Location
Hohhot
Print_ISBN
978-1-4244-9436-1
Type
conf
DOI
10.1109/MACE.2011.5987327
Filename
5987327
Link To Document