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
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;
Conference_Titel :
Mechanic Automation and Control Engineering (MACE), 2011 Second International Conference on
Conference_Location :
Hohhot
Print_ISBN :
978-1-4244-9436-1
DOI :
10.1109/MACE.2011.5987327