Title :
Wavelet transform and neural network approach to developing adaptive single-pole auto-reclosing schemes for EHV transmission systems
Author :
Yu, I.K. ; Song, Y.H.
Author_Institution :
Dept. of Electr. Eng. & Electron., Brunel Univ., Uxbridge, UK
fDate :
11/1/1998 12:00:00 AM
Abstract :
The authors adopt the wavelet transform to detect and identify relevant electrical fault characteristics in power transmission systems. They use several components of wavelet analysis as input features to a forward neural network to distinguish transient, permanent faults and the secondary arc extinction point
Keywords :
fault location; neural nets; power system analysis computing; transmission network calculations; transmission networks; wavelet transforms; EHV transmission systems; adaptive single-pole auto-reclosing schemes; computer simulation; electrical fault characteristics; fault detection; fault identification; neural network approach; permanent faults; secondary arc extinction point; transient faults; wavelet transform; Discrete wavelet transforms; Fault diagnosis; Frequency; Neural networks; Power system transients; Signal analysis; Transient analysis; Voltage; Wavelet analysis; Wavelet transforms;
Journal_Title :
Power Engineering Review, IEEE
