Title :
Application of wavelet theory to power distribution systems for fault detection
Author :
Momoh, James ; Rizy, D. Tom
Author_Institution :
Dept. of Electr. Eng., Howard Univ., Washington, DC, USA
Abstract :
In this paper, an investigation of the wavelet transform as a means of creating a feature extractor for artificial neural network (ANN) training is presented for application to distribution network fault location. The study includes a terrestrial-based three-phase delta-delta power distribution system. Faults were injected into the system and data was obtained from experimentation. Graphical representations of the feature extractors obtained in the time domain, the frequency domain and the wavelet domain are presented to ascertain the superiority of the wavelet transform feature extractor
Keywords :
distribution networks; fault location; feature extraction; learning (artificial intelligence); neural nets; power system analysis computing; wavelet transforms; application; artificial neural network; distribution network fault location; fault injection; feature extractor; frequency domain; graphical representation; power distribution systems; three-phase delta-delta power system; time domain; training; wavelet domain; wavelet transform; Artificial neural networks; Data mining; Electrical fault detection; Feature extraction; Fourier transforms; Impedance; Neural networks; Power distribution; Wavelet domain; Wavelet transforms;
Conference_Titel :
Intelligent Systems Applications to Power Systems, 1996. Proceedings, ISAP '96., International Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
0-7803-3115-X
DOI :
10.1109/ISAP.1996.501096
