By Topic

A high impedance fault detector using a neural network and subband decomposition

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

3 Author(s)
Keyhani, R. ; Signal Process. Res. Centre, Queensland Univ. of Technol., Brisbane, Qld., Australia ; Deriche, M. ; Palmer, E.

High impedance faults (HIFs) are not easily detectable using conventional overcurrent protection relays. The fault current for HIF is usually less than the normal load current, thus the overcurrent relays cannot easily distinguish HIFs from normal currents. A new method based on a subband decomposition of the current is presented. The energies from the different subbands are used as input to train an artificial neural network (ANN) for the detection of HIFs. The technique, not only detects HIF faults, but also classifies the signals into one of several classes. The main advantage of this method is that it is less sensitive to noise and HIF can be distinguished from similar events, even in the presence of high levels of noise

Published in:

Signal Processing and its Applications, Sixth International, Symposium on. 2001  (Volume:2 )

Date of Conference:

2001