By Topic

Fault identification of power transformers using genetic-based wavelet networks

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 $31
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

1 Author(s)

The paper presents genetic-based wavelet networks (GWNs) for fault identification of power transformers. GWNs are three-layer structures, which contain wavelet, weighting and summing layers. Using a genetic-algorithm (GA) based optimisation process, the GWNs automatically tune the network parameters, translation and dilation in the wavelet nodes and the weighting values in the weighting nodes. The GWNs, with global search abilities of the GA and the multiresolution and localisation natures of the wavelets, can identify the complicated relations of dissolved gas contents in transformer oil to corresponding fault types. The proposed GWNs have been tested on the Taipower Company diagnostic records and compared with the fuzzy diagnosis system, artificial neural networks as well as the conventional method. The experimental results reveal that the GWNs have remarkable diagnostic accuracy and require far less construction time than conventional methods.

Published in:

Science, Measurement and Technology, IEE Proceedings -  (Volume:150 ,  Issue: 1 )