Scheduled System Maintenance:
On Monday, April 27th, IEEE Xplore will undergo scheduled maintenance from 1:00 PM - 3:00 PM ET (17:00 - 19:00 UTC). No interruption in service is anticipated.
By Topic

Bushing Fault Detection and Diagnosis using Extension Neural Network

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

2 Author(s)
Vilakazi, C.B. ; Sch. of Electr. & Inf. Eng., Univ. of the Witwatersrand, Johannesburg ; Marwala, T.

This paper proposes an extension neural network (ENN) based bushing fault detection and diagnosis. Experimentation is done using dissolve gas-in-oil analysis (DGA) data from bushings based on IEEEc57.104, IEC599 and IEEE production rates methods for oil impregnated paper (OIP) bushings. The optimal learning rate for ENN is selected using genetic algorithm (GA). The classification process is a two stage phase. The first stage is the detection which identifies if the bushing is faulty or normal while the second stage determines the nature of fault. A classification rate of 100% and an average of 99.89% obtained for the detection and diagnosis stage, respectively. It takes 1.98s and 2.02s to train the ENN for the detection and diagnosis stage, respectively

Published in:

Intelligent Engineering Systems, 2006. INES '06. Proceedings. International Conference on

Date of Conference:

0-0 0