By Topic

Recognition of impulse fault patterns in transformers using Kohonen's self-organizing feature map

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)
De, A. ; Dept. of Electr. Eng., Jadavpur Univ., Calcutta, India ; Chatterjee, N.

Determination of exact nature and location of faults during impulse testing of transformers is of practical importance to the manufacturer as well as designers. The presently available diagnostic techniques more or less depend on expertized knowledge of the test personnel, and in many cases are not beyond ambiguity and controversy. This paper presents an artificial neural network (ANN) approach for detection and diagnosis of fault nature and fault location in oil-filled power transformers during impulse testing. This new approach relies on high discrimination power and excellent generalization ability of ANNs in a complex pattern classification problem, and overcomes the limitations of conventional expert or knowledge-based systems in this field. In the present work the "self-organizing feature map" (SOFM) algorithm with Kohonen's learning has been successfully applied to the problem with good diagnostic accuracy

Published in:

Power Delivery, IEEE Transactions on  (Volume:17 ,  Issue: 2 )