By Topic

Design, implementation and testing of an artificial neural network based fault direction discriminator for protecting transmission lines

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)
Sidhu, T.S. ; Power Syst. Res. Group, Saskatchewan Univ., Saskatoon, Sask., Canada ; Singh, H. ; Sachdev, M.S.

This paper describes a fault direction discriminator that uses an artificial neural network (ANN) for protecting transmission lines. The discriminator uses various attributes to reach a decision and tends to emulate the conventional pattern classification problem. An equation of the boundary describing the classification is embedded in the multilayer feedforward neural network (MFNN) by training through the use of an appropriate learning algorithm and suitable training data. The discriminator uses instantaneous values of the line voltages and line currents to make decisions. Results showing the performance of the ANN-based discriminator are presented in the paper and indicate that it is fast, robust and accurate. It is suitable for realizing an ultrafast directional comparison protection of transmission lines

Published in:

Power Delivery, IEEE Transactions on  (Volume:10 ,  Issue: 2 )