By Topic

On-line supervised learning for dynamic security classification using probabilistic neural 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 $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)
Gavoyiannis, A.E. ; Dept. of Electr. & Comput. Eng., Nat. Tech. Univ. of Athens, Greece ; Voumvoulakis, E.M. ; Hatziargyriou, N.D.

This paper addresses the problem of dynamic security classification of electric power systems using multiclass pattern recognition. In particular, on-line supervised learning using probabilistic neural networks is applied. The various patterns are recognized by calculating probabilities of belonging to each class. These probabilities are used in a subsequent decision-making stage to achieve classification. The learning of each class can be performed in parallel. Results regarding performance of the proposed pattern recognition tested on the dynamic security of an actual island power system are presented and discussed.

Published in:

Power Engineering Society General Meeting, 2005. IEEE

Date of Conference:

12-16 June 2005