By Topic

Fast contingency analysis by means of a progressive learning 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
$33 $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

4 Author(s)
E. Bompard ; Dipt. di Ingegneria Elettrica Ind., Politecnico di Torino, Italy ; G. Chicco ; R. Napoli ; F. Piglione

Contingency analysis is a very demanding task in online operation of electric power systems. Amongst the many approaches proposed in literature, the application of artificial neural networks (ANN) showed promising performances, but it often failed to cope with the huge size and the large number of operative states of the real power systems. This paper presents a fast online method based on an original progressive learning ANN. Firstly, the influence zone of each outage is located. Then, a dedicated ANN is trained to forecast the post-fault values of critical line flows and bus voltages. A progressive learning variant of the radial basis function network allows fast and adaptive learning of the pre/post-fault relationships. Tests carried out on a realistic simulator based on the IEEE 118-bus system proved the feasibility of the proposed method.

Published in:

Electric Power Engineering, 1999. PowerTech Budapest 99. International Conference on

Date of Conference:

Aug. 29 1999-Sept. 2 1999