By Topic

A neural network-based method for voltage security monitoring

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)
La Scala, M. ; Dipartimento di Ingegneria Elettrica, Naples Univ., Italy ; Trovato, M. ; Torelli, F.

In this paper, a neural network-based method is proposed for monitoring the online voltage security of electric power systems. Using a dynamic model of the system, voltage stability is measured totally, considering a suitable stability index for the whole system, and locally, by defining appropriate voltage-margins for detecting the area of the system where the instability phenomenon arises. A three-layer feedforward neural network is trained to give, as outputs to a pre-defined set of input variables, the expected values of the above defined indices. The neural network is designed by using a fast learning strategy that allows the optimal number of hidden neurons to be easily determined. Moreover, it is shown that, in the operation mode, the system power-margin and the bus power-margins can be easily evaluated using the value of the voltage stability index given by the designed NN. The effectiveness of the proposed approach has been demonstrated on the IEEE 118-bus test system

Published in:

Power Systems, IEEE Transactions on  (Volume:11 ,  Issue: 3 )