By Topic

Power quality monitoring using 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

1 Author(s)
Daniels, R.F. ; Southern California Edison, Rosemead, CA, USA

With the proliferation of sensitive control systems and personal computers in the commercial and industrial sector, comes a need for electrical utilities to deliver `clean' power. Voltage variations in the form of sags, surges and impulses, i.e., disturbances, can chronically plague and permanently damage electrical equipment. Southern California Edison (SCE) in joint effort with Basic Measuring Instruments (BMI) were teamed up to automate the process of collecting disturbance data, viewing their contents and applying artificial intelligence paradigms (neural networks) to help identify their causes and present possible solutions

Published in:

Neural Networks to Power Systems, 1991., Proceedings of the First International Forum on Applications of

Date of Conference:

23-26 Jul 1991