Cart (Loading....) | Create Account
Close category search window

Application of data mining on partial discharge part I: predictive modelling classification

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)
Lai, K.X. ; Sch. of Electr. Eng. & Telecommun., Univ. of New South Wales, Sydney, NSW, Australia ; Phung, B.T. ; Blackburn, T.R.

Innovations in computer technology have made possible continuous on-line monitoring of partial discharge (PD) activities. The power industry aims to assess the condition of power system equipment through on-line monitoring of PD activities. This involves long-term continuous data recording and it is very difficult to extract useful information from such a large amount of raw data, particularly if it is done manually. Instead, data mining can be applied in solving this problem. Data mining can be categorized into predictive modelling and descriptive modelling. In this paper, work was mainly focused on predictive data mining, which is classification of PD. The back propagation neural network (BPN), self-organizing map (SOM) and support vector machine (SVM) were used for classification and compared. Results indicate SVM is the best method in terms of classification accuracy and processing speed.

Published in:

Dielectrics and Electrical Insulation, IEEE Transactions on  (Volume:17 ,  Issue: 3 )

Date of Publication:

June 2010

Need Help?

IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.