By Topic

Diagnosis accuracy in electric power apparatus conditions using classification methods

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

2 Author(s)
Hideo Hirose ; Kyushu Institute of Technology, Fukuoka, Japan ; Faisal Zaman

The use of the decision tree method was recommended as a classification tool in diagnosing electric power apparatus because it provides the visible if-then rule, making it possible to connect the physical phenomena with the observed signals. Using a variety of feature variables extracted from the partial discharge patterns and others, the misclassification rates were found to be as small as 2% if results were obtained using training data only. In this paper, we assess the diagnosing accuracy of the classification methods using test data; we have found that the small values of the misclassification rates remain even when test data are applied. The appropriate methods perform fairly well, with misclassification rates of less than 5%. We conclude that although the misclassification rates by the decision tree are not as small as the values obtained by effective ensemble classifiers such as bagging and boosting, the decision tree is still useful and attractive because the method provides explicit rules, and the variability of the misclassification rates is not very large.

Published in:

IEEE Transactions on Dielectrics and Electrical Insulation  (Volume:17 ,  Issue: 1 )