By Topic

Use of hidden Markov models for partial discharge pattern 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

2 Author(s)
Satish, L. ; Dept. of High Voltage Eng., Indian Inst. of Sci., Bangalore, India ; Gururaj, B.I.

An attempt was made to use hidden Markov models (HMM) to classify partial discharge (PD) image patterns. After an introduction to HMM, the methodology and algorithms for evolving them are explained. The selection of the model and training parameters and the results obtained are discussed. The utility of the approach is evaluated by applying it to five types of actual PD image patterns. The performance of the HMM approach is shown to exceed that of neural networks

Published in:

Electrical Insulation, IEEE Transactions on  (Volume:28 ,  Issue: 2 )