By Topic

Fractal image compression for classification of PD sources

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)
Lalitha, E.M. ; Dept. of High Voltage Eng., Indian Inst. of Sci., Bangalore, India ; Satish, L.

The fractal image compression technique has a unique feature due to which physical position of blocks/regions in the input image can be extracted directly from the compressed data. Applying this technique, φ-q-n partial discharge (PD) patterns (treated as an image) are compressed and stored as affine transformations. These transformations then are used directly to extract the embedded pattern features, which are classified by a neural network. The novel route to PD pattern classification described in this paper thus addresses both the tasks of compression and feature extraction in a single step. The task of compression is essential to store and handle large quantities of pattern data acquired, especially during on-line monitoring of PD in power apparatus. Results presented illustrate that this approach can address satisfactorily the tasks of compression and classification of PD patterns

Published in:

Dielectrics and Electrical Insulation, IEEE Transactions on  (Volume:5 ,  Issue: 4 )