By Topic

Power quality data mining using soft computing and wavelet transform

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)
Dash, P.K. ; Silicon Inst. of Technol., Bhubaneshwar, India ; Chun, I.L.W. ; Chilukuri, M.V.

This paper presents a new approach to power quality data mining using a modified wavelet transform for feature extraction of power disturbance signal data and a fuzzy multilayer perceptron network to generate the rules and classify the patterns. The choice of modified wavelet transform known as multiresolution s-transform is essential for processing very short duration nonstationary time series data from transient disturbances occurring on an electric supply network as they can not be handled by conventional Fourier and other transform methods for extraction of relevant features pertinent for data mining applications. The trained fuzzy neural network infers the output class membership value of an input pattern and a certainty measure is also presented to facilitate rule generation. Using the electric supply network disturbance data obtained from numerical algorithms and MATLAB software, the paper presents transient disturbance pattern classification scores. A knowledge discovery approach is also highlighted in the paper to convert raw power disturbance signal data to knowledge in the form of an answer module to the queries by the end-users. The pattern classification approach used in this paper can also be applied to speech, cardiovascular system and other medical and engineering databases.

Published in:

TENCON 2003. Conference on Convergent Technologies for the Asia-Pacific Region  (Volume:3 )

Date of Conference:

15-17 Oct. 2003