By Topic

Nonstationary power signal processing and pattern recognition using genetic algorithm

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

5 Author(s)
Biswal, B. ; Silicon Inst. of Technol., Delhi ; Dash, P.K. ; Panigrahi, K.B. ; Panda, T.K.
more authors

A new approach to time frequency transform and pattern recognition of non-Stationary power signals is presented in this paper. In the proposed work Visual localization, detection and classification of non-stationary power signals are achieved using HS-Transform and automatic pattern recognition is carried out using fuzzy C-means based Genetic algorithm. Time frequency analysis and Feature extraction from the non- stationary power signals is done by HS-Transform. Once the feature vectors are extracted is used for pattern recognition of various non-Stationary signals. Various non-stationary power signals are processed through HS-transform with hyperbolic window to generate time-frequency contours for extracting relevant features for pattern classification. The extracted features are clustered using fuzzy C-means algorithm and finally the algorithm is extended using Genetic algorithm to refine the cluster centers. The average classification accuracy of the disturbances is 93.25% and 95% using fuzzy C-means and Genetic based fuzzy C-means algorithm, respectively.

Published in:

Information, Communications & Signal Processing, 2007 6th International Conference on

Date of Conference:

10-13 Dec. 2007