Automatic recognition system of underlying causes of power quality disturbances based on S-Transform and Extreme Learning Machine
Author:
Publisher
Elsevier BV
Subject
Electrical and Electronic Engineering,Energy Engineering and Power Technology
Reference34 articles.
1. Mason J, Targosz R. European power quality survey report, leonardo energy initiative, November 2008. .
2. Detection and classification of power quality disturbances using S-transform and modular neural network;Bhende;Electric Power Syst Res,2008
3. Classification of power quality data using decision tree and chemotactic differential evolution based fuzzy clustering;Biswal;Swarm Evol Comput,2012
4. Detection and classification of power quality disturbances using discrete wavelet transform and wavelet networks;Masoum;IET Sci Meas Technol,2010
5. Power quality disturbance identification using wavelet packet energy entropy and weighted support vector machines;Hu;Expert Syst Appl,2008
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