loading...
 This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
Sixth International Conference on Intelligent Systems Design and Applications (ISDA'06) Volume 1
Power Quality Disturbances Classification using Wavelet and Support Vector Machines
Jinan, China
October 16-October 18
ISBN: 0-7695-2528-8
ASCII Text
x 
 
Peisheng Gao, Weilin Wu, "Power Quality Disturbances Classification using Wavelet and Support Vector Machines," Intelligent Systems Design and Applications, International Conference on, vol. 1, pp. 201-206, Sixth International Conference on Intelligent Systems Design and Applications (ISDA'06) Volume 1, 2006.
 
 
BibTex
x
 
@article{ 10.1109/ISDA.2006.217,
author = {Peisheng Gao and Weilin Wu},
title = {Power Quality Disturbances Classification using Wavelet and Support Vector Machines},
journal ={Intelligent Systems Design and Applications, International Conference on},
volume = {1},
year = {2006},
isbn = {0-7695-2528-8},
pages = {201-206},
doi = {http://doi.ieeecomputersociety.org/10.1109/ISDA.2006.217},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Intelligent Systems Design and Applications, International Conference on
TI - Power Quality Disturbances Classification using Wavelet and Support Vector Machines
SN - 0-7695-2528-8
SP201
EP206
A1 - Peisheng Gao,
A1 - Weilin Wu,
PY - 2006
KW - power quality
KW - multiresolution
KW - short time power transform
KW - support vector machines
VL - 1
JA - Intelligent Systems Design and Applications, International Conference on
ER -
 
Peisheng Gao, Zhejiang University, China
Weilin Wu, Zhejiang University, China
Based on wavelet multiresolution analysis (MRA) and support vector machines (SVMs), a classification method for power quality disturbances in electrical power system is presented. After multiresolution signal decomposition of power quality disturbances, characteristic vectors can be obtained. Short time power transform (STPT) is also used to supplement the characteristic vectors from MRA. Support vector machines are used to classify these characteristic vectors of power quality disturbances, and the performance of SVMs is compared with that of artificial neural network (ANN).
Index Terms:
power quality, multiresolution, short time power transform, support vector machines
Citation:
Peisheng Gao, Weilin Wu, "Power Quality Disturbances Classification using Wavelet and Support Vector Machines," isda, vol. 1, pp.201-206, Sixth International Conference on Intelligent Systems Design and Applications (ISDA'06) Volume 1, 2006
Usage of this product signifies your acceptance of the Terms of Use.