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6th IEEE/ACIS International Conference on Computer and Information Science (ICIS 2007)
Recognition of Power Quality Events by Using Multiwavelet-Based Neural Network
Melbourne, Australia
July 11-July 13
ISBN: 0-7695-2841-4
ASCII Text
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Suriya Kaewarsa, Kitti Attakitmongcol, Thanatchai Kulworawanichpong, "Recognition of Power Quality Events by Using Multiwavelet-Based Neural Network," Computer and Information Science, ACIS International Conference on, pp. 993-998, 6th IEEE/ACIS International Conference on Computer and Information Science (ICIS 2007), 2007.
 
 
BibTex
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@article{ 10.1109/ICIS.2007.153,
author = {Suriya Kaewarsa and Kitti Attakitmongcol and Thanatchai Kulworawanichpong},
title = {Recognition of Power Quality Events by Using Multiwavelet-Based Neural Network},
journal ={Computer and Information Science, ACIS International Conference on},
volume = {0},
year = {2007},
isbn = {0-7695-2841-4},
pages = {993-998},
doi = {http://doi.ieeecomputersociety.org/10.1109/ICIS.2007.153},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Computer and Information Science, ACIS International Conference on
TI - Recognition of Power Quality Events by Using Multiwavelet-Based Neural Network
SN - 0-7695-2841-4
SP993
EP998
A1 - Suriya Kaewarsa,
A1 - Kitti Attakitmongcol,
A1 - Thanatchai Kulworawanichpong,
PY - 2007
KW - null
VL - 0
JA - Computer and Information Science, ACIS International Conference on
ER -
 
Suriya Kaewarsa, Rajamangala University of Technology Isan, Thailand
Kitti Attakitmongcol, Suranaree University of Technology, Thailand
Thanatchai Kulworawanichpong, Suranaree University of Technology, Thailand
Recognition of power quality events by analyzing the voltage and current waveform disturbances is a very important task for the power system monitoring. This paper presents a novel approach for the recognition of power quality disturbances using multiwavelet transform and neural networks. The proposed method employs the multiwavelet transform using multiresolution signal decomposition techniques working together with multiple neural networks using a learning vector quantization network as a powerful classifier. Various transient events are tested, such as voltage sag, swell, interruption, notching, impulsive transient, and harmonic distortion show that the classifier can detect and classify different.
Citation:
Suriya Kaewarsa, Kitti Attakitmongcol, Thanatchai Kulworawanichpong, "Recognition of Power Quality Events by Using Multiwavelet-Based Neural Network," icis, pp.993-998, 6th IEEE/ACIS International Conference on Computer and Information Science (ICIS 2007), 2007
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