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Received: 2007-05-21

Revision Accepted: 2007-08-10

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Journal of Zhejiang University SCIENCE A 2008 Vol.9 No.2 P.173~181

10.1631/jzus.A071261


Classification of power quality combined disturbances based on phase space reconstruction and support vector machines


Author(s):  Zhi-yong LI, Wei-lin WU

Affiliation(s):  School of Electrical Engineering, Zhejiang University, Hangzhou 310027, China

Corresponding email(s):   zhiyongrwx_jill@163.com, eewuwl@zju.edu.cn

Key Words:  Power Quality (PQ), Combined disturbance, Classification, Phase Space Reconstruction (PSR), Support Vector Machines (SVMs)


Zhi-yong LI, Wei-lin WU. Classification of power quality combined disturbances based on phase space reconstruction and support vector machines[J]. Journal of Zhejiang University Science A, 2008, 9(2): 173~181.

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author="Zhi-yong LI, Wei-lin WU",
journal="Journal of Zhejiang University Science A",
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doi="10.1631/jzus.A071261"
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%T Classification of power quality combined disturbances based on phase space reconstruction and support vector machines
%A Zhi-yong LI
%A Wei-lin WU
%J Journal of Zhejiang University SCIENCE A
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%N 2
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%@ 1673-565X
%D 2008
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A071261

TY - JOUR
T1 - Classification of power quality combined disturbances based on phase space reconstruction and support vector machines
A1 - Zhi-yong LI
A1 - Wei-lin WU
J0 - Journal of Zhejiang University Science A
VL - 9
IS - 2
SP - 173
EP - 181
%@ 1673-565X
Y1 - 2008
PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.A071261


Abstract: 
power Quality (PQ) combined disturbances become common along with ubiquity of voltage flickers and harmonics. This paper presents a novel approach to classify the different patterns of PQ combined disturbances. The classification system consists of two parts, namely the feature extraction and the automatic recognition. In the feature extraction stage, phase Space Reconstruction (PSR), a time series analysis tool, is utilized to construct disturbance signal trajectories. For these trajectories, several indices are proposed to form the feature vectors. support Vector Machines (SVMs) are then implemented to recognize the different patterns and to evaluate the efficiencies. The types of disturbances discussed include a combination of short-term disturbances (voltage sags, swells) and long-term disturbances (flickers, harmonics), as well as their homologous single ones. The feasibilities of the proposed approach are verified by simulation with thousands of PQ events. Comparison studies based on Wavelet Transform (WT) and Artificial Neural Network (ANN) are also reported to show its advantages.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

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