Detection of Disturbances in Voltage Signals for Power Quality Analysis Using HOS

This paper outlines a higher-order statistics (HOS)-based technique for detecting abnormal conditions in voltage signals. The main advantage introduced by the proposed technique refers to its capability to detect voltage disturbances and their start and end points in a frame whose length corresponds...

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Main Authors: José Luiz R. Pereira, Augusto S. Cerqueira, Carlos A. Duque, Cristiano Augusto G. Marques, Moisés V. Ribeiro
Format: Article
Language:English
Published: SpringerOpen 2007-01-01
Series:EURASIP Journal on Advances in Signal Processing
Online Access:http://dx.doi.org/10.1155/2007/59786
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spelling doaj-6ade8cd08709483cb4127d0d00a33f842020-11-24T22:21:51ZengSpringerOpenEURASIP Journal on Advances in Signal Processing1687-61721687-61802007-01-01200710.1155/2007/59786Detection of Disturbances in Voltage Signals for Power Quality Analysis Using HOSJosé Luiz R. PereiraAugusto S. CerqueiraCarlos A. DuqueCristiano Augusto G. MarquesMoisés V. RibeiroThis paper outlines a higher-order statistics (HOS)-based technique for detecting abnormal conditions in voltage signals. The main advantage introduced by the proposed technique refers to its capability to detect voltage disturbances and their start and end points in a frame whose length corresponds to, at least, N=16 samples or 1/16 of the fundamental component if a sampling rate equal to fs=256×60 Hz is considered. This feature allows the detection of disturbances in submultiples or multiples of one-cycle fundamental component if an appropriate sampling rate is considered. From the computational results, one can note that almost all abnormal and normal conditions are correctly detected if N= s256, 128, 64, 32, and 16 and the SNR is higher than 25 dB. In addition, the proposed technique is compared to a root mean square (rms)-based technique, which was recently developed to detect the presence of some voltage events as well as their sources in a frame whose length ranges from 1/8 up to one-cycle fundamental component. The numerical results reveal that the proposed technique shows an improved performance when applied not only to synthetic data, but also to real one. http://dx.doi.org/10.1155/2007/59786
collection DOAJ
language English
format Article
sources DOAJ
author José Luiz R. Pereira
Augusto S. Cerqueira
Carlos A. Duque
Cristiano Augusto G. Marques
Moisés V. Ribeiro
spellingShingle José Luiz R. Pereira
Augusto S. Cerqueira
Carlos A. Duque
Cristiano Augusto G. Marques
Moisés V. Ribeiro
Detection of Disturbances in Voltage Signals for Power Quality Analysis Using HOS
EURASIP Journal on Advances in Signal Processing
author_facet José Luiz R. Pereira
Augusto S. Cerqueira
Carlos A. Duque
Cristiano Augusto G. Marques
Moisés V. Ribeiro
author_sort José Luiz R. Pereira
title Detection of Disturbances in Voltage Signals for Power Quality Analysis Using HOS
title_short Detection of Disturbances in Voltage Signals for Power Quality Analysis Using HOS
title_full Detection of Disturbances in Voltage Signals for Power Quality Analysis Using HOS
title_fullStr Detection of Disturbances in Voltage Signals for Power Quality Analysis Using HOS
title_full_unstemmed Detection of Disturbances in Voltage Signals for Power Quality Analysis Using HOS
title_sort detection of disturbances in voltage signals for power quality analysis using hos
publisher SpringerOpen
series EURASIP Journal on Advances in Signal Processing
issn 1687-6172
1687-6180
publishDate 2007-01-01
description This paper outlines a higher-order statistics (HOS)-based technique for detecting abnormal conditions in voltage signals. The main advantage introduced by the proposed technique refers to its capability to detect voltage disturbances and their start and end points in a frame whose length corresponds to, at least, N=16 samples or 1/16 of the fundamental component if a sampling rate equal to fs=256×60 Hz is considered. This feature allows the detection of disturbances in submultiples or multiples of one-cycle fundamental component if an appropriate sampling rate is considered. From the computational results, one can note that almost all abnormal and normal conditions are correctly detected if N= s256, 128, 64, 32, and 16 and the SNR is higher than 25 dB. In addition, the proposed technique is compared to a root mean square (rms)-based technique, which was recently developed to detect the presence of some voltage events as well as their sources in a frame whose length ranges from 1/8 up to one-cycle fundamental component. The numerical results reveal that the proposed technique shows an improved performance when applied not only to synthetic data, but also to real one.
url http://dx.doi.org/10.1155/2007/59786
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