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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2007-01-01
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Series: | EURASIP Journal on Advances in Signal Processing |
Online Access: | http://dx.doi.org/10.1155/2007/59786 |
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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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