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Partial Discharges and Noise Discrimination Using Magnetic Antennas, the Cross Wavelet Transform and Support Vector Machines
This paper presents a wavelet analysis technique together with support vector machines (SVM) to discriminate partial discharges (PD) from external disturbances (electromagnetic noise) in a GIS PD measuring system based on magnetic antennas. The technique uses the Cross Wavelet Transform (XWT) to pro...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7308997/ https://www.ncbi.nlm.nih.gov/pubmed/32503301 http://dx.doi.org/10.3390/s20113180 |
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author | Muñoz-Muñoz, Fabio Rodrigo-Mor, Armando |
author_facet | Muñoz-Muñoz, Fabio Rodrigo-Mor, Armando |
author_sort | Muñoz-Muñoz, Fabio |
collection | PubMed |
description | This paper presents a wavelet analysis technique together with support vector machines (SVM) to discriminate partial discharges (PD) from external disturbances (electromagnetic noise) in a GIS PD measuring system based on magnetic antennas. The technique uses the Cross Wavelet Transform (XWT) to process the PD signals and the external disturbances coming from the magnetic antennas installed in the GIS compartments. The measurements were performed in a high voltage (HV) GIS containing a source of PD and common-mode external disturbances, where the external disturbances were created by an electric dipole radiator placed in the middle of the GIS. The PD were created by connecting a needle to the main conductor in one of the GIS compartments. The cross wavelet transform and its local relative phase were used for feature extraction from the PD and the external noise. The features extracted formed linearly separable clusters of PD and external disturbances. These clusters were automatically classified by a support vector machine (SVM) algorithm. The SVM presented an error rate of 0.33%, correctly classifying 99.66% of the signals. The technique is intended to reduce the PD false positive indications of the common-mode signals created by an electric dipole. The measuring system fundamentals, the XWT foundations, the features extraction, the data analysis, the classification algorithm, and the experimental results are presented. |
format | Online Article Text |
id | pubmed-7308997 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-73089972020-06-25 Partial Discharges and Noise Discrimination Using Magnetic Antennas, the Cross Wavelet Transform and Support Vector Machines Muñoz-Muñoz, Fabio Rodrigo-Mor, Armando Sensors (Basel) Article This paper presents a wavelet analysis technique together with support vector machines (SVM) to discriminate partial discharges (PD) from external disturbances (electromagnetic noise) in a GIS PD measuring system based on magnetic antennas. The technique uses the Cross Wavelet Transform (XWT) to process the PD signals and the external disturbances coming from the magnetic antennas installed in the GIS compartments. The measurements were performed in a high voltage (HV) GIS containing a source of PD and common-mode external disturbances, where the external disturbances were created by an electric dipole radiator placed in the middle of the GIS. The PD were created by connecting a needle to the main conductor in one of the GIS compartments. The cross wavelet transform and its local relative phase were used for feature extraction from the PD and the external noise. The features extracted formed linearly separable clusters of PD and external disturbances. These clusters were automatically classified by a support vector machine (SVM) algorithm. The SVM presented an error rate of 0.33%, correctly classifying 99.66% of the signals. The technique is intended to reduce the PD false positive indications of the common-mode signals created by an electric dipole. The measuring system fundamentals, the XWT foundations, the features extraction, the data analysis, the classification algorithm, and the experimental results are presented. MDPI 2020-06-03 /pmc/articles/PMC7308997/ /pubmed/32503301 http://dx.doi.org/10.3390/s20113180 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Muñoz-Muñoz, Fabio Rodrigo-Mor, Armando Partial Discharges and Noise Discrimination Using Magnetic Antennas, the Cross Wavelet Transform and Support Vector Machines |
title | Partial Discharges and Noise Discrimination Using Magnetic Antennas, the Cross Wavelet Transform and Support Vector Machines |
title_full | Partial Discharges and Noise Discrimination Using Magnetic Antennas, the Cross Wavelet Transform and Support Vector Machines |
title_fullStr | Partial Discharges and Noise Discrimination Using Magnetic Antennas, the Cross Wavelet Transform and Support Vector Machines |
title_full_unstemmed | Partial Discharges and Noise Discrimination Using Magnetic Antennas, the Cross Wavelet Transform and Support Vector Machines |
title_short | Partial Discharges and Noise Discrimination Using Magnetic Antennas, the Cross Wavelet Transform and Support Vector Machines |
title_sort | partial discharges and noise discrimination using magnetic antennas, the cross wavelet transform and support vector machines |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7308997/ https://www.ncbi.nlm.nih.gov/pubmed/32503301 http://dx.doi.org/10.3390/s20113180 |
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