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Partial Discharge Monitoring on Metal-Enclosed Switchgear with Distributed Non-Contact Sensors
Metal-enclosed switchgear, which are widely used in the distribution of electrical energy, play an important role in power distribution networks. Their safe operation is directly related to the reliability of power system as well as the power quality on the consumer side. Partial discharge detection...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5855104/ https://www.ncbi.nlm.nih.gov/pubmed/29439475 http://dx.doi.org/10.3390/s18020551 |
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author | Zhang, Chongxing Dong, Ming Ren, Ming Huang, Wenguang Zhou, Jierui Gao, Xuze Albarracín, Ricardo |
author_facet | Zhang, Chongxing Dong, Ming Ren, Ming Huang, Wenguang Zhou, Jierui Gao, Xuze Albarracín, Ricardo |
author_sort | Zhang, Chongxing |
collection | PubMed |
description | Metal-enclosed switchgear, which are widely used in the distribution of electrical energy, play an important role in power distribution networks. Their safe operation is directly related to the reliability of power system as well as the power quality on the consumer side. Partial discharge detection is an effective way to identify potential faults and can be utilized for insulation diagnosis of metal-enclosed switchgear. The transient earth voltage method, an effective non-intrusive method, has substantial engineering application value for estimating the insulation condition of switchgear. However, the practical application effectiveness of TEV detection is not satisfactory because of the lack of a TEV detection application method, i.e., a method with sufficient technical cognition and analysis. This paper proposes an innovative online PD detection system and a corresponding application strategy based on an intelligent feedback distributed TEV wireless sensor network, consisting of sensing, communication, and diagnosis layers. In the proposed system, the TEV signal or status data are wirelessly transmitted to the terminal following low-energy signal preprocessing and acquisition by TEV sensors. Then, a central server analyzes the correlation of the uploaded data and gives a fault warning level according to the quantity, trend, parallel analysis, and phase resolved partial discharge pattern recognition. In this way, a TEV detection system and strategy with distributed acquisition, unitized fault warning, and centralized diagnosis is realized. The proposed system has positive significance for reducing the fault rate of medium voltage switchgear and improving its operation and maintenance level. |
format | Online Article Text |
id | pubmed-5855104 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-58551042018-03-20 Partial Discharge Monitoring on Metal-Enclosed Switchgear with Distributed Non-Contact Sensors Zhang, Chongxing Dong, Ming Ren, Ming Huang, Wenguang Zhou, Jierui Gao, Xuze Albarracín, Ricardo Sensors (Basel) Article Metal-enclosed switchgear, which are widely used in the distribution of electrical energy, play an important role in power distribution networks. Their safe operation is directly related to the reliability of power system as well as the power quality on the consumer side. Partial discharge detection is an effective way to identify potential faults and can be utilized for insulation diagnosis of metal-enclosed switchgear. The transient earth voltage method, an effective non-intrusive method, has substantial engineering application value for estimating the insulation condition of switchgear. However, the practical application effectiveness of TEV detection is not satisfactory because of the lack of a TEV detection application method, i.e., a method with sufficient technical cognition and analysis. This paper proposes an innovative online PD detection system and a corresponding application strategy based on an intelligent feedback distributed TEV wireless sensor network, consisting of sensing, communication, and diagnosis layers. In the proposed system, the TEV signal or status data are wirelessly transmitted to the terminal following low-energy signal preprocessing and acquisition by TEV sensors. Then, a central server analyzes the correlation of the uploaded data and gives a fault warning level according to the quantity, trend, parallel analysis, and phase resolved partial discharge pattern recognition. In this way, a TEV detection system and strategy with distributed acquisition, unitized fault warning, and centralized diagnosis is realized. The proposed system has positive significance for reducing the fault rate of medium voltage switchgear and improving its operation and maintenance level. MDPI 2018-02-11 /pmc/articles/PMC5855104/ /pubmed/29439475 http://dx.doi.org/10.3390/s18020551 Text en © 2018 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 Zhang, Chongxing Dong, Ming Ren, Ming Huang, Wenguang Zhou, Jierui Gao, Xuze Albarracín, Ricardo Partial Discharge Monitoring on Metal-Enclosed Switchgear with Distributed Non-Contact Sensors |
title | Partial Discharge Monitoring on Metal-Enclosed Switchgear with Distributed Non-Contact Sensors |
title_full | Partial Discharge Monitoring on Metal-Enclosed Switchgear with Distributed Non-Contact Sensors |
title_fullStr | Partial Discharge Monitoring on Metal-Enclosed Switchgear with Distributed Non-Contact Sensors |
title_full_unstemmed | Partial Discharge Monitoring on Metal-Enclosed Switchgear with Distributed Non-Contact Sensors |
title_short | Partial Discharge Monitoring on Metal-Enclosed Switchgear with Distributed Non-Contact Sensors |
title_sort | partial discharge monitoring on metal-enclosed switchgear with distributed non-contact sensors |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5855104/ https://www.ncbi.nlm.nih.gov/pubmed/29439475 http://dx.doi.org/10.3390/s18020551 |
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