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Title
Signal processing techniques for on-line partial discharge detection and classification
Author
Research Group
Innovación en Diseño Industrial
Teoría de la Señal y Comunicaciones
Version
Postprint
Document type
Journal Article
Embargo end date
2016
Language
English
Rights
© 2016 IEEE
Access
Open access
URI
https://hdl.handle.net/20.500.11984/14539
Publisher’s version
Published at
24th European Signal Processing Conference  2016 EUSIPCO. Budapest, Hungary
xmlui.dri2xhtml.METS-1.0.item-publicationfirstpage
1433
xmlui.dri2xhtml.METS-1.0.item-publicationlastpage
1437
Publisher
IEEE
Keywords
Classification
Extension set theory
Partial discharge
Pattern recognition ... [+]
Classification
Extension set theory
Partial discharge
Pattern recognition
PD
ODS 7 Energía asequible y no contaminante
ODS 8 Trabajo decente y crecimiento económico
ODS 9 Industria, innovación e infraestructura
ODS 11 Ciudades y comunidades sostenibles [-]
Subject (UNESCO Thesaurus)
Communication technology
Telecommunication
Industrial design
UNESCO Classification
Telecommunications technology
Abstract
Partial discharge (PD) detection plays a fundamental role in monitoring the health of medium voltage (MV) systems. This paper presents a method for PD detection and source recognition in MV sub-statio ... [+]
Partial discharge (PD) detection plays a fundamental role in monitoring the health of medium voltage (MV) systems. This paper presents a method for PD detection and source recognition in MV sub-stations based on a combination of signal processing techniques. Firstly, PD detection and signal conditioning is carried out. Then, PDs of different sources are separated and finally classified by means of the extension set theory. The obtained results show a classification effectiveness of 100% on single source PDs and an effectiveness of 72.5% in multisource PDs, where PDs from many sources are captured in the same data set. [-]
Funder
Gobierno Español
Number
RTC-2014-1713-3
Project
OPTIMUS
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