Partekatu
Gorde erreferentzia
Izenburua
Signal processing techniques for on-line partial discharge detection and classification
Bertsioa
PostprintaDokumentu-mota
ArtikuluaBahituraren amaiera data
2016Hizkuntza
IngelesaEskubideak
© 2016 IEEESarbidea
Sarbide irekiaArgitaratzailearen bertsioa
https://doi.org/10.1109/EUSIPCO.2016.7760485Non argitaratua
24th European Signal Processing Conference 2016 EUSIPCO. Budapest, HungaryLehenengo orria
1433Azken orria
1437Argitaratzailea
IEEEGako-hitzak
Classification
Extension set theory
Partial discharge
Pattern recognition ... [+]
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 [-]
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 [-]
Gaia (UNESCO Tesauroa)
Komunikazioaren teknologiaTelekomunikazioak
Diseinu industriala
UNESCO Sailkapena
Telekomunikazioen teknologiaLaburpena
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. [-]
