Publication:
Multiple partial discharge source discrimination with multiclass support vector machines

dc.affiliation.dptoUC3M. Departamento de Ingeniería Eléctricaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Diagnóstico de Máquinas Eléctricas y Materiales Aislantes (DIAMAT)es
dc.contributor.authorRobles Muñoz, Guillermo
dc.contributor.authorParrado Hernández, Emilio
dc.contributor.authorArdila Rey, Jorge Alfredo
dc.contributor.authorMartínez Tarifa, Juan Manuel
dc.date.accessioned2022-09-05T10:08:51Z
dc.date.available2022-09-05T10:08:51Z
dc.date.issued2016-08-15
dc.description.abstractThe costs of decommissioning high-voltage equipment due to insulation breakdown are associated to the substitution of the asset and to the interruption of service. They can reach millions of dollars in new equipment purchases, fines and civil lawsuits, aggravated by the negative perception of the grid utility. Thus, condition based maintenance techniques are widely applied to have information about the status of the machine or power cable readily available. Partial discharge (PD) measurements are an important tool in the diagnosis of power systems equipment. The presence of PD can accelerate the local degradation of insulation systems and generate premature failures. Conventionally, PD classification is carried out using the phase resolved partial discharge (PRPD) pattern of pulses.en
dc.format.extent12
dc.identifier.bibliographicCitationRobles, G., Parrado-Hernández, E., Ardila-Rey, J., & Martínez-Tarifa, J. M. (2016). Multiple partial discharge source discrimination with multiclass support vector machines. Expert Systems with Applications, 55, 417–428.en
dc.identifier.doihttps://doi.org/10.1016/j.eswa.2016.02.014
dc.identifier.issn0957-4174
dc.identifier.publicationfirstpage417
dc.identifier.publicationlastpage428
dc.identifier.publicationtitleExpert Systems with Applicationsen
dc.identifier.publicationvolume55
dc.identifier.urihttps://hdl.handle.net/10016/35636
dc.identifier.uxxiAR/0000018672
dc.language.isoeng
dc.publisherElsevieren
dc.rights© 2016 Elsevier Ltd. All rights reserveden
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.accessRightsopen accessen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subject.ecienciaIngeniería Mecánicaes
dc.subject.otherSupport vector machineen
dc.subject.otherPartial dischargesen
dc.subject.otherElectric maintenanceen
dc.subject.otherMachine learningen
dc.subject.otherCondition monitoringen
dc.subject.otherRisk assessmenten
dc.subject.otherPattern-recognitionen
dc.subject.otherNeural-networken
dc.subject.otherPd sourcesen
dc.subject.otherClassificationen
dc.subject.otherSeparationen
dc.subject.otherApparatusen
dc.subject.otherVoltageen
dc.titleMultiple partial discharge source discrimination with multiclass support vector machinesen
dc.typeresearch article*
dc.type.hasVersionAM*
dspace.entity.typePublication
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