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dc.contributor.authorPalomares-Salas, José Carlos
dc.contributor.authorGonzález de la Rosa, Juan José
dc.contributor.authorAgüera Pérez, Agustín
dc.contributor.authorMoreno-Muñoz, A.
dc.date.accessioned2017-12-05T12:19:33Z
dc.date.available2017-12-05T12:19:33Z
dc.date.issued2012
dc.identifier.urihttp://hdl.handle.net/10396/15647
dc.description.abstractThis paper considers a few important techniques classification for to identify several power quality disturbances. For this purpose, a process based in HOS has been realized to extract features that help in classification. In this stage the geometrical pattern established via higher-order statistical measurements is obtained, and this pattern is function of the amplitudes and frequencies of the power quality disturbances associated to the 50-Hz power-line. Once the features are managed will be segmented to form training and test sets and them will be applied in the statistical methods used to perform automatic classification of PQ disturbances. The best technique of those compared is selected according to correlation and mistake rates.es_ES
dc.format.mimetypeapplication/pdfes_ES
dc.language.isoenges_ES
dc.publisherSIGMA NOTes_ES
dc.rightshttps://creativecommons.org/licenses/by-nc-nd/4.0/es_ES
dc.sourcePrzegląd Elektrotechniczny 8, 236-243 (2012)es_ES
dc.subjectHigher-Order Statistics (HOS)es_ES
dc.subjectArtificial Neural Networks (ANN)es_ES
dc.subjectPower-Quality (PQ)es_ES
dc.subjectSelf Organizing Maps (SOM)es_ES
dc.subjectTransientses_ES
dc.titleIntelligent Methods for Characterization of Electrical Power Quality Signals using Higher Order Statistical Featureses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.relation.projectIDGobierno de España. TEC2009-08988es_ES
dc.relation.projectIDGobierno de España. TEC2010-19242-C03-03es_ES
dc.relation.projectIDJunta de Andalucía. PAIDI-TIC-168es_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES


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