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dc.creatorBorrás-Talavera, María Doloreses
dc.creatorCastilla Ibáñez, Manueles
dc.creatorMoreno-Alfonso, Narcisoes
dc.creatorMontaño Asquerino, Juan-Carloses
dc.date.accessioned2018-02-13T08:20:59Z
dc.date.available2018-02-13T08:20:59Z
dc.date.issued2001
dc.identifier.citationBorrás-Talavera, M.D., Castilla Ibáñez, M., Moreno-Alfonso, N. y Montaño Asquerino, J. (2001). Wavelet and Neural Structure: A New Tool for Diagnostic of Power System Disturbances. IEEE Transactions on Industry Applications, 37 (1), 184-190.
dc.identifier.citationBorrás Talavera, M.D., Castilla Ibáñez, M., Moreno-Alfonso, N. y Montaño, J. (2001). Wavelet and Neural Structure: A New Tool for Diagnostic of Power System Disturbances. IEEE Transactions on Industry Applications, 37 (1), 184-190.
dc.identifier.issn0093-9994es
dc.identifier.issn1939-9367es
dc.identifier.urihttps://hdl.handle.net/11441/70237
dc.description.abstractThe Fourier transform can be used for analysis of nonstationary signals, but the Fourier spectrum does not provide any time-domain information about the signal. When the time localization of the spectral components is needed, a wavelet transform giving the time-frequency representation of the signal must be used. In this paper, using wavelet analysis and neural systems as a new tool for the analysis of power system disturbances, disturbances are automatically detected, compacted, and classified. An example showing the potential of these techniques for diagnosis of actual power system disturbances is presented.es
dc.formatapplication/pdfes
dc.language.isoenges
dc.publisherInstitute of Electrical and Electronics Engineerses
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectHarmonic distortiones
dc.subjectNeural networkses
dc.subjectSignal analysises
dc.subjectTransformses
dc.subjectWaveletses
dc.titleWavelet and Neural Structure: A New Tool for Diagnostic of Power System Disturbanceses
dc.typeinfo:eu-repo/semantics/articlees
dcterms.identifierhttps://ror.org/03yxnpp24
dc.type.versioninfo:eu-repo/semantics/acceptedVersiones
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.contributor.affiliationUniversidad de Sevilla. Departamento de Ingeniería Eléctricaes
dc.relation.publisherversionhttp://ieeexplore.ieee.org/xpl/tocresult.jsp?isnumber=19538es
dc.identifier.doi10.1109/28.903145es
dc.contributor.groupUniversidad de Sevilla. TEP175: Grupo de Investigación en Ingeniería Eléctrica (Invespot)es
idus.format.extent7 p.es
idus.validador.notaPostprint enviado por uno de los autoreses
dc.journaltitleIEEE Transactions on Industry Applicationses
dc.publication.volumen37es
dc.publication.issue1es
dc.publication.initialPage184es
dc.publication.endPage190es
dc.identifier.sisius6680917es

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