dc.creatorGarrido Arévalo, Víctor Manuel
dc.creatorGil-González, Walter
dc.creatorHolguín, M.
dc.date.accessioned2020-11-04T20:17:52Z
dc.date.available2020-11-04T20:17:52Z
dc.date.created2020-11-04T20:17:52Z
dc.date.issued2019-09-24
dc.identifierGarrido-Arévalo, V., Gil-González, W. and Holguin, M., 2020. Power quality detection and classification using wavelet and support vector machine. Journal of Physics: Conference Series, 1448, p.012002.
dc.identifierhttps://hdl.handle.net/20.500.12585/9530
dc.identifierhttps://iopscience.iop.org/article/10.1088/1742-6596/1448/1/012002/meta
dc.identifier448 (2020) 10.1088/1742-6596/1448/1/012002
dc.identifierUniversidad Tecnológica de Bolívar
dc.identifierRepositorio Universidad Tecnológica de Bolívar
dc.description.abstractThis work presents the identification and classification of various disturbances that affect the quality of energy, seen as the quality of the voltage wave (harmonics, sag, swell and flicker). For this, the wavelet transform is used, which allows to have characteristic patterns as input signals of the support vector machine, these are evaluated in their different configurations, bi-class, minimum output coding, error correcting output and one versus all. For all of them, in the first instance they were trained with 200 samples, then the results were validated with 100 samples and finally the evaluation was made with 500 different samples, obtaining that the best result is presented with the minimum output coding configuration.
dc.languageeng
dc.publisherCartagena de Indias
dc.rightshttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional
dc.sourceJournal of Physics: Conference Series, Volume 1448
dc.titlePower quality detection and classification using wavelet and support vector machine


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