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dc.contributor.author
de Yong, David Marcelo
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dc.contributor.author
Bhowmik, S.
dc.contributor.author
Magnago, Fernando
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dc.date.available
2018-06-26T19:17:47Z
dc.date.issued
2015-09
dc.identifier.citation
de Yong, David Marcelo; Bhowmik, S.; Magnago, Fernando; An effective power quality classifier using wavelet transform and support vector machines; Pergamon-Elsevier Science Ltd; Expert Systems with Applications; 42; 15-16; 9-2015; 6075-6081
dc.identifier.issn
0957-4174
dc.identifier.uri
http://hdl.handle.net/11336/50134
dc.description.abstract
In this paper we propose a method based on a combination of binary classifiers which are optimized for those special cases where the real signals contain a multitude of events within the analyzed temporal window. These type of events are known as complex events. The proposed Power Quality (PQ) classifier is based on Wavelet Transforms (WT) and Support Vector Machines (SVM). The method uses a One vs. One multiclass SVM. We propose a novel method which is simple, easy to train, and can be implemented with low computational cost. The proposed algorithm consists of a set of simple binary SVM classifiers. Each SVM node is trained separately allowing them to be parallelized. The training stage is performed using single events, however due to the structure of the SVM methodology selected, it allows the system to detect complex events. Tests and training were performed using real complex signals and the results show the proposed methodology to be highly efficient.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Pergamon-Elsevier Science Ltd
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dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.subject
Complex Disturbance Detection And Classification
dc.subject
Power Quality
dc.subject
Support Vector Machine
dc.subject
Wavelet Transform
dc.subject.classification
Ingeniería de Sistemas y Comunicaciones
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dc.subject.classification
Ingeniería Eléctrica, Ingeniería Electrónica e Ingeniería de la Información
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dc.subject.classification
INGENIERÍAS Y TECNOLOGÍAS
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dc.title
An effective power quality classifier using wavelet transform and support vector machines
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2018-06-26T13:41:48Z
dc.journal.volume
42
dc.journal.number
15-16
dc.journal.pagination
6075-6081
dc.journal.pais
Estados Unidos
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dc.description.fil
Fil: de Yong, David Marcelo. Universidad Nacional de Río Cuarto; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
dc.description.fil
Fil: Bhowmik, S.. Nexant; Estados Unidos
dc.description.fil
Fil: Magnago, Fernando. Universidad Nacional de Río Cuarto; Argentina. Nexant; Estados Unidos. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
dc.journal.title
Expert Systems with Applications
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dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1016/j.eswa.2015.04.002
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0957417415002328
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