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dc.contributor.author
de Yong, David Marcelo
dc.contributor.author
Bhowmik, Sudipto
dc.contributor.author
Magnago, Fernando
dc.date.available
2019-07-30T13:49:35Z
dc.date.issued
2017-09
dc.identifier.citation
de Yong, David Marcelo; Bhowmik, Sudipto; Magnago, Fernando; Optimized complex power quality classifier using one vs. rest support vector machine; Scientific Research Publishing; Energy and Power Engineering; 09; 10; 9-2017; 568-587
dc.identifier.issn
1947-3818
dc.identifier.uri
http://hdl.handle.net/11336/80557
dc.description.abstract
Nowadays, power quality issues are becoming a significant research topic because of the increasing inclusion of very sensitive devices and considerable renewable energy sources. In general, most of the previous power quality classification techniques focused on single power quality events and did not include an optimal feature selection process. This paper presents a classification system that employs Wavelet Transform and the RMS profile to extract the main features of the measured waveforms containing either single or complex disturbances. A data mining process is designed to select the optimal set of features that better describes each disturbance present in the waveform. Support Vector Machine binary classifiers organized in a ?One Vs Rest? architecture are individually optimized to classify single and complex disturbances. The parameters that rule the performance of each binary classifier are also individually adjusted using a grid search algorithm that helps them achieve optimal performance. This specialized process significantly improves the total classification accuracy. Several single and complex disturbances were simulated in order to train and test the algorithm. The results show that the classifier is capable of identifying >99% of single disturbances and >97% of complex disturbances.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Scientific Research Publishing
dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
Complex Power Quality
dc.subject
Pattern Recognition
dc.subject
Support Vector Machine
dc.subject
Wavelet Transform
dc.subject.classification
Ingeniería Eléctrica y Electrónica
dc.subject.classification
Ingeniería Eléctrica, Ingeniería Electrónica e Ingeniería de la Información
dc.subject.classification
INGENIERÍAS Y TECNOLOGÍAS
dc.title
Optimized complex power quality classifier using one vs. rest support vector machine
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2019-04-15T18:26:38Z
dc.identifier.eissn
1947-3818
dc.journal.volume
09
dc.journal.number
10
dc.journal.pagination
568-587
dc.journal.pais
República de China
dc.description.fil
Fil: de Yong, David Marcelo. Universidad Nacional de Río Cuarto. Facultad de Ingeniería. Departamento de Electricidad y Electrónica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; Argentina
dc.description.fil
Fil: Bhowmik, Sudipto. Nexant Inc; Estados Unidos
dc.description.fil
Fil: Magnago, Fernando. Universidad Nacional de Río Cuarto. Facultad de Ingeniería. Departamento de Electricidad y Electrónica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; Argentina
dc.journal.title
Energy and Power Engineering
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/http://www.scirp.org/journal/paperinformation.aspx?paperid=79011
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/https://doi.org/10.4236/epe.2017.910040
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