Educational software for power quality analysis

Autores
de Yong, David Marcelo; Reineri, Claudio Ariel; Magnago, Fernando
Año de publicación
2013
Idioma
español castellano
Tipo de recurso
artículo
Estado
versión publicada
Descripción
This paper presents educational software that allows users to generate, detect and classify electrical power disturbance signals using a Wavelet Transform and Neural Networks based algorithm. This software includes four main modules: a) Signal Acquisition Module that allows the incorporation of waveforms stored in a data base; b) Generation Module which permits the generation of diverse disturbed waveforms; c) Detections Module provides tools to analyze different disturbance detection algorithms and d) Classification Module that determines the disturbance type using different pattern classification methods. © 2003-2012 IEEE.
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
Fil: Reineri, Claudio Ariel. Universidad Nacional de Río Cuarto; Argentina
Fil: Magnago, Fernando. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; Argentina. Universidad Nacional de Río Cuarto. Facultad de Ingeniería. Departamento de Electricidad y Electrónica; Argentina
Materia
Power Quality
Simulation Softwar
Neural Network
Wavelet Transform
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/82252
Acceder
id CONICETDig_5e01f2ac144b44c8143e92be7b27ae00
oai_identifier_str oai:ri.conicet.gov.ar:11336/82252
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Educational software for power quality analysisde Yong, David MarceloReineri, Claudio ArielMagnago, FernandoPower QualitySimulation SoftwarNeural NetworkWavelet Transformhttps://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2This paper presents educational software that allows users to generate, detect and classify electrical power disturbance signals using a Wavelet Transform and Neural Networks based algorithm. This software includes four main modules: a) Signal Acquisition Module that allows the incorporation of waveforms stored in a data base; b) Generation Module which permits the generation of diverse disturbed waveforms; c) Detections Module provides tools to analyze different disturbance detection algorithms and d) Classification Module that determines the disturbance type using different pattern classification methods. © 2003-2012 IEEE.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; ArgentinaFil: Reineri, Claudio Ariel. Universidad Nacional de Río Cuarto; ArgentinaFil: Magnago, Fernando. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; Argentina. Universidad Nacional de Río Cuarto. Facultad de Ingeniería. Departamento de Electricidad y Electrónica; ArgentinaInstitute of Electrical and Electronics Engineers2013-04-23info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/82252de Yong, David Marcelo; Reineri, Claudio Ariel; Magnago, Fernando; Educational software for power quality analysis; Institute of Electrical and Electronics Engineers; IEEE Latin America Transactions; 11; 1; 23-4-2013; 479-4851548-0992CONICET DigitalCONICETspainfo:eu-repo/semantics/altIdentifier/url/http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6502849info:eu-repo/semantics/altIdentifier/doi/info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-11-12T09:40:42Zoai:ri.conicet.gov.ar:11336/82252instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-11-12 09:40:42.649CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Educational software for power quality analysis
title Educational software for power quality analysis
spellingShingle Educational software for power quality analysis
de Yong, David Marcelo
Power Quality
Simulation Softwar
Neural Network
Wavelet Transform
title_short Educational software for power quality analysis
title_full Educational software for power quality analysis
title_fullStr Educational software for power quality analysis
title_full_unstemmed Educational software for power quality analysis
title_sort Educational software for power quality analysis
dc.creator.none.fl_str_mv de Yong, David Marcelo
Reineri, Claudio Ariel
Magnago, Fernando
author de Yong, David Marcelo
author_facet de Yong, David Marcelo
Reineri, Claudio Ariel
Magnago, Fernando
author_role author
author2 Reineri, Claudio Ariel
Magnago, Fernando
author2_role author
author
dc.subject.none.fl_str_mv Power Quality
Simulation Softwar
Neural Network
Wavelet Transform
topic Power Quality
Simulation Softwar
Neural Network
Wavelet Transform
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.2
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv This paper presents educational software that allows users to generate, detect and classify electrical power disturbance signals using a Wavelet Transform and Neural Networks based algorithm. This software includes four main modules: a) Signal Acquisition Module that allows the incorporation of waveforms stored in a data base; b) Generation Module which permits the generation of diverse disturbed waveforms; c) Detections Module provides tools to analyze different disturbance detection algorithms and d) Classification Module that determines the disturbance type using different pattern classification methods. © 2003-2012 IEEE.
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
Fil: Reineri, Claudio Ariel. Universidad Nacional de Río Cuarto; Argentina
Fil: Magnago, Fernando. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; Argentina. Universidad Nacional de Río Cuarto. Facultad de Ingeniería. Departamento de Electricidad y Electrónica; Argentina
description This paper presents educational software that allows users to generate, detect and classify electrical power disturbance signals using a Wavelet Transform and Neural Networks based algorithm. This software includes four main modules: a) Signal Acquisition Module that allows the incorporation of waveforms stored in a data base; b) Generation Module which permits the generation of diverse disturbed waveforms; c) Detections Module provides tools to analyze different disturbance detection algorithms and d) Classification Module that determines the disturbance type using different pattern classification methods. © 2003-2012 IEEE.
publishDate 2013
dc.date.none.fl_str_mv 2013-04-23
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/82252
de Yong, David Marcelo; Reineri, Claudio Ariel; Magnago, Fernando; Educational software for power quality analysis; Institute of Electrical and Electronics Engineers; IEEE Latin America Transactions; 11; 1; 23-4-2013; 479-485
1548-0992
CONICET Digital
CONICET
url http://hdl.handle.net/11336/82252
identifier_str_mv de Yong, David Marcelo; Reineri, Claudio Ariel; Magnago, Fernando; Educational software for power quality analysis; Institute of Electrical and Electronics Engineers; IEEE Latin America Transactions; 11; 1; 23-4-2013; 479-485
1548-0992
CONICET Digital
CONICET
dc.language.none.fl_str_mv spa
language spa
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6502849
info:eu-repo/semantics/altIdentifier/doi/
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers
publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1848597525677735936
score 12.976206

Publicaciones similares