Fault diagnosis on electrical distribution systems based on fuzzy logic

he occurrence of faults in distribution systems has a negative impact on society, and their effects can be reduced by fast and accurate diagnostic systems that allow to identify, locate, and correct the failures. Since the 1990s, fuzzy logic and other artificial intelligence techniques have been imp...

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Autores:
Perez, Ramon
Inga, Esteban
Aguila, Alexander
Vásquez, Carmen
Chaltel Lima, Liliana A.
Viloria Silva, Amelec Jesus
Maury Ardila, Henry Alfonso
Tipo de recurso:
Article of journal
Fecha de publicación:
2018
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/1775
Acceso en línea:
https://hdl.handle.net/11323/1775
https://repositorio.cuc.edu.co/
Palabra clave:
Distribution systems
Fault location
Fault type
Fuzzy logic
Rights
openAccess
License
Atribución – No comercial – Compartir igual
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dc.title.eng.fl_str_mv Fault diagnosis on electrical distribution systems based on fuzzy logic
title Fault diagnosis on electrical distribution systems based on fuzzy logic
spellingShingle Fault diagnosis on electrical distribution systems based on fuzzy logic
Distribution systems
Fault location
Fault type
Fuzzy logic
title_short Fault diagnosis on electrical distribution systems based on fuzzy logic
title_full Fault diagnosis on electrical distribution systems based on fuzzy logic
title_fullStr Fault diagnosis on electrical distribution systems based on fuzzy logic
title_full_unstemmed Fault diagnosis on electrical distribution systems based on fuzzy logic
title_sort Fault diagnosis on electrical distribution systems based on fuzzy logic
dc.creator.fl_str_mv Perez, Ramon
Inga, Esteban
Aguila, Alexander
Vásquez, Carmen
Chaltel Lima, Liliana A.
Viloria Silva, Amelec Jesus
Maury Ardila, Henry Alfonso
dc.contributor.author.spa.fl_str_mv Perez, Ramon
Inga, Esteban
Aguila, Alexander
Vásquez, Carmen
Chaltel Lima, Liliana A.
Viloria Silva, Amelec Jesus
Maury Ardila, Henry Alfonso
dc.subject.eng.fl_str_mv Distribution systems
Fault location
Fault type
Fuzzy logic
topic Distribution systems
Fault location
Fault type
Fuzzy logic
description he occurrence of faults in distribution systems has a negative impact on society, and their effects can be reduced by fast and accurate diagnostic systems that allow to identify, locate, and correct the failures. Since the 1990s, fuzzy logic and other artificial intelligence techniques have been implemented to identify faults in distribution systems. The main objective of this paper is to perform fault diagnoses based on fuzzy logic. For conducting the study, the IEEE 34-Node Radial Test Feeder is used. The data was obtained from ATPDraw-based fault simulation on different nodes of the circuit considering three different fault resistance values of 0, 5, and 10 ohms. The fuzzy rules to identify the type of fault are defined using the magnitudes of the phase and neutral currents. All measurements are taken at the substation, and the results show that the proposed technique can perfectly identify and locate the type of failure.
publishDate 2018
dc.date.accessioned.none.fl_str_mv 2018-11-23T20:46:20Z
dc.date.available.none.fl_str_mv 2018-11-23T20:46:20Z
dc.date.issued.none.fl_str_mv 2018
dc.type.spa.fl_str_mv Artículo de revista
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_2df8fbb1
dc.type.coar.spa.fl_str_mv http://purl.org/coar/resource_type/c_6501
dc.type.content.spa.fl_str_mv Text
dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/article
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dc.identifier.issn.spa.fl_str_mv 0302-9743
dc.identifier.uri.spa.fl_str_mv https://hdl.handle.net/11323/1775
dc.identifier.instname.spa.fl_str_mv Corporación Universidad de la Costa
dc.identifier.reponame.spa.fl_str_mv REDICUC - Repositorio CUC
dc.identifier.repourl.spa.fl_str_mv https://repositorio.cuc.edu.co/
identifier_str_mv 0302-9743
Corporación Universidad de la Costa
REDICUC - Repositorio CUC
url https://hdl.handle.net/11323/1775
https://repositorio.cuc.edu.co/
dc.language.iso.none.fl_str_mv eng
language eng
dc.rights.spa.fl_str_mv Atribución – No comercial – Compartir igual
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rights_invalid_str_mv Atribución – No comercial – Compartir igual
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.publisher.spa.fl_str_mv Lecture Notes in Computer Science
institution Corporación Universidad de la Costa
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spelling Perez, RamonInga, EstebanAguila, AlexanderVásquez, CarmenChaltel Lima, Liliana A.Viloria Silva, Amelec JesusMaury Ardila, Henry Alfonso2018-11-23T20:46:20Z2018-11-23T20:46:20Z20180302-9743https://hdl.handle.net/11323/1775Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/he occurrence of faults in distribution systems has a negative impact on society, and their effects can be reduced by fast and accurate diagnostic systems that allow to identify, locate, and correct the failures. Since the 1990s, fuzzy logic and other artificial intelligence techniques have been implemented to identify faults in distribution systems. The main objective of this paper is to perform fault diagnoses based on fuzzy logic. For conducting the study, the IEEE 34-Node Radial Test Feeder is used. The data was obtained from ATPDraw-based fault simulation on different nodes of the circuit considering three different fault resistance values of 0, 5, and 10 ohms. The fuzzy rules to identify the type of fault are defined using the magnitudes of the phase and neutral currents. 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