An artificial immune system with continuous-learning for voltage disturbance diagnosis in electrical distribution systems
Autor(a) principal: | |
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Data de Publicação: | 2016 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1016/j.eswa.2016.03.010 http://hdl.handle.net/11449/172756 |
Resumo: | This paper presents a new artificial immune algorithm with continuous-learning, which is inspired by the biological immune system, to realize the voltage diagnosis in electrical distribution systems. This conception allows one to compose a diagnosis system that can continuously learn without reinitialization when new disturbances occur due to the evolution of the electrical system. Two artificial immune algorithms, which are the negative selection algorithm and the clonal selection algorithm, are used for the pattern recognition process and the learning process, respectively. The principal application of this new method aids the operation during failures, supervises the protection system, and can evolve with the power systems to continuously acquire new knowledge. This new methodology has a direct impact in the area of diagnosis in electrical systems, as well as, in the pattern recognition problem, because the main contribution and novelty of this method is the continuous learning capability, which enables the system to learn unknown patterns without having to restart the knowledge. This is the major advantage of this methodology. To evaluate the efficiency and performance of this new method, failure simulations were performed in a real distribution system with 134 buses using the EMTP software. The results show robustness and efficiency. |
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Repositório Institucional da UNESP |
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An artificial immune system with continuous-learning for voltage disturbance diagnosis in electrical distribution systemsArtificial immune systemsClonal selection algorithmContinuous-learningDiagnosisElectrical distribution systemsNegative selection algorithmVoltage disturbancesThis paper presents a new artificial immune algorithm with continuous-learning, which is inspired by the biological immune system, to realize the voltage diagnosis in electrical distribution systems. This conception allows one to compose a diagnosis system that can continuously learn without reinitialization when new disturbances occur due to the evolution of the electrical system. Two artificial immune algorithms, which are the negative selection algorithm and the clonal selection algorithm, are used for the pattern recognition process and the learning process, respectively. The principal application of this new method aids the operation during failures, supervises the protection system, and can evolve with the power systems to continuously acquire new knowledge. This new methodology has a direct impact in the area of diagnosis in electrical systems, as well as, in the pattern recognition problem, because the main contribution and novelty of this method is the continuous learning capability, which enables the system to learn unknown patterns without having to restart the knowledge. This is the major advantage of this methodology. To evaluate the efficiency and performance of this new method, failure simulations were performed in a real distribution system with 134 buses using the EMTP software. The results show robustness and efficiency.Electrical Engineering Department Faculty of Engineering of Ilha Solteira (FEIS) UNESP Univ Estadual Paulista Júlio de Mesquita Filho, Av. Brasil 56, PO Box 31Electrical Engineering Department Faculty of Engineering of Ilha Solteira (FEIS) UNESP Univ Estadual Paulista Júlio de Mesquita Filho, Av. Brasil 56, PO Box 31Universidade Estadual Paulista (Unesp)Lima, Fernando P.A. [UNESP]Lopes, Mara L.M. [UNESP]Lotufo, Anna Diva P. [UNESP]Minussi, Carlos R. [UNESP]2018-12-11T17:02:03Z2018-12-11T17:02:03Z2016-09-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article131-142application/pdfhttp://dx.doi.org/10.1016/j.eswa.2016.03.010Expert Systems with Applications, v. 56, p. 131-142.0957-4174http://hdl.handle.net/11449/17275610.1016/j.eswa.2016.03.0102-s2.0-849621974302-s2.0-84962197430.pdfScopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengExpert Systems with Applications1,271info:eu-repo/semantics/openAccess2023-10-01T06:05:56Zoai:repositorio.unesp.br:11449/172756Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462023-10-01T06:05:56Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
An artificial immune system with continuous-learning for voltage disturbance diagnosis in electrical distribution systems |
title |
An artificial immune system with continuous-learning for voltage disturbance diagnosis in electrical distribution systems |
spellingShingle |
An artificial immune system with continuous-learning for voltage disturbance diagnosis in electrical distribution systems Lima, Fernando P.A. [UNESP] Artificial immune systems Clonal selection algorithm Continuous-learning Diagnosis Electrical distribution systems Negative selection algorithm Voltage disturbances |
title_short |
An artificial immune system with continuous-learning for voltage disturbance diagnosis in electrical distribution systems |
title_full |
An artificial immune system with continuous-learning for voltage disturbance diagnosis in electrical distribution systems |
title_fullStr |
An artificial immune system with continuous-learning for voltage disturbance diagnosis in electrical distribution systems |
title_full_unstemmed |
An artificial immune system with continuous-learning for voltage disturbance diagnosis in electrical distribution systems |
title_sort |
An artificial immune system with continuous-learning for voltage disturbance diagnosis in electrical distribution systems |
author |
Lima, Fernando P.A. [UNESP] |
author_facet |
Lima, Fernando P.A. [UNESP] Lopes, Mara L.M. [UNESP] Lotufo, Anna Diva P. [UNESP] Minussi, Carlos R. [UNESP] |
author_role |
author |
author2 |
Lopes, Mara L.M. [UNESP] Lotufo, Anna Diva P. [UNESP] Minussi, Carlos R. [UNESP] |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) |
dc.contributor.author.fl_str_mv |
Lima, Fernando P.A. [UNESP] Lopes, Mara L.M. [UNESP] Lotufo, Anna Diva P. [UNESP] Minussi, Carlos R. [UNESP] |
dc.subject.por.fl_str_mv |
Artificial immune systems Clonal selection algorithm Continuous-learning Diagnosis Electrical distribution systems Negative selection algorithm Voltage disturbances |
topic |
Artificial immune systems Clonal selection algorithm Continuous-learning Diagnosis Electrical distribution systems Negative selection algorithm Voltage disturbances |
description |
This paper presents a new artificial immune algorithm with continuous-learning, which is inspired by the biological immune system, to realize the voltage diagnosis in electrical distribution systems. This conception allows one to compose a diagnosis system that can continuously learn without reinitialization when new disturbances occur due to the evolution of the electrical system. Two artificial immune algorithms, which are the negative selection algorithm and the clonal selection algorithm, are used for the pattern recognition process and the learning process, respectively. The principal application of this new method aids the operation during failures, supervises the protection system, and can evolve with the power systems to continuously acquire new knowledge. This new methodology has a direct impact in the area of diagnosis in electrical systems, as well as, in the pattern recognition problem, because the main contribution and novelty of this method is the continuous learning capability, which enables the system to learn unknown patterns without having to restart the knowledge. This is the major advantage of this methodology. To evaluate the efficiency and performance of this new method, failure simulations were performed in a real distribution system with 134 buses using the EMTP software. The results show robustness and efficiency. |
publishDate |
2016 |
dc.date.none.fl_str_mv |
2016-09-01 2018-12-11T17:02:03Z 2018-12-11T17:02:03Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1016/j.eswa.2016.03.010 Expert Systems with Applications, v. 56, p. 131-142. 0957-4174 http://hdl.handle.net/11449/172756 10.1016/j.eswa.2016.03.010 2-s2.0-84962197430 2-s2.0-84962197430.pdf |
url |
http://dx.doi.org/10.1016/j.eswa.2016.03.010 http://hdl.handle.net/11449/172756 |
identifier_str_mv |
Expert Systems with Applications, v. 56, p. 131-142. 0957-4174 10.1016/j.eswa.2016.03.010 2-s2.0-84962197430 2-s2.0-84962197430.pdf |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Expert Systems with Applications 1,271 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
131-142 application/pdf |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1799964392172814336 |