Um novo conceito de inteligência artificial baseado nas redes da lógica paraconsistente anotada de dois-valores para tratamento de incertezas na estimativa de seção em falta do sistema elétrico de potência
Ano de defesa: | 2022 |
---|---|
Autor(a) principal: | |
Orientador(a): | |
Banca de defesa: | , , , |
Tipo de documento: | Tese |
Tipo de acesso: | Acesso aberto |
Idioma: | por |
Instituição de defesa: |
Universidade Federal de Santa Maria
Centro de Tecnologia |
Programa de Pós-Graduação: |
Programa de Pós-Graduação em Engenharia Elétrica
|
Departamento: |
Engenharia Elétrica
|
País: |
Brasil
|
Palavras-chave em Português: | |
Palavras-chave em Inglês: | |
Área do conhecimento CNPq: | |
Link de acesso: | http://repositorio.ufsm.br/handle/1/26132 |
Resumo: | This thesis proposes a novel concept of artificial intelligence based on a contemporary and non-classical logic called Paraconsistent Annotated Logic of two-values (PAL2v) applied on fault section estimation in an electric power system. The fault section estimation is a decision-making problem because, under abnormal electrical operating conditions, a large volume of alarms are generated in a short time. It is up to the control center operator to make the most appropriate decision to isolate the fault section. In this context, the importance of this work is to develop a methodology that aids the operator in decision making in stressful situations. In addition, considering that there are no reports in the literature of LPA2v for fault section estimation, the work contributes to the innovative aspect. Its innovation lies on the problem´s approach, since unlike conventional methodologies that establish binary solutions (fault section is 1 and not fault is 0), LPA2v admits uncertainties in its own decision making. Likewise, it is possible to ensure higher reliability in the solutions passed to the operator, because in the presence of an uncertain solution, it avoids hasty decisions in binary 1. LPA2v is evidence-based, and the more evidence it obtains for a fault scenario, the more equitable the diagnoses/solutions will be. To extract the evidence, three heuristic functions were used that employ inference rules using reported alarms from Supervisory Control and Data Acquisition (SCADA). The use of three functions avoids dependence on probabilistic or empirical values. Furthermore, two paraconsistent networks were developed, both extracting evidence from the heuristic functions. The first paraconsistent analysis network call with LPA2v was tested on a 138/203 kV subsystem in southern Brazil and its results were compared with exact mathematical models to solution optimizations. The second analytical paraconsistent artificial neural network call was tested in a 345 Kv transmission system and was compared with three propositional logic models that incorporate fuzzy interval values and spiking neural network. Finally, both paraconsistent networks showed: 1- robustness in the comparisons of results, because in cases of false and failed alarms, they detected uncertain solutions as expected; 2- easy implementation in different electrical systems, as it does not require training and elaborate construction of rules or pattern; 3- intuitiveness in estimating faults in the SEP, as it goes beyond methods that offer conventional solutions (0 or 1) for the system operator. |
id |
UFSM-20_967004c3bcf76679ca763a9b26358bff |
---|---|
oai_identifier_str |
oai:repositorio.ufsm.br:1/26132 |
network_acronym_str |
UFSM-20 |
network_name_str |
Manancial - Repositório Digital da UFSM |
repository_id_str |
|
spelling |
2022-09-13T11:20:10Z2022-09-13T11:20:10Z2022-07-26http://repositorio.ufsm.br/handle/1/26132This thesis proposes a novel concept of artificial intelligence based on a contemporary and non-classical logic called Paraconsistent Annotated Logic of two-values (PAL2v) applied on fault section estimation in an electric power system. The fault section estimation is a decision-making problem because, under abnormal electrical operating conditions, a large volume of alarms are generated in a short time. It is up to the control center operator to make the most appropriate decision to isolate the fault section. In this context, the importance of this work is to develop a methodology that aids the operator in decision making in stressful situations. In addition, considering that there are no reports in the literature of LPA2v for fault section estimation, the work contributes to the innovative aspect. Its innovation lies on the problem´s approach, since unlike conventional methodologies that establish binary solutions (fault section is 1 and not fault is 0), LPA2v admits uncertainties in its own decision making. Likewise, it is possible to ensure higher reliability in the solutions passed to the operator, because in the presence of an uncertain solution, it avoids hasty decisions in binary 1. LPA2v is evidence-based, and the more evidence it obtains for a fault scenario, the more equitable the diagnoses/solutions will be. To extract the evidence, three heuristic functions were used that employ inference rules using reported alarms from Supervisory Control and Data Acquisition (SCADA). The use of three functions avoids dependence on probabilistic or empirical values. Furthermore, two paraconsistent networks were developed, both extracting evidence from the heuristic functions. The first paraconsistent analysis network call with LPA2v was tested on a 138/203 kV subsystem in southern Brazil and its results were compared with exact mathematical models to solution optimizations. The second analytical paraconsistent artificial neural network call was tested in a 345 Kv transmission system and was compared with three propositional logic models that incorporate fuzzy interval values and spiking neural network. Finally, both paraconsistent networks showed: 1- robustness in the comparisons of results, because in cases of false and failed alarms, they detected uncertain solutions as expected; 2- easy implementation in different electrical systems, as it does not require training and elaborate construction of rules or pattern; 3- intuitiveness in estimating faults in the SEP, as it goes beyond methods that offer conventional solutions (0 or 1) for the system operator.Esta tese propõe um novo conceito de inteligência artificial baseada em uma lógica contemporânea e não clássica chamada de lógica paraconsistente anotada de dois valores (LPA2v) aplicada para estimativa de seção em falta no sistema elétrico de potência. A estimativa de uma seção em falta é um problema de tomada de decisão, pois, em condições elétricas anormais, um grande volume de alarmes é gerado em um curto espaço de tempo. Cabe ao operador do centro de controle tomar a decisão mais adequada para isolar a seção em falta. Neste contexto, a importância deste trabalho é desenvolver uma metodologia que auxilie o operador na tomada de decisões em situações de estresse. Além disso, considerando que não há relatos na literatura da LPA2v para estimativa de seção em falta, o trabalho contribui no aspecto inovador. Sua inovação está na abordagem do problema porque, ao contrário das metodologias convencionais que estabelecem soluções binárias (seção em falta é 1 e não falta é 0), a LPA2v admite incertezas na sua própria tomada de decisão. Desta forma, é possível garantir uma maior confiabilidade nas soluções repassadas para o operador pois, na existência de uma solução incerta, evita decisões precipitadas no binário 1. A LPA2v é fundamentada em evidências e, quanto mais evidências obtiver em um cenário de falta, mais equânimes serão os diagnósticos/soluções. Para extração das evidências, foram utilizadas três funções heurísticas que empregam regras de inferências a partir dos alarmes reportados no sistema de supervisão e aquisição de dados (SCADA). O uso de três funções evita a dependência de valores probabilísticos ou empíricos. Outrossim, foram desenvolvidas duas redes paraconsistentes, ambas extraindo evidências das três funções heurísticas. A primeira chamada de rede de análise paraconsistente com LPA2v foi testada em um subsistema de 138/203 kV da região sul do Brasil e seus resultados foram comparados com modelo de métodos exatos para otimizar soluções. A segunda chamada de rede neural artificial paraconsistente analítica foi testada em sistema de transmissão de 345 kv e foi comparada com três modelos de lógicas proposicionais que incorporam valores de intervalos fuzzy e redes neurais pulsante. Por fim, ambas as redes paraconsistentes apresentaram: 1- robustez e resiliência nos resultados, pois nos casos de falta com alarmes falsos e falhos, detectaram soluções incertas como era de se esperar; 2- fácil implementação em sistemas elétricos distintos, pois dispensa treinamentos e construções elaboradas das regras de inferências ou padrões; 3- intuitividade na estimação de falta no SEP, pois vai além dos métodos que oferecem soluções convencionais (0 ou 1) para o operador do sistema.porUniversidade Federal de Santa MariaCentro de TecnologiaPrograma de Pós-Graduação em Engenharia ElétricaUFSMBrasilEngenharia ElétricaAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessRede de análise paraconsistenteRede neural paraconsistenteEstimação da seção em faltaDiagnóstico de faltaProteção de sistemas elétricosIncertezaParaconsistent analysis networkParaconsistent neural networkFault section estimationFault diagnosisProtection of electrical systemsUncertaintyCNPQ::ENGENHARIAS::ENGENHARIA ELETRICAUm novo conceito de inteligência artificial baseado nas redes da lógica paraconsistente anotada de dois-valores para tratamento de incertezas na estimativa de seção em falta do sistema elétrico de potênciaA novel concept of artificial intelligence based on paraconsistent annotated logic of two-values networks for treatment of uncertainty in the fault section estimation in an electrical power systeminfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesisCardoso Junior, Ghendyhttp://lattes.cnpq.br/6284386218725402Oliveira, Aécio de LimaRicciotti, Antônio Carlos DuarteFritzen, Paulo CíceroGuarda, Fernando Guilherme Kaehlerhttp://lattes.cnpq.br/9307581340965790Ribeiro, Julio Cesar300400000007600600600600600600600eed4f55e-1a74-47ce-afc0-4daa8655d8a3713e93e2-4718-4f1f-ba7c-f202778ba0d0b325897b-e199-425a-9d1d-34f3c1e4e8c1e772442e-777a-4050-b4c3-0ce530c4ef5ea9e9a4d7-8bdf-4a6a-bcd1-3570def02ee9bfacb786-1d04-4eba-a63a-274df0761458reponame:Manancial - Repositório Digital da UFSMinstname:Universidade Federal de Santa Maria (UFSM)instacron:UFSMCC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8805http://repositorio.ufsm.br/bitstream/1/26132/2/license_rdf4460e5956bc1d1639be9ae6146a50347MD52LICENSElicense.txtlicense.txttext/plain; charset=utf-81956http://repositorio.ufsm.br/bitstream/1/26132/3/license.txt2f0571ecee68693bd5cd3f17c1e075dfMD53ORIGINALTES_PPGEE_2022_RIBEIRO_JULIO.pdfTES_PPGEE_2022_RIBEIRO_JULIO.pdfTeseapplication/pdf3649525http://repositorio.ufsm.br/bitstream/1/26132/1/TES_PPGEE_2022_RIBEIRO_JULIO.pdf4fd3e3c43eadce71870445e4d10c973fMD511/261322022-09-13 08:20:10.91oai:repositorio.ufsm.br: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ório Institucionalhttp://repositorio.ufsm.br/PUBhttp://repositorio.ufsm.br/oai/requestopendoar:39132022-09-13T11:20:10Manancial - Repositório Digital da UFSM - Universidade Federal de Santa Maria (UFSM)false |
dc.title.por.fl_str_mv |
Um novo conceito de inteligência artificial baseado nas redes da lógica paraconsistente anotada de dois-valores para tratamento de incertezas na estimativa de seção em falta do sistema elétrico de potência |
dc.title.alternative.eng.fl_str_mv |
A novel concept of artificial intelligence based on paraconsistent annotated logic of two-values networks for treatment of uncertainty in the fault section estimation in an electrical power system |
title |
Um novo conceito de inteligência artificial baseado nas redes da lógica paraconsistente anotada de dois-valores para tratamento de incertezas na estimativa de seção em falta do sistema elétrico de potência |
spellingShingle |
Um novo conceito de inteligência artificial baseado nas redes da lógica paraconsistente anotada de dois-valores para tratamento de incertezas na estimativa de seção em falta do sistema elétrico de potência Ribeiro, Julio Cesar Rede de análise paraconsistente Rede neural paraconsistente Estimação da seção em falta Diagnóstico de falta Proteção de sistemas elétricos Incerteza Paraconsistent analysis network Paraconsistent neural network Fault section estimation Fault diagnosis Protection of electrical systems Uncertainty CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA |
title_short |
Um novo conceito de inteligência artificial baseado nas redes da lógica paraconsistente anotada de dois-valores para tratamento de incertezas na estimativa de seção em falta do sistema elétrico de potência |
title_full |
Um novo conceito de inteligência artificial baseado nas redes da lógica paraconsistente anotada de dois-valores para tratamento de incertezas na estimativa de seção em falta do sistema elétrico de potência |
title_fullStr |
Um novo conceito de inteligência artificial baseado nas redes da lógica paraconsistente anotada de dois-valores para tratamento de incertezas na estimativa de seção em falta do sistema elétrico de potência |
title_full_unstemmed |
Um novo conceito de inteligência artificial baseado nas redes da lógica paraconsistente anotada de dois-valores para tratamento de incertezas na estimativa de seção em falta do sistema elétrico de potência |
title_sort |
Um novo conceito de inteligência artificial baseado nas redes da lógica paraconsistente anotada de dois-valores para tratamento de incertezas na estimativa de seção em falta do sistema elétrico de potência |
author |
Ribeiro, Julio Cesar |
author_facet |
Ribeiro, Julio Cesar |
author_role |
author |
dc.contributor.advisor1.fl_str_mv |
Cardoso Junior, Ghendy |
dc.contributor.advisor1Lattes.fl_str_mv |
http://lattes.cnpq.br/6284386218725402 |
dc.contributor.referee1.fl_str_mv |
Oliveira, Aécio de Lima |
dc.contributor.referee2.fl_str_mv |
Ricciotti, Antônio Carlos Duarte |
dc.contributor.referee3.fl_str_mv |
Fritzen, Paulo Cícero |
dc.contributor.referee4.fl_str_mv |
Guarda, Fernando Guilherme Kaehler |
dc.contributor.authorLattes.fl_str_mv |
http://lattes.cnpq.br/9307581340965790 |
dc.contributor.author.fl_str_mv |
Ribeiro, Julio Cesar |
contributor_str_mv |
Cardoso Junior, Ghendy Oliveira, Aécio de Lima Ricciotti, Antônio Carlos Duarte Fritzen, Paulo Cícero Guarda, Fernando Guilherme Kaehler |
dc.subject.por.fl_str_mv |
Rede de análise paraconsistente Rede neural paraconsistente Estimação da seção em falta Diagnóstico de falta Proteção de sistemas elétricos Incerteza |
topic |
Rede de análise paraconsistente Rede neural paraconsistente Estimação da seção em falta Diagnóstico de falta Proteção de sistemas elétricos Incerteza Paraconsistent analysis network Paraconsistent neural network Fault section estimation Fault diagnosis Protection of electrical systems Uncertainty CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA |
dc.subject.eng.fl_str_mv |
Paraconsistent analysis network Paraconsistent neural network Fault section estimation Fault diagnosis Protection of electrical systems Uncertainty |
dc.subject.cnpq.fl_str_mv |
CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA |
description |
This thesis proposes a novel concept of artificial intelligence based on a contemporary and non-classical logic called Paraconsistent Annotated Logic of two-values (PAL2v) applied on fault section estimation in an electric power system. The fault section estimation is a decision-making problem because, under abnormal electrical operating conditions, a large volume of alarms are generated in a short time. It is up to the control center operator to make the most appropriate decision to isolate the fault section. In this context, the importance of this work is to develop a methodology that aids the operator in decision making in stressful situations. In addition, considering that there are no reports in the literature of LPA2v for fault section estimation, the work contributes to the innovative aspect. Its innovation lies on the problem´s approach, since unlike conventional methodologies that establish binary solutions (fault section is 1 and not fault is 0), LPA2v admits uncertainties in its own decision making. Likewise, it is possible to ensure higher reliability in the solutions passed to the operator, because in the presence of an uncertain solution, it avoids hasty decisions in binary 1. LPA2v is evidence-based, and the more evidence it obtains for a fault scenario, the more equitable the diagnoses/solutions will be. To extract the evidence, three heuristic functions were used that employ inference rules using reported alarms from Supervisory Control and Data Acquisition (SCADA). The use of three functions avoids dependence on probabilistic or empirical values. Furthermore, two paraconsistent networks were developed, both extracting evidence from the heuristic functions. The first paraconsistent analysis network call with LPA2v was tested on a 138/203 kV subsystem in southern Brazil and its results were compared with exact mathematical models to solution optimizations. The second analytical paraconsistent artificial neural network call was tested in a 345 Kv transmission system and was compared with three propositional logic models that incorporate fuzzy interval values and spiking neural network. Finally, both paraconsistent networks showed: 1- robustness in the comparisons of results, because in cases of false and failed alarms, they detected uncertain solutions as expected; 2- easy implementation in different electrical systems, as it does not require training and elaborate construction of rules or pattern; 3- intuitiveness in estimating faults in the SEP, as it goes beyond methods that offer conventional solutions (0 or 1) for the system operator. |
publishDate |
2022 |
dc.date.accessioned.fl_str_mv |
2022-09-13T11:20:10Z |
dc.date.available.fl_str_mv |
2022-09-13T11:20:10Z |
dc.date.issued.fl_str_mv |
2022-07-26 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/doctoralThesis |
format |
doctoralThesis |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://repositorio.ufsm.br/handle/1/26132 |
url |
http://repositorio.ufsm.br/handle/1/26132 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.cnpq.fl_str_mv |
300400000007 |
dc.relation.confidence.fl_str_mv |
600 600 600 600 600 600 600 |
dc.relation.authority.fl_str_mv |
eed4f55e-1a74-47ce-afc0-4daa8655d8a3 713e93e2-4718-4f1f-ba7c-f202778ba0d0 b325897b-e199-425a-9d1d-34f3c1e4e8c1 e772442e-777a-4050-b4c3-0ce530c4ef5e a9e9a4d7-8bdf-4a6a-bcd1-3570def02ee9 bfacb786-1d04-4eba-a63a-274df0761458 |
dc.rights.driver.fl_str_mv |
Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ |
eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
Universidade Federal de Santa Maria Centro de Tecnologia |
dc.publisher.program.fl_str_mv |
Programa de Pós-Graduação em Engenharia Elétrica |
dc.publisher.initials.fl_str_mv |
UFSM |
dc.publisher.country.fl_str_mv |
Brasil |
dc.publisher.department.fl_str_mv |
Engenharia Elétrica |
publisher.none.fl_str_mv |
Universidade Federal de Santa Maria Centro de Tecnologia |
dc.source.none.fl_str_mv |
reponame:Manancial - Repositório Digital da UFSM instname:Universidade Federal de Santa Maria (UFSM) instacron:UFSM |
instname_str |
Universidade Federal de Santa Maria (UFSM) |
instacron_str |
UFSM |
institution |
UFSM |
reponame_str |
Manancial - Repositório Digital da UFSM |
collection |
Manancial - Repositório Digital da UFSM |
bitstream.url.fl_str_mv |
http://repositorio.ufsm.br/bitstream/1/26132/2/license_rdf http://repositorio.ufsm.br/bitstream/1/26132/3/license.txt http://repositorio.ufsm.br/bitstream/1/26132/1/TES_PPGEE_2022_RIBEIRO_JULIO.pdf |
bitstream.checksum.fl_str_mv |
4460e5956bc1d1639be9ae6146a50347 2f0571ecee68693bd5cd3f17c1e075df 4fd3e3c43eadce71870445e4d10c973f |
bitstream.checksumAlgorithm.fl_str_mv |
MD5 MD5 MD5 |
repository.name.fl_str_mv |
Manancial - Repositório Digital da UFSM - Universidade Federal de Santa Maria (UFSM) |
repository.mail.fl_str_mv |
|
_version_ |
1794524392428929024 |