Novo conjunto de indicadores de desempenho operacional para redes elétricas inteligentes por meio da Lógica fuzzy
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Tipo de documento: | Tese |
Idioma: | por |
Título da fonte: | Manancial - Repositório Digital da UFSM |
Texto Completo: | http://repositorio.ufsm.br/handle/1/22696 |
Resumo: | Operational performance analysis is a multidisciplinary tool that allows the engineering systems manager to control the quality of services provided, as needed. For this operational tool to be effective, it is necessary to know the details of the processes involved and mathematically model metrics that bring representative responses from the goals established for the qualification of the system. In this work, the quality metrics established for the Telecommunications systems and the continuity of the Electricity Distribution services, inspected by Anatel and Aneel, respectively, were analyzed. The two methods have differences in schedules, evaluated periods and mathematical relationships. The two methods have differences in schedules, evaluated periods and mathematical relationships. In addition, they have few indexes listing their indicators. Thus, Fuzzy Logic was selected as a mathematical tool to relate the indicators currently practiced in order to create more comprehensive indices that can compose a new methodology for the analysis of operational performance for Smart Grids (REI). This methodology starts from the union of the current sets with their respective metrics, being organized in four main axes: specific indices, instant monitoring indices, new indicators and customer satisfaction. From these groups, indicators and indexes were developed to assess the quality of REI. Then, for the first grouping: indexes of establishment and continuity of data connection (REI01) and guarantee of the contracted transmission rate (REI02) and infrastructure quality indicator (REI07); for the second: indices of instantaneous capacity of individual interruptions (REI03), of instantaneous ability to calculate the frequency of individual interruptions (REI04), of instantaneous capacity of collective interruptions (REI05), of instantaneous ability to calculate the frequency of interruptions individual (REI06); for the third: indicators of the instantaneous data transfer rate (SMP10i / SCM4i) and the average monthly individual data transfer rate (SMP11i / SCM5i); and, for the fourth: the union of several current indicators and indices that make up customer satisfaction, such as drop rate (SMP7), maximum duration of continuous interruption (DMIC), user complaints rate (REL), number repair requests (RAI), consumer satisfaction Aneel (IASC), satisfaction survey and perceived quality Anatel (SCM and SMP). Equal data with different goals per company (DIC, for example) were normalized through the Indicator Unit Value (VUI) created. Through the Fuzzy Logic it was possible to implement the evaluation of the inputs from the goals stipulated by the current regulations, in levels established as stages or equivalent numerical quality levels, being: very bad (0 - 0.3), bad (0.2 - 0 , 6), good (0.5 - 0.9) and excellent (0.8 - 1). The fuzzifiers and defuzzifiers were tested with a REI, found in the bibliography, and with real data from a Brazilian region with an implanted REI, showing adherence in the evaluations and results obtained. |
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Novo conjunto de indicadores de desempenho operacional para redes elétricas inteligentes por meio da Lógica fuzzyNew group of operational performance indicators for smart grids through fuzzy LogicRede elétrica inteligenteIndicadores de continuidadeTelecomunicaçõesIndicadores operacionaisLógica fuzzyDistribuição de energia elétricaSmart gridsContinuity indicatorsTelecommunicationsOperational indicatorsFuzzy logicElectricity distributionCNPQ::ENGENHARIAS::ENGENHARIA ELETRICAOperational performance analysis is a multidisciplinary tool that allows the engineering systems manager to control the quality of services provided, as needed. For this operational tool to be effective, it is necessary to know the details of the processes involved and mathematically model metrics that bring representative responses from the goals established for the qualification of the system. In this work, the quality metrics established for the Telecommunications systems and the continuity of the Electricity Distribution services, inspected by Anatel and Aneel, respectively, were analyzed. The two methods have differences in schedules, evaluated periods and mathematical relationships. The two methods have differences in schedules, evaluated periods and mathematical relationships. In addition, they have few indexes listing their indicators. Thus, Fuzzy Logic was selected as a mathematical tool to relate the indicators currently practiced in order to create more comprehensive indices that can compose a new methodology for the analysis of operational performance for Smart Grids (REI). This methodology starts from the union of the current sets with their respective metrics, being organized in four main axes: specific indices, instant monitoring indices, new indicators and customer satisfaction. From these groups, indicators and indexes were developed to assess the quality of REI. Then, for the first grouping: indexes of establishment and continuity of data connection (REI01) and guarantee of the contracted transmission rate (REI02) and infrastructure quality indicator (REI07); for the second: indices of instantaneous capacity of individual interruptions (REI03), of instantaneous ability to calculate the frequency of individual interruptions (REI04), of instantaneous capacity of collective interruptions (REI05), of instantaneous ability to calculate the frequency of interruptions individual (REI06); for the third: indicators of the instantaneous data transfer rate (SMP10i / SCM4i) and the average monthly individual data transfer rate (SMP11i / SCM5i); and, for the fourth: the union of several current indicators and indices that make up customer satisfaction, such as drop rate (SMP7), maximum duration of continuous interruption (DMIC), user complaints rate (REL), number repair requests (RAI), consumer satisfaction Aneel (IASC), satisfaction survey and perceived quality Anatel (SCM and SMP). Equal data with different goals per company (DIC, for example) were normalized through the Indicator Unit Value (VUI) created. Through the Fuzzy Logic it was possible to implement the evaluation of the inputs from the goals stipulated by the current regulations, in levels established as stages or equivalent numerical quality levels, being: very bad (0 - 0.3), bad (0.2 - 0 , 6), good (0.5 - 0.9) and excellent (0.8 - 1). The fuzzifiers and defuzzifiers were tested with a REI, found in the bibliography, and with real data from a Brazilian region with an implanted REI, showing adherence in the evaluations and results obtained.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPESA análise de desempenho operacional é uma ferramenta multidisciplinar que permite ao gestor de sistemas de Engenharia uma forma de controlar a qualidade dos serviços prestados ou fornecidos, conforme a necessidade. Para que essa ferramenta operacional tenha efetividade é necessário conhecer os detalhes dos processos envolvidos e modelar matematicamente métricas que tragam respostas representativas a partir de metas estabelecidas para qualificação do sistema. Neste trabalho, foram analisadas as métricas de qualidade estabelecidas para os sistemas de Telecomunicações e de continuidade dos serviços de Distribuição de Energia Elétrica, fiscalizados por Anatel e Aneel, respectivamente. Os dois métodos possuem divergências em horários, períodos avaliados e relações matemáticas. Além disso, possuem poucos índices relacionando seus indicadores. Assim, foi selecionada a Lógica Fuzzy, como ferramenta matemática, para relacionar os indicadores atualmente praticados de forma a criar índices mais abrangentes que possam compor uma nova metodologia de análise de desempenho operacional para as Redes Elétricas Inteligentes (REI). Esta metodologia parte da união dos conjuntos atuais com suas respectivas métricas, sendo organizada em quatro eixos principais: índices específicos, índices de monitoramento instantâneo, indicadores novos e satisfação do cliente. A partir desses grupos, foram desenvolvidos indicadores e índices para avaliar a qualidade da REI. Então, para o primeiro agrupamento: índices de estabelecimento e continuidade da conexão de dados (REI01) e de garantia da taxa de transmissão contratada (REI02) e indicador de qualidade da infraestrutura (REI07); para o segundo: índices de capacidade instantânea de interrupções individuais (REI03), de capacidade instantânea de apurar a frequência de interrupções individuais (REI04), de capacidade de apuração instantânea de interrupções coletivas (REI05), de capacidade instantânea de apurar a frequência de interrupções individuais (REI06); para o terceiro: indicadores da taxa instantânea de transferência de dados (SMP10i/SCM4i) e da taxa média mensal de transferência de dados individual (SMP11i/SCM5i); e, para o quarto: a união de vários indicadores e índices atuais que compõem a satisfação do cliente, como taxa de queda de ligações (SMP7), duração máxima de interrupção contínua (DMIC), taxa de reclamações dos usuários (REL), número de solicitações de reparo (RAI), satisfação do consumidor Aneel (IASC), pesquisa de satisfação e qualidade percebida Anatel (SCM e SMP). Dados iguais com metas distintas por empresa (DIC, por exemplo) foram normalizados através do Valor Unitário de Indicador (VUI) criado. Através da Lógica Fuzzy foi possível implementar a avaliação das entradas a partir das metas estipuladas pelos regulamentos atuais, em níveis estabelecidos como estágios ou níveis de qualidade numéricos equivalentes, sendo: péssimo (0 – 0,3), ruim (0,2 – 0,6), bom (0,5 – 0,9) e excelente (0,8 – 1). Os fuzzificadores e defuzzificadores foram testados com uma REI, encontrada na bibliografia, e com dados reais de uma região brasileira com REI implantada, mostrando aderência nas avaliações e resultados obtidos.Universidade Federal de Santa MariaBrasilEngenharia ElétricaUFSMPrograma de Pós-Graduação em Engenharia ElétricaCentro de TecnologiaAbaide, Alzenira da Rosahttp://lattes.cnpq.br/2427825596072142Canha, Luciane NevesBernardon, Daniel PinheiroKnak Neto, NelsonBinelo, Manuel OsorioRodrigues, Mauro Fonseca2021-11-03T19:51:56Z2021-11-03T19:51:56Z2020-08-14info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesisapplication/pdfhttp://repositorio.ufsm.br/handle/1/22696porAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessreponame:Manancial - Repositório Digital da UFSMinstname:Universidade Federal de Santa Maria (UFSM)instacron:UFSM2022-08-09T12:20:48Zoai:repositorio.ufsm.br:1/22696Biblioteca Digital de Teses e Dissertaçõeshttps://repositorio.ufsm.br/ONGhttps://repositorio.ufsm.br/oai/requestatendimento.sib@ufsm.br||tedebc@gmail.comopendoar:2022-08-09T12:20:48Manancial - Repositório Digital da UFSM - Universidade Federal de Santa Maria (UFSM)false |
dc.title.none.fl_str_mv |
Novo conjunto de indicadores de desempenho operacional para redes elétricas inteligentes por meio da Lógica fuzzy New group of operational performance indicators for smart grids through fuzzy Logic |
title |
Novo conjunto de indicadores de desempenho operacional para redes elétricas inteligentes por meio da Lógica fuzzy |
spellingShingle |
Novo conjunto de indicadores de desempenho operacional para redes elétricas inteligentes por meio da Lógica fuzzy Rodrigues, Mauro Fonseca Rede elétrica inteligente Indicadores de continuidade Telecomunicações Indicadores operacionais Lógica fuzzy Distribuição de energia elétrica Smart grids Continuity indicators Telecommunications Operational indicators Fuzzy logic Electricity distribution CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA |
title_short |
Novo conjunto de indicadores de desempenho operacional para redes elétricas inteligentes por meio da Lógica fuzzy |
title_full |
Novo conjunto de indicadores de desempenho operacional para redes elétricas inteligentes por meio da Lógica fuzzy |
title_fullStr |
Novo conjunto de indicadores de desempenho operacional para redes elétricas inteligentes por meio da Lógica fuzzy |
title_full_unstemmed |
Novo conjunto de indicadores de desempenho operacional para redes elétricas inteligentes por meio da Lógica fuzzy |
title_sort |
Novo conjunto de indicadores de desempenho operacional para redes elétricas inteligentes por meio da Lógica fuzzy |
author |
Rodrigues, Mauro Fonseca |
author_facet |
Rodrigues, Mauro Fonseca |
author_role |
author |
dc.contributor.none.fl_str_mv |
Abaide, Alzenira da Rosa http://lattes.cnpq.br/2427825596072142 Canha, Luciane Neves Bernardon, Daniel Pinheiro Knak Neto, Nelson Binelo, Manuel Osorio |
dc.contributor.author.fl_str_mv |
Rodrigues, Mauro Fonseca |
dc.subject.por.fl_str_mv |
Rede elétrica inteligente Indicadores de continuidade Telecomunicações Indicadores operacionais Lógica fuzzy Distribuição de energia elétrica Smart grids Continuity indicators Telecommunications Operational indicators Fuzzy logic Electricity distribution CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA |
topic |
Rede elétrica inteligente Indicadores de continuidade Telecomunicações Indicadores operacionais Lógica fuzzy Distribuição de energia elétrica Smart grids Continuity indicators Telecommunications Operational indicators Fuzzy logic Electricity distribution CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA |
description |
Operational performance analysis is a multidisciplinary tool that allows the engineering systems manager to control the quality of services provided, as needed. For this operational tool to be effective, it is necessary to know the details of the processes involved and mathematically model metrics that bring representative responses from the goals established for the qualification of the system. In this work, the quality metrics established for the Telecommunications systems and the continuity of the Electricity Distribution services, inspected by Anatel and Aneel, respectively, were analyzed. The two methods have differences in schedules, evaluated periods and mathematical relationships. The two methods have differences in schedules, evaluated periods and mathematical relationships. In addition, they have few indexes listing their indicators. Thus, Fuzzy Logic was selected as a mathematical tool to relate the indicators currently practiced in order to create more comprehensive indices that can compose a new methodology for the analysis of operational performance for Smart Grids (REI). This methodology starts from the union of the current sets with their respective metrics, being organized in four main axes: specific indices, instant monitoring indices, new indicators and customer satisfaction. From these groups, indicators and indexes were developed to assess the quality of REI. Then, for the first grouping: indexes of establishment and continuity of data connection (REI01) and guarantee of the contracted transmission rate (REI02) and infrastructure quality indicator (REI07); for the second: indices of instantaneous capacity of individual interruptions (REI03), of instantaneous ability to calculate the frequency of individual interruptions (REI04), of instantaneous capacity of collective interruptions (REI05), of instantaneous ability to calculate the frequency of interruptions individual (REI06); for the third: indicators of the instantaneous data transfer rate (SMP10i / SCM4i) and the average monthly individual data transfer rate (SMP11i / SCM5i); and, for the fourth: the union of several current indicators and indices that make up customer satisfaction, such as drop rate (SMP7), maximum duration of continuous interruption (DMIC), user complaints rate (REL), number repair requests (RAI), consumer satisfaction Aneel (IASC), satisfaction survey and perceived quality Anatel (SCM and SMP). Equal data with different goals per company (DIC, for example) were normalized through the Indicator Unit Value (VUI) created. Through the Fuzzy Logic it was possible to implement the evaluation of the inputs from the goals stipulated by the current regulations, in levels established as stages or equivalent numerical quality levels, being: very bad (0 - 0.3), bad (0.2 - 0 , 6), good (0.5 - 0.9) and excellent (0.8 - 1). The fuzzifiers and defuzzifiers were tested with a REI, found in the bibliography, and with real data from a Brazilian region with an implanted REI, showing adherence in the evaluations and results obtained. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-08-14 2021-11-03T19:51:56Z 2021-11-03T19:51:56Z |
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/1/22696 |
url |
http://repositorio.ufsm.br/handle/1/22696 |
dc.language.iso.fl_str_mv |
por |
language |
por |
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.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Universidade Federal de Santa Maria Brasil Engenharia Elétrica UFSM Programa de Pós-Graduação em Engenharia Elétrica Centro de Tecnologia |
publisher.none.fl_str_mv |
Universidade Federal de Santa Maria Brasil Engenharia Elétrica UFSM Programa de Pós-Graduação em Engenharia Elétrica 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 |
repository.name.fl_str_mv |
Manancial - Repositório Digital da UFSM - Universidade Federal de Santa Maria (UFSM) |
repository.mail.fl_str_mv |
atendimento.sib@ufsm.br||tedebc@gmail.com |
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1805922104073256960 |