Restabelecimento de sistemas de distribuição utilizando fluxo de potência ótimo

Borges, Thiago Trezza; Garcia, P.A.N.; Carneiro, S.; Pereira, J.L.R.

doi:10.1590/s0103-17592012000600007

Cited by 5 publications

(3 citation statements)

References 31 publications

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“…The focus of this study was on the immediate reclosing of an electricity distribution network, considering that there was a failure in a certain part of the model. According to [10], the solution of the problem of reestablishment of Electric Energy Distribution Systems (EEDS) is important. Thus, several works have been developed as: Comprehensive Search Methods [11], Automation Logics; Intelligent Systems [12], [13] [7].…”

Section: Resultsmentioning

confidence: 99%

Power Flow Analysis and Self-recovery of Electrical Energy Distribution Network Using Artificial Neural Networks

Avelar

Fritzen

Aguiar-Furucho

2018

Braz. arch. biol. technol.

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A computational model for self-recovery of electricity distribution network was developed to simulate it, emulated by the IEEE 123 node model. The electrical system considered has automatic switches capable of identifying a momentary failure in the line and finding the best reconfiguration for its reclosing. An artificial neural network (ANN), backpropagation, was used to classify the type of failure and determine the best reconfiguration of the distribution network. Initially, five power failure scenarios were simulated in certain different parts of the power grid, and power flow analysis via OpenDSS was performed. Next, the most suitable switching was observed within the shortest time interval to restore the power supply. With the purpose of better visualization to identify the reclosing, an implementation was carried out via ELIPSE SCADA. In this way, it is possible to identify the faulted segment in order to isolate it, leaving the smallest number of consumers without power supply in shortest possible time. With the results of the simulations, tests and analyzes were performed to verify their robustness and speed, in the expectation that the model developed be faster than an experienced Operating Distribution Center.

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Section: Resultsmentioning

confidence: 99%

Power Flow Analysis and Self-recovery of Electrical Energy Distribution Network Using Artificial Neural Networks

Avelar

Fritzen

Aguiar-Furucho

2018

Braz. arch. biol. technol.

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“…Teoria dos Grafos e Locais de Falta Sob o ponto de vista topológico, um SDEE pode ser modelado de forma simplicada por meio de grafos, trazendo para o estudo conceitos, técnicas e teoremas definidos com rigor matemático e já estabelecidos pelo uso em outrasáreas de conhecimento (Braz, 2010). Naárea de sistemas elé-tricos, a teoria dos grafos vem sendo largamente utilizada em estudos sobre configuração do sistema (Braz, 2010), processamento topológico (Bernardes et al, 2015), fluxo de potência (Marini et al, 2019;Borges et al, 2012) e ilhamento controlado (Souza, 2014).…”

Section: Seleção Das Variáveis De Simulaçãounclassified

Uma Proposta para Automação de Bases de Dados Simuladas de Faltas de Alta Impedância

Lima

Lucena²,

Brito

et al. 2020

Anais Do Congresso Brasileiro De Automática 2020

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As etapas para a construção automática de uma base de dados simulada para faltas de alta impedância são apresentadas neste trabalho, visando suprir a escassez de registros oscilográficos reais para este tipo de falta. Os softwares Matlab R e ATP são utilizados para estruturação e simulação dos cenários. O conceito de automação robótica de processos é adotado enquanto solução para integração dos softwares e automação do sistema, utilizando a linguagem Python. Em auxílio a tomada de decisões, a teoria dos grafos é aplicada à seleção das variáveis de simulação. Os resultados apresentados evidenciam ganhos produtivos e sugerem que a metodologia adotada pode ser aplicada na construção de bases de dados simulados de outros distúrbios dos sistemas elétricos de potência.

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“…The IPM-Carvalho model simulates straw yield involving rainfall and nitrogen supply (Carvalho et al, 2009;Borges et al, 2012). The regression model allows estimation of the ideal input with simulation of the harvest index.…”

Section: Introductionmentioning

confidence: 99%

Research Article A combination of regression and internal point methods as a hybrid model for estimating oat plant productivity

Marolli¹,

Silva²,

Rosa³

et al. 2021

Genet. Mol. Res.

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The internal points method (IPM-Carvalho), with regression analysis, can generate an efficient hybrid model for estimating oat grain productivity. We tested a combination of the internal points method and regression to estimate straw productivity. We also applied this methodology to forecast a harvest index in the elaboration of a hybrid model to estimate oat grain productivity, taking into account nitrogen management and growth regulator use, with biological and environmental indicators. Simulation of oat yield as a function of nitrogen and growth regulator applications, with biological and environmental inputs, can assist in the development of more efficient and sustainable management for this crop. Two experiments were conducted during 2013, 2014, and 2015; one was used to quantify biomass yield and the other to determine grain yield and plant lodging. The experimental design was randomized blocks with four replications in a 4 x 3 factorial scheme in the sources of variation, which were growth regulator (0, 200, 400 and 600 mL ha -1 ) and nitrogen (30, 90 and 150 kg ha applications. The environmental parameters that were included were rainfall and maximum air temperature. The nitrogen was applied as urea at the expanded fourth leaf stage. The growth regulator was trinexapac-ethyl applied at the ©FUNPEC-RP www.funpecrp.com.br Genetics and Molecular Research 20 (2): gmr18756 A. Marolli et al. 2 stage between the 1st and 2nd visible stem node. Straw productivity was obtained by the IPM model with nitrogen dose and rainfall inputs. The harvest index was obtained by regression as a function of the growth regulator doses. The combination of the internal points method to estimate straw productivity with the use of regression in the forecast of the harvest index proved to be a useful model for estimating oat grain productivity based on biological and environmental parameters, together with nitrogen and growth regulator applications.

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Restabelecimento de sistemas de distribuição utilizando fluxo de potência ótimo

Cited by 5 publications

References 31 publications

Power Flow Analysis and Self-recovery of Electrical Energy Distribution Network Using Artificial Neural Networks

Power Flow Analysis and Self-recovery of Electrical Energy Distribution Network Using Artificial Neural Networks

Uma Proposta para Automação de Bases de Dados Simuladas de Faltas de Alta Impedância

Research Article A combination of regression and internal point methods as a hybrid model for estimating oat plant productivity

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