Registro fonte: Neuro-fuzzy control and particle swarm optimization on horizontal axis wind turbine

Neuro-fuzzy control and particle swarm optimization on horizontal axis wind turbine

The consumption of electric energy is increasing. This growth stimulates production that is entirely based on fossil fuels. However, for social, political or environmental reasons there is a need to change the energy source. Large, medium and low-scale generation by means of wind turbines is a viabl...

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Autor principal:	Lenz, Wagner Barth
Formato:	Dissertação
Idioma:	Português
Publicado em:	Universidade Tecnológica Federal do Paraná 2019
Assuntos:	Turbinas Energia - Fontes alternativas Controladores PID Turbines Renewable energy sources PID controllers CNPQ::ENGENHARIAS::ENGENHARIA MECANICA Engenharia Mecânica
Acesso em linha:	http://repositorio.utfpr.edu.br/jspui/handle/1/4170
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spelling	riut-1-41702019-07-18T06:01:03Z Neuro-fuzzy control and particle swarm optimization on horizontal axis wind turbine Neuro-fuzzy controle e optimização por enxame de partículas em uma turbina eólica de eixo horizontal Lenz, Wagner Barth Tusset, Angelo Marcelo http://lattes.cnpq.br/1204232509410955 Balthazar, José Manoel http://lattes.cnpq.br/9728054402919622 Tusset, Angelo Marcelo Ribeiro, Mauricio Aparecido Tadano, Yara de Souza Gonçalves, Paulo José Paupitz Turbinas Energia - Fontes alternativas Controladores PID Turbines Renewable energy sources PID controllers CNPQ::ENGENHARIAS::ENGENHARIA MECANICA Engenharia Mecânica The consumption of electric energy is increasing. This growth stimulates production that is entirely based on fossil fuels. However, for social, political or environmental reasons there is a need to change the energy source. Large, medium and low-scale generation by means of wind turbines is a viable solution. Similar to other generation methods, the wind turbine needs to be optimized and controlled to function as efficiently as possible. In this work a particle swarm optimization process optimized a profile of a wind turbine based on two stretching equations that had as objective the best coefficient of power for the average speed. We also used a fuzzy neuro controller based on the maximum for each ratio between speeds (𝑇𝑆𝑅). Three wind speed profiles were used to analyze the dynamics of the wind turbine. The controller was efficient and kept the rotation within the expected range. The results show that the controller prevented the generation above the maximum power, reducing the rotations by up to 12 [rad/s] above the maximum power, in cases of oscillation in the velocity of the view the control remained stable with a low standard deviation and reducing the power in at up to 8 [rad/s] for sine waves and up to 9 [rad/s] for random inputs. O consumo de energia elétrica vem aumentando. Esse crescimento estimula a produção que está inteiramente baseada em combustíveis fósseis. Entretanto, por razoes sociais, políticas ou ambientais há uma necessidade de mudar a fonte energética. A geração em larga, média e baixa escala por meio de turbinas eólicas é uma solução viável. Similar a outros métodos de geração, a turbina eólica precisa ser otimizada e controlada para funcionar da forma mais eficiente possível. Nesse trabalho um processo de otimização por enxame de partículas otimizou um perfil de uma turbina eólica baseado em duas equações de esticão que tinha como objetivo o melhor coeficiente de potência para a velocidade média. Também utilizou um controlador neuro fuzzy com base nos máximos para cada razão entre velocidades (𝑇𝑆𝑅). Três perfis de velocidade de vento foram usados para analisar a dinâmica da turbina eólica. O controlador se mostrou eficiente e manteve a rotação dentro da faixa esperada. Os resultados mostram que o controlador preveniu a geração acima da potência máxima, reduzindo a rotação em até 12 [rad/s] acima da potência máxima, em casos de oscilação na velocidade do vendo o controle se manteve estável com um baixo desvio padrão e reduzindo a potência em ao em até 8 [rad/s] para ondas senoidal e em até 9 [rad/s] para entradas aleatórias. 2019-07-17T13:58:55Z 2019-07-17T13:58:55Z 2019-06-11 masterThesis LENZ, Wagner Barth. Neuro-fuzzy control and particle swarm optimization on horizontal axis wind turbine. 2019. 100 p. Thesis (Master’s Degree in Mechanical Engineer) - Federal University of Technology - Paraná, Ponta Grossa, 2019. http://repositorio.utfpr.edu.br/jspui/handle/1/4170 por openAccess application/pdf Universidade Tecnológica Federal do Paraná Ponta Grossa Brasil Programa de Pós-Graduação em Engenharia Mecânica UTFPR
institution	Universidade Tecnológica Federal do Paraná
collection	RIUT
language	Português
topic	Turbinas Energia - Fontes alternativas Controladores PID Turbines Renewable energy sources PID controllers CNPQ::ENGENHARIAS::ENGENHARIA MECANICA Engenharia Mecânica
spellingShingle	Turbinas Energia - Fontes alternativas Controladores PID Turbines Renewable energy sources PID controllers CNPQ::ENGENHARIAS::ENGENHARIA MECANICA Engenharia Mecânica Lenz, Wagner Barth Neuro-fuzzy control and particle swarm optimization on horizontal axis wind turbine
description	The consumption of electric energy is increasing. This growth stimulates production that is entirely based on fossil fuels. However, for social, political or environmental reasons there is a need to change the energy source. Large, medium and low-scale generation by means of wind turbines is a viable solution. Similar to other generation methods, the wind turbine needs to be optimized and controlled to function as efficiently as possible. In this work a particle swarm optimization process optimized a profile of a wind turbine based on two stretching equations that had as objective the best coefficient of power for the average speed. We also used a fuzzy neuro controller based on the maximum for each ratio between speeds (𝑇𝑆𝑅). Three wind speed profiles were used to analyze the dynamics of the wind turbine. The controller was efficient and kept the rotation within the expected range. The results show that the controller prevented the generation above the maximum power, reducing the rotations by up to 12 [rad/s] above the maximum power, in cases of oscillation in the velocity of the view the control remained stable with a low standard deviation and reducing the power in at up to 8 [rad/s] for sine waves and up to 9 [rad/s] for random inputs.
format	Dissertação
author	Lenz, Wagner Barth
author_sort	Lenz, Wagner Barth
title	Neuro-fuzzy control and particle swarm optimization on horizontal axis wind turbine
title_short	Neuro-fuzzy control and particle swarm optimization on horizontal axis wind turbine
title_full	Neuro-fuzzy control and particle swarm optimization on horizontal axis wind turbine
title_fullStr	Neuro-fuzzy control and particle swarm optimization on horizontal axis wind turbine
title_full_unstemmed	Neuro-fuzzy control and particle swarm optimization on horizontal axis wind turbine
title_sort	neuro-fuzzy control and particle swarm optimization on horizontal axis wind turbine
publisher	Universidade Tecnológica Federal do Paraná
publishDate	2019
citation	LENZ, Wagner Barth. Neuro-fuzzy control and particle swarm optimization on horizontal axis wind turbine. 2019. 100 p. Thesis (Master’s Degree in Mechanical Engineer) - Federal University of Technology - Paraná, Ponta Grossa, 2019.
url	http://repositorio.utfpr.edu.br/jspui/handle/1/4170
_version_	1805322684737781760
score	10,814766

Neuro-fuzzy control and particle swarm optimization on horizontal axis wind turbine

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