Doutorado em Engenharia Elétrica

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Navegando Doutorado em Engenharia Elétrica por Autor "Almeida, Gustavo Maia de"

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    Controle preditivo sintonizado via algoritmo genético aplicado em processos siderúrgicos
    (Universidade Federal do Espírito Santo, 2011-03-04) Almeida, Gustavo Maia de; Denti Filho, José; Salles, José Leandro Félix; Fardin, Jussara Farias; Mattedi, Alessandro; Coelho, Antônio Augusto Rodrigues; Machado, Marcelo Lucas Pereira
    Techniques of Model Based Predictive Control (MPC) are increasingly applied in industry because they generally exhibit a good performance and robustness, since the parameters of controller are tuned correctly. This thesis use the Genetic Algorithm (GA) to perform the tuning of parameters of the predictive controller to control mono and multivariable, linear and nonlinear models. The existing technical literature for tuning Predictive Dynamic Matrix Controller (DMC), which use a step response to control systems that are open loop stable will be compared with the tuning by GA. In the event that the process is unstable in open-loop, there is not an analytical method for the tuning. Therefore, it is noted in the literature that the use of MPC in some open-loop unstable systems, linear or not (Such as these to be studied in this thesis) is lacking, because the tuning procedure is based on trial and error and sometimes is impractical. Therefore, this study focuses the application of the MPC tuning by Genetic algorithm for two open-loop unstable processes, which are very important in the Steel Industry. The first is composed by Rolling Mill Stands, where we wish to minimize the variation of strip thickness the last stand due to disturbances that affect the process such as temperature and strip thickness variations in the first stand. In this case we use the linear and multivariable model to develop the Generalized Predictive Controller (GPC) whose parameters are tuned by GA. The second process, unstable in open-loop, is the level of Mold of a Continuous Casting, which has a nonlinear model and is therefore controlled by techniques of nonlinear predictive control using neural networks and Hammerstein model. A comparison is made between these controllers to analyze the stability and robustness when the mold is affected by disturbance of Bulging, Clogging and Argon.
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    Gerenciamento de uma microrrede utilizando controle preditivo com incertezas meteorológicas
    (Universidade Federal do Espírito Santo, 2020-12-07) Silva, Danilo de Paula e; Fardin, Jussara Farias; https://orcid.org/000000034785556X; http://lattes.cnpq.br/1912113095988528; https://orcid.org/; http://lattes.cnpq.br/; Arruda, Edilson Fernandes de; https://orcid.org/; http://lattes.cnpq.br/; Almeida, Gustavo Maia de; https://orcid.org/; http://lattes.cnpq.br/; Rocha, Helder Roberto de Oliveira; https://orcid.org/000000016215664X; http://lattes.cnpq.br/8801325729735529; Medina, Augusto Cesar Rueda; https://orcid.org/0000000242913153; http://lattes.cnpq.br/7397584412509839
    Microgrid management is a multi-objective problem that involves purchasing and selling energy, time-variant renewable generation, and maintenance costs. The microgrid can operate autonomously on an island or through mode connected with the main grid. This