Engenharia Elétrica

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http://repositorio.ufes.br/handle/10/17617
Programa de Pós-Graduação em Engenharia Elétrica

Centro: CT
Telefone: (27) 4009 2663
URL do programa: https://engenhariaeletrica.ufes.br/pt-br/pos-graduacao/PPGEE

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

Agora exibindo 1 - 4 de 4
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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
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    Implantação do controlador preditivo multivariável DMC em uma planta piloto
    (Universidade Federal do Espírito Santo, 2011-11-21) Pereira, Rogério Passos do Amaral; Munareto, Saul da Silva; Salles, José Leandro Félix; Munaro, Celso José; Almeida, Gustavo Maia de
    This work aims to implement the predictive multivariable DMC controller in a real plant and compare it with the multi-loop PID. The practical application is made in a pilot plant located in the IFES / Serra, where pressure and level are controlled with the speed of the pump and the valve opening. The process model, the calculation of the relative gain array to determine the degree of coupling of the loops and the tuning of the multi-loop PID controllers are presented. The initial tuning of the PID is suggested by a simulator that compares the performance of various tuning methods commonly found in the literature. The initial DMC tuning is suggested by a simulator based on genetic algorithm. In both cases the final tuning is adjusted manually to improve the performance of the loops. Plant responses to step using the multi-loop PID and DMC are compared with and without restrictions on the valve opening and the pump speed. A didactic interface developed with the LabVIEW software is used to interact with MATLAB and the CompactRIO controller, this iteration allows use MATLAB optimization functions in the implementation of the DMC controller.
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    Modelagem e controle preditivo dos queimadores de uma caldeira
    (Universidade Federal do Espírito Santo, 2019-12-02) Barbarioli, Guilherme Lima; Fardin, Jussara Farias; https://orcid.org/000000034785556X; http://lattes.cnpq.br/1912113095988528; Almeida, Gustavo Maia de; Rocha, Helder Roberto de Oliveira; Salles, Jose Leandro Felix
    Environmental sustainability has been a main topic leading industries to a better energy efficiency. Basic industries such as the metallurgic sector, applied many improvements in the last 50 years, mainly through new control and process techniques contributing to a outstanding 60% increase in energy efficiency, throughout all steel plants processes. However, those changes do not involve managing steel mill byproduct gases. The major issue that need to be addressed is the management and control systems that handle the destination of those gases. Mostly, those gases are used to generate electric energy and also to heat up some internal steps in the metal making process. To optimize the gas usage, the management strategies must include some mathematical optimizing models, to manage and predict the best route and time to consume those gases. Subjected to that management tool there must be a set of advanced controls that manage optimally the permissions and regulatory control to achieve the goals set by management algorithm. On this paper is proposed a hybrid control system based on furnace model of a boiler that is used to supply vapor for thermoelectric power plant. The main objective is to track the set-point path without any offset. Targeting this performance is proposed a hybridmodel using MLD (Mixed Logical Dynamics) formulation, utilizing HYSDEL(Hybrid System DEscription Language) language tools. This mathematical model attach many of the start-up and shut-down procedures, physical operation limits and also the switch affine systems that compose the furnace model. The control system use an on-line quadratic mixed-integer optimization with a receding control horizon, to predict the best control actions. To estimate some of the non-measured variables it was built a switched Kalman f ilter, allowing the control system to reject white noise and also constant disturb signal such as the valve grasp issue. In order to evaluate the control system performance, it is executed five case studies, including a comparison with a PI (Proportional Integral) controller.