Cette thèse porte sur l’application des approches de commande prédictive pour l’optimisation des démarrages des centrales à cycles combinés. Il s’agit d’une problématique à fort enjeu qui pose des défis importants. L’élaboration des approches est progressive. Dans une première partie un modèle de centrale est construit et adapté à l’optimisation, en utilisant une méthodologie qui transforme des modèles physiques Modelica conçus pour la simulation en des modèles pour l’optimisation. Cette méthodologie a permis de construire une bibliothèque adaptée à l’optimisation. La suite des travaux porte sur l’utilisation du modèle afin d’optimiser phase par phase les performances du démarrage. La solution proposée optimise, en temps continu, le profil de charge des turbines en recherchant dans des ensembles de fonctions particulières. Le profil optimal est déterminé en considérant que celui-ci peut être décrit par une fonction paramétrée dont les paramètres sont calculés en résolvant un problème de commande optimale sous contraintes. La dernière partie des travaux consiste à intégrer cette démarche d’optimisation à temps continu dans une stratégie de commande à horizon glissant. Cette approche permet d’une part de corriger les dérives liées aux erreurs de modèles et aux perturbations, et d’autre part, d’améliorer le compromis entre le temps de calcul et l’optimalité de la solution. Cette approche de commande conduit cependant à des temps de calcul importants. Afin de réduire le temps de calcul, une structure de commande prédictive hiérarchisée avec deux niveaux, en travaillant à des échelles de temps et sur des horizons différents, a été proposée. / This thesis focuses on the application of model predictive control approaches to optimize the combined cycle power plants start-ups. Generally, the optimization of start-up is a very problematic issue that poses significant challenges. The development of the proposed approaches is progressive. In the first part a physical model of plant is developed and adapted to optimization purposes, by using a methodology which transforms Modelica model components into optimization-oriented models. By applying this methodology, a library suitable for optimization purposes has been built.In the second part, based on the developed model, an optimization procedure to improve the performances of the start-up phases is suggested. The proposed solution optimizes, in continuous time, the load profile of the turbines, by seeking in specific sets of functions. The optimal profile is derived by considering that this profile can be described by a parameterized function whose parameters are computed by solving a constrained optimal control problem. In the last part, the open-loop optimization procedure has been integrated into a receding horizon control strategy. This strategy represents a robust solution against perturbation and models errors, and enables to improve the trade-off between computation time and optimality of the solution. Nevertheless, the control approach leads to a significant computation time. In order to obtain real-time implementable results, a hierarchical model predictive control structure with two layers, working at different time scales and over different prediction horizons, has been proposed.

Submitted by Johnny Rodrigues (johnnyrodrigues@ufcg.edu.br) on 2018-08-06T15:24:59Z No. of bitstreams: 1 LUCIANO CIPRIANO DA SILVA - DISSERTAÇÃO PPGMAT 2013..pdf: 1125916 bytes, checksum: d2b1ef64aa3ef95093acedfd0f7a711c (MD5) / Made available in DSpace on 2018-08-06T15:24:59Z (GMT). No. of bitstreams: 1 LUCIANO CIPRIANO DA SILVA - DISSERTAÇÃO PPGMAT 2013..pdf: 1125916 bytes, checksum: d2b1ef64aa3ef95093acedfd0f7a711c (MD5) Previous issue date: 2013-02 / Capes / Neste trabalho estudamos o controle hierárquico, para um sistema parabólico, em um domínio não cilíndrico. O controle hierárquico é um problema que consiste em aproximar, em um tempo fixado, as soluções das equações de estado que temos, (essas soluções dependem de funções chamadas controles), de um estado considerado ideal, através de um sistema de líder, que é o controle independente, e seguidores, que são os controles que dependem da ação do líder. Começamos fazendo uma transformação do problema original para um equivalente em domínio cilíndrico, então estudamos o controle hierárquico deste sistema. Usaremos a estratégia de Stackelberg-Nash, processo no qual, para cada escolha do líder, procuramos por seguidores que satisfaçam um certo problema de minimização, as soluções deste problema formam o que chamamos de Equilíbrio de Nash, resolvido esse problema, trabalhamos para provar que o sistema é aproximadamente controlável usando o líder. Resolvemos ainda um sistema sistema de otimalidade para os seguidores. / We present hierarchic control to a parabolic system in a noncylindrical domain. The hierarchic control is a problem that is how to bring in a fixed time, the solutions of the equations of state we have, (these solutions depend on a functions called controls), a state considered ideal, througha system of leading, independent control, and followers, the leader controls dependents. We start by making a transformation of the original problem to an equivalent cylindrical domain, then do the hierarchic control of this problem. We use the strategy Stackelberg-Nash, a process in which each leader’s choice, look for followers to satisfy a minimization problem, the solution of this problem form what we call the Nash equilibrium, solved this problem, work to prove that the approximately system is controllable using the leader. We further resolve to a of optimality for followers.

Submitted by Renata Lopes (renatasil82@gmail.com) on 2016-06-06T12:01:24Z No. of bitstreams: 1 wesleyperes.pdf: 8553175 bytes, checksum: 6f7d954432a35224645decbd6b1e94f7 (MD5) / Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2016-07-02T13:24:49Z (GMT) No. of bitstreams: 1 wesleyperes.pdf: 8553175 bytes, checksum: 6f7d954432a35224645decbd6b1e94f7 (MD5) / Made available in DSpace on 2016-07-02T13:24:49Z (GMT). No. of bitstreams: 1 wesleyperes.pdf: 8553175 bytes, checksum: 6f7d954432a35224645decbd6b1e94f7 (MD5) Previous issue date: 2016-03-28 / Os Estabilizadores de Sistemas de Potência (ESP) têm sido usados por décadas para fornecer amortecimento às oscilações eletromecânicas através do controle de excitação dos geradores síncronos. No presente trabalho, são propostas metodologias para o projeto de estabilizadores por meio do ajuste de ganho e fase. O ajuste é realizado de forma simultânea (coordenada), considerando múltiplos pontos de operação com o objetivo de garantir a robustez dos controladores. Duas estruturas são consideradas: a descentralizada, que utiliza somente sinais locais, e a hierárquica, que utiliza sinais remotos. A dinâmica do sistema elétrico de potência é modelada em espaço de estados e o procedimento de ajuste é formulado como um problema de otimização para a maximização do coeficiente de amortecimento do autovalor dominante do sistema em malha fechada considerando todos os pontos de operação. As metodologias, aqui abordadas, são baseadas em métodos de otimização bioinspirados no comportamento de enxames: Enxame de Partículas e Eco-localização de Morcegos. São desenvolvidas metodologias híbridas baseadas no acoplamento dos métodos bioinspirados com o Método do Gradiente Descendente para o refinamento dos ganhos dos controladores de forma a melhorar a busca local do processo de otimização. Com o objetivo de manter a diversidade da população, estratégias baseadas em multipopulações também são propostas. As metodologias propostas foram validadas através da simulação de sistemas teste de pequeno e médio porte, que são normalmente utilizados na literatura especializada. Os resultados são considerados promissores e acredita-se que as metodologias propostas possam ser de grande valor nessa área de conhecimento. / Power system stabilizers have been used for decades in order to provide the necessary damping of power system oscillations through generators excitation control. In this thesis, methodologies for stabilizers design (gain and phase compensation tuning) are presented. All stabilizers are simultaneously designed (coordinated design) taking into account a set of pre-specified operating conditions in order to ensure robustness. Two control structures are considered: decentralized (based on the use of local signals) and hierarchical (that uses remote signals). The power system dynamic is modeled in state space and the tuning procedure is formulated as an optimization problem in order to maximize the damping coefficient associated to the dominant pole in closed-loop operation for all operating conditions. The proposed methodologies are based on optimization algorithms bio-inspired in swarm behavior: Particle Swarm Optimization and Bat Algorithm. Hybrid methodologies are developed through coupling bio-inspired methods to the Steepest Descent method in order to enhance the local search procedure during the optimization process (only the stabilizers gain are adjusted in the local search). Multipopulational approaches are also developed in order to prevent the population diversity lost. The proposed methodologies are validated by using small and medium-sized benchmark power systems. The results are considered to be promising and the proposed methodologies are found of a great value in this research field.