Master of Science / Department of Electrical and Computer Engineering / Sanjoy Das / Stephen M. Welch / Multi-objective optimization problems deal with finding a set of candidate optimal solutions to be presented to the decision maker. In industry, this could be the problem of finding alternative car designs given the usually conflicting objectives of performance, safety, environmental friendliness, ease of maintenance, price among others. Despite the significance of this problem, most of the non-evolutionary algorithms which are widely used cannot find a set of diverse and nearly optimal solutions due to the huge size of the search space. At the same time, the solution set produced by most of the currently used evolutionary algorithms lacks diversity. The present study investigates a new optimization method to solve multi-objective problems based on the widely used swarm-intelligence approach, Particle Swarm Optimization (PSO). Compared to other approaches, the proposed algorithm converges relatively fast while maintaining a diverse set of solutions. The investigated algorithm, Partially Informed Fuzzy-Dominance (PIFD) based PSO uses a dynamic network topology and fuzzy dominance to guide the swarm of dominated solutions. The proposed algorithm in this study has been tested on four benchmark problems and other real-world applications to ensure proper functionality and assess overall performance. The multi-objective gene regulatory network (GRN) problem entails the minimization of the coefficient of variation of modified photothermal units (MPTUs) across multiple sites along with the total sum of similarity background between ecotypes. The results throughout the current research study show that the investigated algorithm attains outstanding performance regarding optimization aspects, and exhibits rapid convergence and diversity.

The developments of advanced non-linear control strategies have attracted a considerable research interests over the past decades especially in process control. Rather than an absolute reliance on mathematical models of process plants which often brings discrepancies especially owing to design errors and equipment degradation, non-linear models are however required because they provide improved prediction capabilities but they are very difficult to derive. In addition, the derivation of the global optimal solution gets more difficult especially when multivariable and non-linear systems are involved. Hence, this research investigates soft computing techniques for the implementation of a novel real time constrained non-linear model predictive controller (NMPC). The time-frequency localisation characteristics of wavelet neural network (WNN) were utilised for the non-linear models design using system identification approach from experimental data and improve upon the conventional artificial neural network (ANN) which is prone to low convergence rate and the difficulties in locating the global minimum point during training process. Salient features of particle swarm optimisation and a genetic algorithm (GA) were combined to optimise the network weights. Real time optimisation occurring at every sampling instant is achieved using a GA to deliver results both in simulations and real time implementation on coupled tank systems with further extension to a complex quadruple tank process in simulations. The results show the superiority of the novel WNN-NMPC approach in terms of the average controller energy and mean squared error over the conventional ANN-NMPC strategies and PID control strategy for both SISO and MIMO systems.

Thesis (MEng)--Stellenbosch University, 2014. / ENGLISH ABSTRACT: Over the past 50 years smart materials have made their appearance in many structures. The thermopiezoelectric ceramic is one of these smart materials. When thermal e ects are considered negligible, then the materials are classified as piezo-ceramic and piezoelectric materials. These so called piezo-ceramics are used as actuator and sensor components in many structures. The use of these components with composite materials is significant due to their application in the aerospace and aeronautics fields. The interaction that the piezoelectric material has with a composite body can be improved in order to reduce the energy requirement of the material for deformation. An objective in the optimisation of composite material structures is to minimise compliance or maximise sti ness uT f, with the laminate ply orientations as design variables, where u and f are displacement and force vectors, respectively. Here, the objective is not the maximisation of sti ness but the maximisation of compliance, with typical constraints being failure criteria. These failure criteria can include theories such as the maximum principle stress, the Tsai-Hill or Tsai-Wu failure theories. The compliance is maximised to accentuate any piezoelectric movement and is for theoretical treatment only. Piezoelectric materials once polarized the materials becomes quasi-isotropic. The piezoelectric materials are isotropic in the plane normal to the direction of the voltage being applied and have altered properties normal to this plane. This change in the material properties can be exploited so that the layup can be altered in orientation to improve performance. The idea is to improve the mechanical capabilities of the structure subject to an electrical input or vice versa. In the works by both Carrera et al. and Piefort, First Order Shear Deformation Theory (FSDT) is used in finite element analysis to characterise the structural and electrical behaviour of a plate or shell. FSDT, also known as the Mindlin-Reissner theory, is a plate bending theory that assumes a transverse shear distribution through the thickness of the plate. This theory is considered an improvement on the standard theories such as the Kircho or Timoshenko theories. Many optimisation techniques exist and are classed as either being direct search or gradient based methods. Particle Swarm Optimisation (PSO) is a direct search method. It mimics the behaviour of a flock of birds or school of fish in their attempt to find food. The PSO’s mathematical statement characterises a set of initial unknown particles within a designated search space that are compared to a set of local best particles and a single global best particle. This comparison is used to update the swarm each run cycle. Regression is a procedure whereby a set of testing data is used to fit a pseudo-function that represents the form the data should take in practice. The aim of this work is to optimise the piezoelectric-composite layer interaction to improve the overall compliance of a structure. Extensive modelling is performed and tested with peer reviewed literature to demonstrate its accuracy. / AFRIKAANSE OPSOMMING: Oor die afgelope 50 jaar het slim materiale hulle verskyning gemaak in verskeie strukture. Termopiezo-elektriese keramieke is een van hierdie nuwe materiale. Wanneer termiese e ekte onbeduidend is, word hierdie materiale as piezo-elektriese materiale geklassifiseer. Hierdie sogenaamde piezo-keramieke word gebruik as aandrywers en sensoriese onderdele in verskeie strukture. Die kombinasie van hierdie onderdele met saamgestelde materiale het belangrike toepassings in die ruimte- en lugvaartkunde. Die interaksie van die piezo-elektriese materiale met die saamgestelde materiaal strukture kan verbeter word om die energie-vereistes van die materiaal vir vervorming te verminder. ’n Tipiese doel in die optimering van saamgestelde materiaalstrukture is om styfheid uT f te maksimeer met die gelamineerde laag-oriëntasies as ontwerpsveranderlikes, waar u en f onderskeidelik verplasing en kragvektor voorstel. In teenstelling met die optimering van die samestelling wat voorheen gedoen is, is die doel hier nie die maksimering van styfheid nie, maar die minimering van styfheid, met falingskriteria as tipiese beperkings. Die falingskriteria sluit die volgende in: die maksimum spanningsteorie, en die Tsai-Hill of Tsai-Wu falingsteorieë. Die styfheid word geminimeer om piezo-elektriese verplasing te versterk, maar word hierin net teoreties bekyk. Sodra piezo-elektriese materiale gepolariseer word, word hulle quasi-isotropies. Die piezoelektriese materiale is isotropies in die vlak gelyk aan die rigting van die stroomspanning wat daarop toegepas word en het ander eienskappe normaal tot die vlak. Die verandering in die materiaal se eienskappe kan gebruik word sodat beide die saamgestelde materiaal en die piezoelektriese laag se oriëntasie aangepas kan word vir verbeterde werkverrigting. Die idee is om die meganiese vermoëns te verbeter van ’n struktuur wat onderwerp word aan ’n elektriese inset of vice versa. In die literatuur van beide Carrera et al. en Piefort word Eerste Orde Skuifvervormings Teorie (EOST) gebruik in eindige element analises om die strukturele en elektriese gedrag van ’n plaat of dop te karakteriseer. EOST, ook bekend as Mindlin-Reissner teorie, is ’n plaat buigings-teorie wat ’n dwarsvervormingverspreiding aanneem deur die dikte van die plaat. Hierdie teorie word gesien as ’n verbetering op die standaard teorieë soos bv. Kircho of Timoshenko se teorieë. Daar bestaan baie optimeringstegnieke wat geklassifiseer word as ’direkte soek’ of ’hellinggebaseerde’ metodes. Partikel swerm-optimering (PSO) is ’n direkte soekmetode. Dit boots die gedrag van ’n swerm voëls of ’n skool visse in hulle poging om kos te vind, na. PSO se wiskundige stelling karakteriseer ’n aanvanklike stel onbekende partikels binne ’n afgebakende soekgebied wat vergelyk word met ’n stel van die beste plaaslike partikels sowel as ’n enkele beste globale partikel. Die vergelykings word gebruik om die swerm met elke siklus op te dateer. Regressie is ’n metode waarin toetsdata gebruik word om ’n benaderde funksie te konstrueer wat ongeveer voorspel hoe die regte funksie lyk. Die doel van hierdie werk is om die piezoelektriese saamgestelde laag te optimeer en die interaksie van die totale gedrag van die struktuur te verbeter. Uitgebreide modellering word uitgevoer en getoets met eweknie-beoordeelde literatuur om die akkuraatheid en korrektheid te bewys.

Ces travaux de recherche abordent un problème de défense anti-aérien, usuellement appelé problème d'allocation d'armes à des cibles dans la littérature. L'allocation d'armes à des cibles est un problème bien connu de la recherche opérationnelle militaire qui a rencontré un franc succès parmi la communauté des chercheurs, et qui aujourd'hui encore suscite un large engouement puisque sa propriété démontrée NP-difficile en fait un problème qui reste irrésolu. Que ce soit par des méthodes analytiques ou meta-heuristiques, le problème d'allocation d'armes à des cibles a fait l'objet de nombreuses propositions de résolution. Cependant, il est assez surprenant de voir que la modélisation proposée pour ce problème n'a guère évolué depuis qu'il est apparu pour la première fois dans la littérature en 1950. Cette modélisation peut être considérée comme obsolète aujourd'hui et ne répond plus aux exigences qui accompagnent les technologies modernes. En effet, en 60 ans le champ de bataille a complètement changé, et dans la littérature seulement un nombre limité d'études proposent de prendre en compte ces évolutions. L'étude menée dans cette thèse propose de s'intéresser aux systèmes de Commandes et Contrôles (C2) pour des applications anti-aériennes. Habituellement un système C2 est composé de senseurs, d'un centre d'opérations tactiques et d'un ou plusieurs lanceurs. Les senseurs alimentent le centre d'opérations tactiques à partir des informations qu'ils recueillent, puis, une fois ces informations reçues, le centre d'opérations tactiques va interpréter ces données afin de calculer l'atteignabilité des menaces. Enfin, un plan d'engagement qui comprend l'allocation des munitions disponibles aux cibles et une date de tir sont proposés à un opérateur humain qui aura pour mission de valider cette proposition en totalité ou partiellement, puis va procéder à l'engagement des menaces. Pour remplir cet objectif, une approche innovante et faisant l'objet d'un dépôt de brevet a été développée afin de répondre aux difficultés relatives aux problèmes d'optimisation multi-objectifs. Ensuite, un algorithme d'optimisation continue basé sur la combinaison de l'optimisation par essaim particulaires avec la théorie des jeux évolutionnaires est proposé pour optimiser les dates de tirs. L'allocation optimale, elle, est obtenue en adaptant cette méthode continue au cas discret. La preuve que l'algorithme développé est localement convergent est donnée dans cette thèse. D'autre part, l'aspect temps-réel a également fait l'objet d'une recherche attentive et l'algorithme précédemment cité a été hybridé avec les réseaux de neurones afin d'accélérer le temps de calcul des composants identifiés comme "lourds" en termes de charge de calcul. Enfin, cette étude ne se limite pas à une application de recherche opérationnelle militaire, mais inclut quelques concepts élémentaires de guidage et de navigation pour le calcul d'atteignabilité des menaces. Finalement, cette thèse permet d'identifier que les points suivants doivent faire l'objet d'une attention très particulière afin de développer un outil d'aide à la décision efficace. D'abord, la métrique d'évaluation d'un bon plan d'engagement doit être clairement analysée. Ensuite, le plan d'engagement proposé doit être stable et ne pas proposer de changements soudains qui pourraient perturber l'opérateur. Le troisième point concerne la robustesse de la solution proposée et sa capacité à faire face aux situations les plus compliquées. Quatrièmement, le temps et la charge de calcul sont des contraintes techniques qui ne peuvent pas être outrepassées. Finalement, les exigences posées lors de la préparation de mission et qui dépendent du contexte doivent faire l'objet d'une attention particulière. C'est pourquoi, l'outil d'aide à la décision proposé doit permettre un allègement significatif de la charge de travail de l'opérateur ainsi que la réduction considérable du stress lié à ce contexte / This research investigates a practical air defence problem, usually named Weapon Target Assignment (WTA) in the literature. The WTA problem is a well-known problem of military operation research that encountered a wide success in the research community, but still nowadays since it remains an unsolved problem because of its NP-hardness property. From analytical to heuristic methods, the WTA was deeply investigated and many attempts to solve this problem have been proposed. However, the proposed modelling of this problem is consistent with the 1950's technologies. Thus, the proposed modelling found in the literature can be considered as obsolete and cannot fit the requirement of the current technology advances. Indeed, the battle field dramatically changes over 60 years, and the recent literature proposes only few studies taking into account these amendments. The herein study proposes to investigate a Command & Control system (C2) in air defence applications. Usually a C2 system includes sensors, a Tactical Operation Centre (TOC) and one or more launchers. The sensors provide information about aerial tactical situation to the TOC. This TOC is in charge of evaluating the received information in order to compute the attainability of the targets, then an engagement plan that includes the assignment of the available weapons to the incoming targets and a date to fire for each assignment. This engagement plan is then proposed to one human operator in charge of accepting whole or part of this engagement plan and engage the targets following the received instructions. To achieve this goal, an innovative and patented approach to mitigate the issues related to multi-objective optimisation is proposed. Then, a continuous optimisation algorithm based on the combination of the Particle Swarm Optimisation and the Evolutionary Game Theory was proposed to determine the best dates to fire. The optimal assignment was obtained by adapting the aforementioned algorithm to the discrete case. This thesis also gives the proof that the designed algorithms are locally convergent and intensive benchmarking confirms the developed theory. In order to respect the real-time requirement, it was also devised to use the Neural Networks to lighten the identified burdensome parts of the algorithm and decrease computational time. Not limited to the military operation research field, the herein study reuse some basic concepts of missile guidance and navigation to compute the attainability of the targets. From this thesis, it can be identified that following aspects need to be carefully considered to provide an efficient decision making support to a human operator: First, clearly define what a good engagement plan is. Second, the engagement plan must be steady to avoid high rate changing in the assignments that could significantly disturb the operator. Third, the proposed engagement also must be reliable and robust to face any possible situations. Fourth, the computation time and computation load are technical constraints that cannot be overstepped. Finally, the operational constraints related to the mission context defined during a pre-mission stage must also be taken into account. Therefore, the proposed decision making support must help and significantly reduce the operator's work load in this situation of high stress and sensitive context

A successful Chemical Enhanced Oil Recovery (CEOR) program starts with a proper process selection for a given field, followed by a formulation of the batch components and a representative simulation step. Also, lab studies, field data, pilot testing, and economic analyses are required before project implementation. This work discusses the state of the art of the Surfactant-Polymer flood (SP) EOR technique, specifically for carbonate reservoirs, and states a methodology mixing laboratory, literature and reservoir simulation, to assess its applicability under economic and geological uncertainties. First, it is explained concepts related to the research, such as polymer, surfactant, microemulsion, functionalities of each chemical injected, advantages, and disadvantages. Second, a state of the art is developed about recent SP advances. Third, it is described the laboratory method being used to evaluate some properties of the chemicals injected for the Polymer flooding (PF) and SP flooding. Later, the simulation study step being conducted is explained, which will define the volume recovered and Net Present Value (NVP) obtained for the PF, SP injections and water flooding, in different economic and geological scenarios for two models resembling carbonate Brazilian reservoirs. Finally, it is discussed the results obtained, future researches that could be performed, and the respective bibliography. As part of this research, it was verified the Xanthan gum shows adequate results at different concentrations; that a surfactant specifically selected for a carbonate rock with low Interfacial tension and low adsorption is required; also that for the Lula based model although the polymer flooding and Surfactant-Polymer simulation brought some benefits, when compared with the waterflooding, on different economic scenarios and geological models, the high cost associated to the chemical handling facilities and volume spent do not make favorable its application in any scenario. On the contrary for the Cerena I field model, it was found the SP and Polymer flooding on all cases brought better results when compared with the water injection. Concluding that the performance and success of a CEOR program require finding the correct slug characteristics for the unique conditions of each reservoir. In this research the reservoir with higher production rates made possible the use of Chemical EOR presenting better results than a water injection however in the smaller model they were not economically viable due to the additional associated prices. / Um programa bem-sucedido de recuperação melhorada de petróleo por método químico (CEOR) começa com uma seleção precisa do processo para um determinado campo, seguido pela formulação dos componentes e uma etapa de formulação representativa. Adicionalmente, testes laboratoriais, dados de campos, testes pilotos e análises econômicas são necessárias antes da implementação de um projeto. Este trabalho discute o estado da arte da técnica de recuperação melhorada de petróleo (EOR) pela injeção de surfactante-polímero (SP), especificamente para reservatórios carbonáticos e, utilizada uma metodologia baseada em dados de laboratório, literatura e de simulação de reservatório para avaliar sua aplicabilidade sob incertezas econômicas e geológicas. Primeiramente, são explicados conceitos necessários a este trabalho relacionados com polímero, surfactante, microemulsão, funcionalidades de cada produto químico injetado, vantagens e desvantagens. Em segundo lugar, um estado da arte é desenvolvido sobre os avanços recentes do SP. Após, descreve-se os métodos laboratoriais utilizados para avaliar algumas propriedades dos produtos químicos usados nas injeções de Polímeros (PF) e SP. Posteriormente, é explicada a etapa do estudo de simulação, que definirá o volume recuperado e o valor presente líquido (NVP), obtidos para injeções PF, SP e água, em diferentes cenários econômicos e geológicos, para dois modelos semelhantes a reservatórios carbonáticos brasileiros. Por fim, são discutidos os resultados obtidos, sugestões de trabalhos futuros e apresentação da bibliografia. Como parte desta pesquisa, verificou-se que a goma xantana apresenta resultados consistentes em diferentes concentrações e que é necessário um surfactante especificamente selecionado para uma rocha carbonática, possuindo baixa tensão interfacial e baixa adsorção. Para o modelo baseado em Lula, embora a simulação de injeção de polímero e surfactante-polímero tenham trazido alguns benefícios, quando comparados com a injeção de água, em diferentes cenários econômicos e modelos geológicos, o alto custo associado às instalações de manipulação química e volume gasto não favorece sua aplicação em qualquer cenário. Por outro lado, no modelo de campo Cerena I, verificou-se que as injeções de SP e de polímero, em todos os casos, trouxeram melhores resultados quando comparadas com a injeção de água. Concluindo, o desempenho e o sucesso de um programa de CEOR exige encontrar as corretas características de slugs para condições únicas de cada reservatório. Neste trabalho, o reservatório com maiores taxas de produção infere que o método químico de EOR apresente melhores resultados quando comparado com a injeção de água.

Les métaheuristiques sont une famille d'algorithmes stochastiques destinés à résoudre des problèmes d 'optimisation difficile . Utilisées dans de nombreux domaines, ces méthodes présentent l'avantage d'être généralement efficaces, sans pour autant que l'utilisateur ait à modifier la structure de base de l'algorithme qu'il utilise. Parmi celles-ci, l'Optimisation par Essaim Particulaire (OEP) est une nouvelle classe d'algorithmes proposée pour résoudre les problèmes à variables continues. Les algorithmes d'OEP s'inspirent du comportement social des animaux évoluant en essaim, tels que les oiseaux migrateurs ou les poissons. Les particules d'un même essaim communiquent de manière directe entre elles tout au long de la recherche pour construire une solution au problème posé, en s'appuyant sur leur expérience collective. Reconnues depuis de nombreuses années pour leur efficacité, les métaheuristiques présentent des défauts qui rebutent encore certains utilisateurs. Le réglage des paramètres des algorithmes est un de ceux-ci. Il est important, pour chaque probléme posé, de trouver le jeu de paramètres qui conduise à des performances optimales de l'algorithme. Cependant, cette tâche est fastidieuse et coûteuse en temps, surtout pour les utilisateurs novices. Pour s'affranchir de ce type de réglage, des recherches ont été menées pour proposer des algorithmes dits adaptatifs . Avec ces algorithmes, les valeurs des paramètres ne sont plus figées, mais sont modifiées, en fonction des résultats collectés durant le processus de recherche. Dans cette optique-là, Maurice Clerc a proposé TRIBES, qui est un algorithme d'OEP mono-objectif sans aucun paramètre de contrôle. Cet algorithme fonctionne comme une boite noire , pour laquelle l'utilisateur n'a qu'à définir le problème à traiter et le critàre d'arrêt de l'algorithme. Nous proposons dans cette thèse une étude comportementale de TRIBES, qui permet d'en dégager les principales qualités et les principaux défauts. Afin de corriger certains de ces défauts, deux modules ont été ajoutés à TRIBES. Une phase d'initialisation régulière est insérée, afin d'assurer, dès le départ de l'algorithme, une bonne couverture de l'espace de recherche par les particules. Une nouvelle stratégie de déplacement, basée sur une hybridation avec un algorithme à estimation de distribution, est aussi définie, afin de maintenir la diversité au sein de l'essaim, tout au long du traitement. Le besoin croissant de méthodes de résolution de problèmes multiobjectifs a conduit les concepteurs à adapter leurs méthodes pour résoudre ce type de problème. La complexité de cette opération provient du fait que les objectifs à optimiser sont souvent contradictoires. Nous avons élaboré une version multiobjectif de TRIBES, dénommée MO-TRIBES. Nos algorithmes ont été enfin appliqués à la résolution de problèmes de seuillage d'images médicales et au problème de dimensionnement de composants de circuits analogiques / Metaheuristics are a new family of stochastic algorithms which aim at solving difficult optimization problems. Used to solve various applicative problems, these methods have the advantage to be generally efficient on a large amount of problems. Among the metaheuristics, Particle Swarm Optimization (PSO) is a new class of algorithms proposed to solve continuous optimization problems. PSO algorithms are inspired from the social behavior of animals living in swarm, such as bird flocks or fish schools. The particles of the swarm use a direct way of communication in order to build a solution to the considered problem, based on their collective experience. Known for their e ciency, metaheuristics show the drawback of comprising too many parameters to be tuned. Such a drawback may rebu some users. Indeed, according to the values given to the parameters of the algorithm, its performance uctuates. So, it is important, for each problem, to nd the parameter set which gives the best performance of the algorithm. However, such a problem is complex and time consuming, especially for novice users. To avoid the user to tune the parameters, numerous researches have been done to propose adaptive algorithms. For such algorithms, the values of the parameters are changed according to the results previously found during the optimization process. TRIBES is an adaptive mono-objective parameter-free PSO algorithm, which was proposed by Maurice Clerc. TRIBES acts as a black box , for which the user has only the problem and the stopping criterion to de ne. The rst objective of this PhD is to make a global study of the behavior of TRIBES under several conditions, in order to determine the strengths and drawbacks of this adaptive algorithm. In order to improve TRIBES, two new strategies are added. First, a regular initialization process is defined in order to insure an exploration as wide as possible of the search space, since the beginning of the optimization process. A new strategy of displacement, based on an hybridation with an estimation of distribution algorithm, is also introduced to maintain the diversity in the swarm all along the process. The increasing need for multiobjective methods leads the researchers to adapt their methods to the multiobjective case. The di culty of such an operation is that, in most cases, the objectives are con icting. We designed MO-TRIBES, which is a multiobjective version of TRIBES. Finally, our algorithms are applied to thresholding segmentation of medical images and to the design of electronic components

A proteção de sistemas de energia elétrica possui papel extremamente importante no aspecto de garantir o fornecimento de energia de maneira segura e confiável. Assim, a ação indevida ou a não atuação deste sistema de proteção pode causar danos materiais ou econômicos tanto para as concessionárias quanto para os consumidores de energia elétrica. Dessa forma, o sistema de proteção deve estar bem ajustado para que possa garantir suas funções, sendo sensível, seletivo, confiável e rápido. Para tanto, uma boa coordenação entre os relés de proteção deve ser estabelecida. No caso de um sistema de transmissão, o qual costuma ser um sistema malhado, a proteção é comumente realizada por relés de distância aliados a relés de sobrecorrente com unidade direcional, sendo que estes funcionam como elemento de retaguarda daqueles. O processo de ajuste desses relés é um trabalho muito difícil e demorado, que pode ainda estar sujeito a erros do engenheiro de proteção responsável pelo estudo. Neste contexto, este trabalho tem como objetivo desenvolver uma metodologia baseada na otimização por enxame de partículas que obtenha automaticamente os ajustes desses relés de forma a garantir a coordenação e seletividade entre eles, tornando assim o processo de ajuste mais rápido e preciso. Dessa forma, essa metodologia pode constituir uma ferramenta de auxílio muito favorável ao engenheiro de proteção. Além disso, como em todo problema de otimização, a função objetivo e as restrições foram definidas de maneira a retratar o problema de coordenação envolvendo tanto os relés de distância quanto os direcionais de sobrecorrente. A metodologia foi aplicada a dois sistemas, um fictício com 16 relés e um sistema de transmissão real com 44 relés, sendo que em ambos os casos ela apresentou resultados bastante satisfatórios proporcionando ajustes bem coordenados. / Electrical power systems protection has a very important role in the aspect of ensuring energy supply with safety and reliability. Thus, improper action or non-actuation of this protection system can cause materials and/or economics damages to electricity utilities as well as ordinary energy consumers. Therefore the protection system must be well adjusted so it can ensure its functions and thus being sensible, selective, reliable and fast. In order to achieve these characteristics, the protective relays must be well coordinated. In the case of meshed transmission systems, the protection is generally performed by distance relays as primary protection associated with directional overcurrent relays as backup protection. The process of adjusting these relays is very tough, slow and it can even be subject to the protection engineer mistakes. In this context, this work aims to develop a particle swarm optimization based methodology that can automatically obtain these relays adjusts so they can ensure relays coordination and selectivity, and then make this setting process faster and more precise. Thus, this methodology may provide a very favorable tool to aid the protection engineer. Moreover, as in any optimization problem, the objective function and the constraints were defined to represent the coordination problem involving both distance and directional overcurrent relays. The methodology was applied to two systems, a fictitious with 16 relays and a real transmission system with 44 relays, and in both cases it showed satisfactory results providing well-coordinated settings.

Le travail présenté dans ce mémoire porte sur l'étude, l'optimisation et la réalisation d'une transmission planétaire magnétique. Dans notre thèse nous essayons de répondre à quelques questions intéressantes sur la possibilité de remplacer un train planétaire mécanique par un train planétaire magnétique, est-ce que la formule de Willis reste valable pour le train planétaire magnétique et est-ce que les trains magnétiques ont des performances similaires à celles des trains mécaniques ? Donc nous étudions, le remplacement du train mécanique par une transmission magnétique. Nous montrons que le train magnétique a un volume moindre, des pertes inférieures et plusieurs autres avantages. Notre but dans cette thèse est d'obtenir un "design" optimal d'un train magnétique. Nous utilisons un logiciel de calcul par éléments finis pour l'étude électromagnétique et nous cherchons également à optimiser les dimensions de ce train. Pour cela nous utilisons la méthode d'optimisation par essaim de particules (OEP). Un prototype a été réalisé ce qui permet de confronter les résultats de simulation et expérimentaux. / The work presented in this thesis deals with the study, the optimisation and the realisation of a magnetic planetary transmission. We try to answer some questions about the possibility of replacing the mechanical planetary gear used in industrial machines by a magnetic planetary gear; is the formula of Willis still valid for the magnetic planetary gear and are the magnetic planetary gear performances at least similar to ones of the mechanical gears? We study the replacement of the mechanical planetary gear by a magnetic one. We show that the magnetic one has a smaller volume, lower losses and many other benefits. The objective of this work is to obtain an optimum design of a magnetic planetary gear. We use a finite element software to study the magnetic behaviour of the device and we also perform the optimization of the dimensions of the magnetic planetary gear. The particle swarm optimization method (PSO) has been used. A prototype has been built so the computation results has been compared to the experimental ones.

Made available in DSpace on 2014-06-12T17:36:41Z (GMT). No. of bitstreams: 2 arquivo9409_1.pdf: 2593707 bytes, checksum: c82123beb5138e539b7e5a7a30279094 (MD5) license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5) Previous issue date: 2012 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Nesta Tese são propostas diversas estratégias para projetar redes ópticas WDM de forma otimizada. As redes são consideradas com tráfego dinâmico e penalizadas pelas penalidades da camada física. As estratégias propostas lidam com os principais elementos que afetam a relação custo desempenho em uma rede óptica: o algoritmo de roteamento e atribuição de comprimentos de onda (RWA), a colocação de regeneradores (RP), a atribuição de regeneradores (RA), o projeto da topologia física da rede (PTD) e o dimensionamento dos dispositivos ópticos (DDO) que serão instalados na rede. Esses problemas são tratados tanto de forma separada quanto de forma integrada na Tese. Para o RWA, é proposta uma metodologia para se projetar algoritmos heurísticos de roteamento que têm como objetivo o aumento no desempenho da rede e que levam em conta as penalidades da camada física. Para a solução do RP são propostos algoritmos heurísticos e metaheurísticos para o projeto de redes ópticas translúcidas, considerando simultaneamente a otimização dos custos de capital (CapEx) e operacional (OpEx) e do desempenho da rede. O problema de PTD é tratado em conjunto com o DDO também de forma mutiobjetiva, considerando a otimização simultânea do CapEx e do desempenho (probabilidade de bloqueio). Um algoritmo multiobjetivo para realização da expansão de topologia (i.e. adição de novos enlaces a uma rede já existente) também é proposto. Além disso, são resolvidos conjuntamente os problemas de PTD, RP e RWA de forma mutiobjetiva considerando a otimização simultânea de CapEx e desempenho da rede. As otimizações das soluções são feitas utilizando as seguintes estratégias metaheuristicas propostas na área de inteligência computacional: Particle Swarm Optimization (PSO) e Non-dominated Sorting Genetic Algorithm II (NSGA-II)

Este trabalho propõe uma metodologia baseada em técnicas inteligentes capaz de fornecer uma coordenação otimizada de relés direcionais de sobrecorrente instalados em sistemas de energia elétrica. O problema é modelado como um caso de programação não linear inteira mista, em que os relés permitem ajustes discretizados de múltiplos de tempo e/ou múltiplos de corrente. A solução do problema de otimização correspondente é obtida através de uma metaheurística nomeada como Discrete Particle Swarm Optimization. Na literatura técnico-científica esse problema geralmente é linearizado e aplicam-se arredondamentos das variáveis discretas. Na metodologia proposta, as variáveis discretas são tratadas adequadamente para utilização na metaheurística e são apresentados os resultados que foram comparados com os obtidos pelo modelo clássico de otimização implementado no General Algebraic Modeling System (GAMS). Tendo em vista os aspectos observados, o método permite ao engenheiro de proteção ter um subsídio adicional na tarefa da coordenação dos relés direcionais de sobrecorrente, disponibilizando uma técnica eficaz e de fácil aplicabilidade ao sistema elétrico a ser protegido, independentemente da topologia e condição operacional. / This work proposes a methodology that based on intelligent technique to obtain an optimized coordination of directional overcurrent relays in electric power systems. The problem is modeled as a mixed integer nonlinear problem, because the relays allows a discrete setting of time and/or current multipliers. The solution of the proposed optimization problem is obtained from the proposed metaheuristic named as Discrete Particle Swarm Optimization. In scientific and technical literature this problem is usually linearized and discrete variables are rounded off. In the proposed method, the discrete variables are modeled adequately in the metaheuristic and the results are compared to the classical optimization solvers implemented in General Algebraic Modeling System (GAMS). The method provides an important method for helping the engineers in to coordinate directional overcurrent relays in a very optimized way. It has high potential for the application to realistic systems, regardless of topology and operating condition.