Os Sistemas de Armazenamento a Baterias (SAEB) têm demonstrado uma grande flexibilidade de aplicações em melhorias e resoluções de problemas em Sistemas de Distribuição de Energia Elétrica (SDEEs). Grandes variações no valor de tensão dentro de um período, seja diário ou semanal, são observados devido à predominante topologia radial dos SDEEs e o constante aumento da utilização de Geradores Distribuídos (GDs). Pelas características de operar como carga ou geração, os SAEBs podem ser utilizados para melhorar o perfil de tensão. No entanto, as restrições de operação desses dispositivos tornam dificultoso identificar bons momentos de atuação e barramentos de alocação para este propósito. Geralmente, essa atuação é tratada de uma forma dependente dos GDs, porém essa abordagem não permite que os SAEBs possam operar em momentos independentes a fim de melhorar o perfil de tensão do SDEE. Desta forma, neste trabalho é desenvolvida uma abordagem para o planejamento da alocação e atuação do SAEB, utilizando uma modificação no algoritmo Particle Swarm Optimization (PSO) de forma que o SAEB possa atuar independente dos GDs e ser alocado em outras barras, com o objetivo de melhorar o perfil de tensão. As soluções são analisadas através de simulações de Monte Carlo para investigar o comportamento em diversas situações de curva de carga. Os resultados demonstram que a abordagem proposta busca encontrar boas alocações e atuações e que os parâmetros técnicos dos SAEBs, como capacidade de energia armazenada e potência nominal do inversor, influenciam diretamente nos resultados. / Battery Energy Storage Systems (BESS) have demonstrated great flexibility of applications in improvements and problem-solving in Electrical Distribution Systems (DSs). Significant variations in the nominal voltage value within a period, either daily or weekly, are observed due to the predominant radial topology of the DSs and the constant increase of the use of Distributed Generators (DGs). By the characteristics of operating as load or generation, SAEBs can be used to improve the voltage profile. However, the restrictions of these devices make it difficult to identify good operating moments and allocation buses for this purpose. Generally, this operation is treated in a way dependent on the DGs, but this strategy does not allow the BESS to operate at independent moments to improve the voltage profile of the SDEE. Thus, in this work an approach is developed for SAEBs allocation and operation planning, using a modification in the Particle Swarm Optimization (PSO) algorithm so that the SAEB can operate independently of the GDs and be allocated in other bars, with the objective to improve the voltage profile. The solutions are analyzed through Monte Carlo simulations to investigate the behavior in various load curve situations. The results demonstrate that the proposed approach seeks to find proper allocations and actions and that the technical parameters of SAEBs directly influence the results.

The power transmission network is stretched thin in Western North America. When generators or substations fault, the resultant cascading failures can diminish transmission capabilities across wide regions of the continent. This thesis examined several methods of<br><div>determining one-shot controls based on frequency decline in electrical generators to reduce the effect of one or more phase faults and tripped generators. These methods included criteria based on indices calculated from frequency measured at the controller location. These indices included criteria based on local modes and the rate of change of frequency.</div><br>This thesis primarily used particle swarm optimization (PSO) with inertia to determine a well-adapted set of parameters. The parameters included up to three thresholds for indices calculated from frequency. The researchers found that the best method for distinguishing between one or more phase faults used thresholds on two Fourier indices. Future lines of research regarding one-shot controls were considered.<br><div><br></div><div>A method that distinguished nearby tripped generators from one or more phase faults and load change events was proposed. This method used a moving average, a negative<br></div>threshold for control, and a positive threshold to reject control. The negative threshold for the moving average is met frequently during any large transient event. An additional index must be used to distinguish loss of generation events. This index is the maximum value of the moving average up to the present time and it is good for distinguishing loss of<br>generation events from transient swings caused by other events.<br><br><div>This thesis further demonstrated how well a combination of controls based on both rate of change of frequency and local modes reduces instability of the network as determined by both a reduction in RMSGA and control efficiency at any time after the events.</div><br>This thesis found that using local modes is generally useful to diagnose and apply one-shot controls when instability is caused by one or more phase faults, while when disconnected generators or reduced loads cause instability in the system, the local modes did not distinguish between loss of generation capacity events and reduced load events. Instead, differentiating based on the rate of change of frequency and an initial upward deflection of frequency or an initial downward deflection of frequency did distinguish between these types of events.

Submitted by Marlucy Farias Medeiros (marlucy.farias@unipampa.edu.br) on 2017-10-02T18:12:15Z No. of bitstreams: 1 Robson André Domanski - 2016.pdf: 5137261 bytes, checksum: 1e4aac0a601a8fb57b3a32e21268568a (MD5) / Approved for entry into archive by Marlucy Farias Medeiros (marlucy.farias@unipampa.edu.br) on 2017-10-04T17:34:54Z (GMT) No. of bitstreams: 1 Robson André Domanski - 2016.pdf: 5137261 bytes, checksum: 1e4aac0a601a8fb57b3a32e21268568a (MD5) / Made available in DSpace on 2017-10-04T17:34:54Z (GMT). No. of bitstreams: 1 Robson André Domanski - 2016.pdf: 5137261 bytes, checksum: 1e4aac0a601a8fb57b3a32e21268568a (MD5) Previous issue date: 2016-12-13 / O projeto de circuitos integrados analógicos, dentro da indústria da microeletrônica tem a sua evolução ditada pela grande necessidade da integração de circuitos mistos. Esta evolução faz com que os dispositivos semicondutores sejam cada vez mais miniaturizados, o que implica na complexidade cada vez maior no processo de fabricação, resultando em uma grande variabilidade de parâmetros. Esta complexidade no projeto está diretamente ligada ao dimensionamento dos dispositivos que compõem o circuito, já que o espaço de projeto é altamente não-linear. O dimensionamento de circuito analógico pode ser modelado como um problema de otimização e resolvido por heurísticas de otimização. A solução resultante é dependente da estratégia de modelagem e na estimativa de desempenho, o que é feito, em geral, por simulação elétrica. Neste contexto, foi desenvolvida a ferramenta UCAF. No entanto, a solução otimizada cai na fronteira do espaço de projeto, onde uma pequena variação nos parâmetros do dispositivo afeta o desempenho do circuito. Isso conduz à inclusão de simulação Monte Carlo no circuito de otimização, aumentando o esforço computacional. O objetivo principal deste trabalho é analisar dois métodos diferentes de amostragem, a fim de reduzir o número de rodadas Monte Carlo, e a inserção da heurística de otimização Particle Swarm Optimization, visando a minimização do tempo necessário para o dimensionamento do circuito. A amostragem por hipercubo latino, a qual requer um número menor de amostras para um nível de confiança razoável, é utilizado nas primeiras iterações do processo de otimização. Depois de um certo ponto, o método de amostragem é alterado para a amostragem aleatória tradicional. A heurística Particle Swarm Optimization foi implementada na ferramenta UCAF, devido ao seu baixo custo computacional. A metodologia é aplicada para o dimensionamento de um amplificador de transcondutância operacional OTA Miller e um amplificador Telescopic, mostrando vantagens em termos de tempo de processamento e desempenho do circuito. Pode-se demonstrar que a utilização de uma nova heurística, e diferentes métodos de amostragem para a simulação Monte Carlo no processo de otimização produz uma busca mais eficiente no espaço de projeto com um ganho em relação ao esforço computacional. / The analog integrated circuit design within the microelectronics industry has its evolution dictated by the great need for integration of mixed circuits. This trend makes the semiconductor devices are increasingly miniaturized, which implies the increasing complexity in the manufacturing process, resulting in a large variability op parameters. This complexity is directly linked to the design of devices that compose the circuit, since the design space is highly nonlinear. The design of analog circuit can be modeled as an optimization problem and solved by optimization heuristics. The resulting solution is dependent on modeling strategy and performance estimation, which is done generally by electrical simulation. In this context, the UCAF tool was developed. However, the optimized solution falls on the border of the design space where a small variation in device parameters affect circuit performance. This leads to the inclusion of Monte Carlo simulation on the circuit optimization, increasing the computational effort. The main objective of this study is to analyze two different methods of sampling, in order to reduce the number of Monte Carlo runs, and the inclusion of a new heuristic optimization, in order to minimize the time required for the design of the circuit. The Latin hypercube sampling, which requires a smaller number of samples for a reasonable confidence level is used in the first iteration of the optimization process. After a certain point, the sampling method is changed to the traditional random sampling. Heuristic Particle Swarm Optimization was implemented in UCAF tool, due to its low computational cost. The methodology is applied for the design of a Miller and a Telescopic operational transconductance amplifier, showing advantages in terms of processing time and circuit performance. We can demonstrate that the use of a new heuristic, and different methods of sampling for Monte Carlo simulation in the optimization process produces a more efficient search of the design space, and advantages in relation to computational effort.

Esta tese consiste em apresentar um método para localização e identificação de consumidores com alta contribuição para a distorção harmônica de tensão medida em subestações de sistemas de distribuição de energia elétrica. Cabe comentar que a etapa de localização visa obter uma lista das possíveis posições onde possa estar alocado o consumidor que possua cargas não lineares com grande consumo de potências harmônicas. Partindo-se desta lista, realiza-se a etapa de identificação, em que são estimadas as amplitudes de cada harmônica na posição selecionada. Por fim, um algoritmo para ajuste/sintonia do método de localização é empregado com o intuito de se realizar uma possível correção com relação à posição do consumidor. Desta forma, por meio de estudos de caso (simulados), os resultados obtidos procuram validar a metodologia proposta. / This thesis provides a method for location and identification of consumers with larger contribution to harmonic distortion of voltage in power distribution substations. It is worth to mention that the stage of consumers location must furnish a list of possible positions where there may be consumers, who have nonlinear loads with high consumption of harmonic power. From this list, the identification stage is performed in order to estimate the amplitude of each harmonic from the location selected. Finally, a method for improve the location algorithm is employed in order to refine the consumer position. Therefore, by means of simulated case studies, the results obtained for these stages seek to validate the methodology proposed.

Submitted by JOSIANE SANTOS DE OLIVEIRA (josianeso) on 2019-03-13T12:19:46Z No. of bitstreams: 1 Lucas Schmidt Goecks_.pdf: 2315393 bytes, checksum: 3985e874dfb67958cda4aa69697f671f (MD5) / Made available in DSpace on 2019-03-13T12:19:46Z (GMT). No. of bitstreams: 1 Lucas Schmidt Goecks_.pdf: 2315393 bytes, checksum: 3985e874dfb67958cda4aa69697f671f (MD5) Previous issue date: 2018-12-30 / CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Como uma das atividades mais importantes na engenharia de produção, o planejamento de instalações consiste na tomada de decisões relativas ao leiaute dos setores, unidades de produção/ fabricação, locais de armazenamento, e assim por diante. Conceito que é apoiado pela variabilidade dos processos produtivos, que muda de um período de produção para outro e de uma empresa para a outra. Atualmente, a literatura apresenta abordagens de como solucionar o problema de leiaute para empresas de pequeno e médio porte com modelos de planejamento, e de tomada de decisão multicritérios, ou meta-heurísticos. A literatura aborda estes dois métodos de forma separada. Inclusive, não existem relatos de comparações entre eles desde o conhecimento do autor. Como resposta à esta lacuna de pesquisa, definiu-se o seguinte objetivo: "identificar um método para planejamento de leiautes aplicável em empresas de pequeno e médio porte". A meta foi desenvolver uma ferramenta de modelagem genérica e que atenda à diferentes necessidades. Sendo assim, este trabalho abordou o Systematic Layout Planning (SLP) e o Particle Swarm Optimization (PSO) para planejamento de leiautes, avaliando a melhor proposta pelo Analytic Hierarchy Process (AHP). Em decorrência de interesses práticos que visam à aplicação de ferramentas para a solução de problemas específicos, este trabalho classifica-se como pesquisa aplicada de abordagem quantitativa, embasado por processos de tomada de decisão e de modelagem. Os resultados obtidos demonstram que o SLP fornece melhores propostas de leiautes que o PSO, para pequenas e médias empresas. O SLP respeita a alocação adjacente dos setores de acordo com o fluxo de material, enquanto que o PSO distribui aleatoriamente as áreas produtivas, o que proporciona maior variabilidade nas propostas de leiautes. O SLP demandou maior tempo de planejamento e um método auxiliar (AHP) para definição da melhor proposta de leiaute. Já o PSO forneceu o melhor leiaute sem uma ferramenta de suporte e a simulação foi mais rápida após estruturação do modelo do algoritmo. Implicações práticas à esta pesquisa encontram-se na análise da redução de custos com dados reais. Foram identificados na literatura objetivos de otimização e restrições mais usuais. Quanto ao tipo de leiaute, conforme as características da empresa a ser explorada, será considerado o tipo job-shop/funcional. Esta pesquisa contribui ao meio acadêmico no âmbito de sintetizar dois métodos, distintos, para planejamento de leiautes e compará-los com uma ferramenta de tomada de decisão multicriterial. Ao meio empresarial, a mesma fornece métodos que podem ser incorporados ao cotidiano das empresas no que diz respeito ao planejamento de leiautes e tomada de decisões. / As one of the most important activities in production engineering, facility planning consists of making decisions regarding the layout of the sectors, production/manufacturing units, storage locations, and so on. This concept is supported by the variability of production processes, which changes from one period of production to another and from one company to another. Currently, the literature presents approaches of how to solve the problem of layout for small and medium-sized companies with models of planning, and decision-making multi-criteria, or metaheuristics. The literature addresses these two methods separately. In fact, there are no reports of comparisons between them since the knowledge of the author. In response to this research gap, the following objective was defined: "to identify a method for layout planning applicable to small and medium-sized enterprises". The objective was to develop a generic modeling tool that meets different needs. Thus, this work approached Systematic Layout Planning (SLP) and Particle Swarm Optimization (PSO) for the layout planning, evaluating the best proposal by the Analytic Hierarchy Process (AHP). Because of practical interests that aim at the application of tools for the solution of specific problems, this work is classified as applied research of quantitative approach, based on processes of decision-making and modeling. The results obtained demonstrate that SLP provides better layout proposals than the PSO, for small and medium enterprises. The SLP respects the adjacent allocation of the sectors according to the material flow, while the PSO randomly distributes the productive areas, which provides greater variability in the layout proposals. The SLP required greater planning time and an auxiliary method (AHP) to define the best layout proposal. The PSO provided the best layout without a support tool and the simulation was faster after structuring the algorithm model. Practical implications of this research lie in the analysis of cost reduction with real data. Optimization objectives and constraints that are more usual have been identified in the literature. As for the type of layout, according to the characteristics of the company, and because it is a single case study, the job-shop type will be considered. This research contributes to the academic environment in the context of synthesizing two distinct methods for planning layouts and comparing them with a multi-criteria decision-making tool. In the business environment, it provides methods that can be incorporated into companies’ day-to-day planning and decision making.

[pt] O potássio é um íon essencial para a nutrição de plantas, geralmente fornecido sob a forma de cloretos e sulfatos. De acordo com a disponibilidade e demanda brasileira de fertilizantes agrícolas, a importação de compostos portadores desse elemento químico é mandatória para atender a enorme demanda por esse nutriente. Assim, iniciativas que buscam fontes alternativas de potássio tornam-se cada vez mais interessantes e economicamente atraentes. Uma rota potencial está associada com a digestão com ácido sulfúrico de minerais portadores de glauconita e operações unitárias sequenciais para recuperar compostos de alumínio, ferro, magnésio e potássio. No contexto deste processo químico, o alúmen de potássio dodecahidrato aparece como um produto intermediário relevante que permite a recuperação seletiva de potássio e alumínio através de decomposição térmica seguida de solubilização em água e filtração. Com base no que foi dito, o presente trabalho investiga a cinética da decomposição do alúmen de potássio dodecahidratado sob condições não-redutoras e redutoras, e um novo modelo matemático é proposto para descrever a perda de massa ao longo do tempo. Uma abordagem estocástica, com o uso do método de otimização enxame de partículas, é empregada para estimar os parâmetros desconhecidos do modelo. As previsões do modelo são validadas por dados experimentais obtidos via análise termogravimétrica dinâmica em diferentes atmosferas de reação (inerte e oxidante), e com a presença ou não de agente redutor (finos de coque metalúrgico). Com os parâmetros do modelo validado, é possível usar o mesmo para monitorar as composições mássicas de todos os compostos presentes no meio assim como empregar o modelo futuramente para monitoramento online uma vez que sua simulação leva menos do que 1 s para simular 20 min de decomposição térmica. / [en] Potassium is an essential ion for plant nutrition, usually supplied in the form of chlorides and sulfates. According to Brazilian availability and demand of agriculture fertilizers, the importation of compounds carrying this chemical element is mandatory in order to fulfill the huge demand for this nutrient. So initiatives looking for alternative sources of potassium become increasingly interesting and economically attractive. A potential route is associated with the sulfuric digestion of glauconite-bearing greensands and sequential unit operations in order to recover aluminum, iron, magnesium and potassium compounds. In the context of this chemical process, the potassium alum dodecahydrate appears as a relevant intermediate product that allows the selective recovery of potassium and aluminum through thermal decomposition followed by solubilization in water and filtration. Based on what was said, the present work investigates the kinetics of potassium alum dodecahydrate decomposition under nonreductive and reductive conditions, and a novel mathematical model is proposed to describe the weight loss during time. A stochastic approach approach, using particle swarm optimization method, is employed to estimate the unknown model parameters. The model predictions are validated by experimental data obtained through dynamic thermogravimetric analysis at different reaction atmospheres (inert and oxidant), and with the presence or not of reducing agent (metallurgical coke breeze). With the validated model parameters, it is possible to use them to monitor the mass compositions of all compounds present in the process as well as to use the model for future online monitoring since its simulation takes less than 1 s to simulate 20 min of decomposition thermal.

Dans le cadre de l'optimisation multiobjectif, les métaheuristiques sont reconnues pour être des méthodes performantes mais elles ne rencontrent qu'un succès modéré dans le monde de l'industrie. Dans un milieu où seule la performance compte, l'aspect stochastique des métaheuristiques semble encore être un obstacle difficile à franchir pour les décisionnaires. Il est donc important que les chercheurs de la communauté portent un effort tout particulier sur la facilité de prise en main des algorithmes. Plus les algorithmes seront faciles d'accès pour les utilisateurs novices, plus l'utilisation de ceux-ci pourra se répandre. Parmi les améliorations possibles, la réduction du nombre de paramètres des algorithmes apparaît comme un enjeu majeur. En effet, les métaheuristiques sont fortement dépendantes de leur jeu de paramètres. Dans ce cadre se situe l'apport majeur de TRIBES, un algorithme mono-objectif d'Optimisation par Essaim Particulaire (OEP) qui fonctionne automatiquement,sans paramètres. Il a été mis au point par Maurice Clerc. En fait, le fonctionnement de l'OEP nécessite la manipulation de plusieurs paramètres. De ce fait, TRIBES évite l'effort de les régler (taille de l'essaim, vitesse maximale, facteur d'inertie, etc.).Nous proposons dans cette thèse une adaptation de TRIBES à l'optimisation multiobjectif. L'objectif est d'obtenir un algorithme d'optimisation par essaim particulaire multiobjectif sans paramètres de contrôle. Nous reprenons les principaux mécanismes de TRIBES auxquels sont ajoutés de nouveaux mécanismes destinés à traiter des problèmes multiobjectif. Après les expérimentations, nous avons constaté, que TRIBES-Multiobjectif est moins compétitif par rapport aux algorithmes de référence dans la littérature. Ceci peut être expliqué par la stagnation prématurée de l'essaim. Pour remédier à ces problèmes, nous avons proposé l'hybridation entre TRIBES-Multiobjectif et un algorithme de recherche locale, à savoir le recuit simulé et la recherche tabou. L'idée était d'améliorer la capacité d'exploitation deTRIBES-Multiobjectif. Nos algorithmes ont été finalement appliqués sur des problèmes de dimensionnement des transistors dans les circuits analogiques / Meta-heuristics are recognized to be successful to deal with multiobjective optimization problems but still with limited success in engineering fields. In an environment where only the performance counts, the stochastic aspect of meta-heuristics again seems to be a difficult obstacle to cross for the decision-makers. It is, thus, important that the researchers of the community concern a quite particular effort to ease the handling of those algorithms. The more the algorithms will be easily accessible for the novices, the more the use of these algorithms can spread. Among the possible improvements, reducing the number of parameters is considered as the most challenging one. In fact, the performance of meta-heuristics is strongly dependent on their parameters values. TRIBES presents an attempt to remedy this problem. In fact, it is a particle swarm optimization (PSO) algorithm that works in an autonomous way. It was proposed by Maurice Clerc. Indeed, like every other meta-heuristic, PSO requires many parameters to be fitted every time a new problem is considered. The major contribution of TRIBES is to avoid the effort of fitting them. We propose, in this thesis, an adaptation of TRIBES to the multiobjective optimization. Our aim is to conceive a competitive PSO algorithm free of parameters. We consider the main mechanisms of TRIBES to which are added new mechanisms intended to handle multiobjective problems. After the experimentations, we noticed that Multiobjective-TRIBESis not competitive compared to other multiobjective algorithms representative of the state of art. It can be explained by the premature stagnation of the swarm. To remedy these problems, we proposed the hybridization between Multiobjective-TRIBES and local search algorithms such as simulated annealing and tabu search. The idea behind the hybridization was to improve the capacity of exploitation of Multiobjective-TRIBES. Our algorithms were finally applied to sizing analogical circuits' problems

Because blades in jet engine compressors are subject to dynamic loads based on the engine's speed, it is essential that the blades are properly "tuned" to avoid resonance at those frequencies to ensure safe operation of the engine. The tuning process can be time consuming for designers because there are many parameters controlling the geometry of the blade and, therefore, its resonance frequencies. Humans cannot easily optimize design spaces consisting of multiple variables, but optimization algorithms can effectively optimize a design space with any number of design variables. Automated blade tuning can reduce design time while increasing the fidelity and robustness of the design. Using surrogate modeling techniques and gradient-free optimization algorithms, this thesis presents a method for automating the tuning process of an airfoil. Surrogate models are generated to relate airfoil geometry to the modal frequencies of the airfoil. These surrogates enable rapid exploration of the entire design space. The optimization algorithm uses a novel objective function that accounts for the contribution of every mode's value at a specific operating speed on a Campbell diagram. When the optimization converges on a solution, the new blade parameters are output to the designer for review. This optimization guarantees a feasible solution for tuning of a blade. With 21 geometric parameters controlling the shape of the blade, the geometry for an optimally tuned blade can be determined within 20 minutes.

Tese de mestrado integrado. Engenharia Electrotécnica e de Computadores (Área de Especialização de Energia). Faculdade de Engenharia. Universidade do Porto. 2012

In this thesis, the effect of pitch based optimisation was explored for a 80 turbine wind farm. Using a modified Jensen wake model and the Particle Swarm Optimisation (PSO) model, a pitch optimisation strategy was created for the dominant turbulence and atmospheric condition for the wind farm. As the wake model was based on the FLORIS model developed by P.M.O Gebraad et. al., the wake and power model was compared with the FLORIS model and a -0.090% difference was found. To determine the dynamic predictive capability of the wake model, measurement values across a 10 minute period for a 19 wind turbine array were used and the wake model under predicted the power production by 17.55%. Despite its poor dynamic predictive capability, the wake model was shown to accurately match the AEP production of the wind farm when compared to a CFD simulation done in FarmFlow and only gave a 3.10% over-prediction. When the optimisation model was applied with 150 iterations and particles, the AEP production of the wind farm increased by 0.1052%, proving that the pitch optimisation method works for the examined wind farm. When the iterations and particles used for the optimisation was increased to 250, the power improvement between optimised results improved by 0.1144% at a 222.5% increase in computational time, suggesting that the solution has yet to fully converge. While the solutions did not fully converge, they converged sufficiently and an increase in iterations gave diminishing results. From the results, the pitch optimisation model was found to give a significant increase in power production, especially in wake intensive wind directions. However, the dynamic predictive capabilities will have be improved upon before the control strategy can be applied to an operational wind farm.