Dissertationen zum Thema „Grid Optimization Method“

Finite element optimum meshes are synthesized by the use of thermal expansion principles in conjunction with an analogous temperature field computed from the element strain energy contents. Elements having high strain energy contents are shrunk and those with low strain energy contents are expanded until all elements contain the same amount of strain energy. Deviatoric strain energy is also used in place of the strain energy as the objective function for the optimization method. Both objective functions yield significant improvements of the meshes after only a few iterations. In one test case, the errors in the maximum stresses are reduced by more than 1/3 after 1 iteration. In another test case, the error in the stress concentration factor is reduced by more than 3/4 after 7 iterations.
M.S.

For the numerical simulation of many problems of engineering interest, it is desirable to have an automated mesh adaption tool. This is important especially for problems characterized by anisotropic features and require mesh clustering in the direction of high gradients. Another significant issue in meshing emerges in unsteady simulations with moving boundaries, where the boundary motion has to be accommodated by deforming the computational grid. Similarly, there exist problems where current mesh needs to be adapted to get more accurate solutions. To solve these problems, we propose three novel procedures. In the first part of this work, we present an optimization procedure for three-dimensional anisotropic tetrahedral grids based on metric-driven h-adaptation. Through the use of topological and geometrical operators, the mesh is iteratively adapted until the final mesh minimizes a given objective function. We propose an optimization process based on an ad-hoc application of the simulated annealing technique, which improves the likelihood of removing poor elements from the grid. Moreover, a local implementation of the simulated annealing is proposed to reduce the computational cost. Many challenging unsteady multi-physics problems are characterized by moving boundaries and/or interfaces. When the boundary displacements are large, degenerate elements are easily formed in the grid such that frequent remeshing is required. We propose a new r-adaptation technique that is valid for all types of elements (e.g., triangle, tet, quad, hex, hybrid) and deforms grids that undergo large imposed displacements at their boundaries. A grid is deformed using a network of linear springs composed of edge springs and a set of virtual springs. The virtual springs are constructed in such a way as to oppose element collapsing. Both frequent remeshing, and exact-pinpointing of clustering locations are great challenges of numerical simulations, which can be overcome by adaptive meshing algorithms. Therefore, we conclude this work by defining a novel mesh adaptation technique where the entire mesh is adapted upon application of a force field in order to comply with the target mesh or to get more accurate solutions. The method has been tested for two-dimensional problems of a-priori metric definitions as well as for oblique shock clusterings.

Nous traitons les problèmes lies aux maillages non structures par des méthodes d'optimisation utilisant des algorithmes d'exploration locale. Le principe consiste à partir d'une solution existante et a l'améliorer grâce a des opérations élémentaires. L'intérêt de cette approche est de pouvoir modifier localement la solution initiale pour qu'elle réponde a des contraintes ou des critères qui peuvent évoluer. On évite ainsi la reconstruction couteuse d'un nouveau maillage à chaque nouvelle demande des utilisateurs
Une étude théorique introduit de nouveaux objets, les tétraphores réalisables, en considérant les seules conditions topologiques d'un maillage. Ces objets se construisent facilement à partir de la frontière du domaine à mailler ; il suffit d'ajouter des contraintes géométriques, très simples à tester et pouvant se traduire sous la forme d'un critère à optimiser, pour obtenir un maillage. Des opérations transformant ces tétraphores sont définies. Les algorithmes d'optimisation sont ainsi bien plus efficaces car ils peuvent être appliques sur un ensemble plus vaste que les maillages
Les algorithmes décrits dans cette thèse sont utilisés industriellement. Des résultats sont donnes pour l'optimisation selon des critères géométriques et topologiques, l'adaptation selon un critère de densité, la correction après déformation des frontières et la génération de maillages

A method for solving the inverse geometrical problem is presented by reconstructing the unknown subsurface cavity geometry using boundary element methods, a genetic algorithm, and Nelder-Mead non-linear simplex optimization. The heat conduction problem is solved utilizing the boundary element method, which calculates the difference between the measured temperature at the exposed surface and the computed temperature under the current update of the unknown subsurface flaws and cavities. In a first step, clusters of singularities are utilized to solve the inverse problem and to identify the location of the centroid(s) of the subsurface cavity(ies)/flaw(s). In a second step, the reconstruction of the estimated cavity(ies)/flaw(s) geometry(ies) is accomplished by utilizing an anchored grid pattern upon which cubic spline knots are restricted to move in the search for unknown geometry. Solution of the inverse problem is achieved using a genetic algorithm accelerated with the Nelder-Mead non-linear simplex. To optimize the cubic spline interpolated geometry, the flux (Neumann) boundary conditions are minimized using a least squares functional. The automated algorithm successfully reconstructs single and multiple subsurface cavities within two dimensional mediums. The solver is also shown to accurately predict cavity geometries with random noise in the boundary condition measurements. Subsurface cavities can be difficult to detect based on their location. By applying different boundary conditions to the same geometry, more information is supplied at the boundary, and the subsurface cavity is easily detected despite its low heat signature effect at the boundaries. Extensions to three-dimensional applications are outlined.
M.S.M.E.
Masters
Mechanical and Aerospace Engineering
Engineering and Computer Science
Mechanical Engineering; Thermo-Fluids

Distributed infrastructures (e.g., water networks and electric Grids) are difficult to manage due to their scale, lack of accessibility, complexity, ageing and uncertainties in knowledge of their structure. In addition they are subject to loads that can be highly variable and unpredictable and to accidental events such as component failure, leakage and malicious tampering. To support in-field operations and central management of these infrastructures, the availability of consistent and up-to-date knowledge about the current state of the network and how it would respond to planned interventions is argued to be highly desirable. However, at present, large-scale infrastructures are “data rich but knowledge poor”. Data, algorithms and tools for network analysis are improving but there is a need to integrate them to support more directly engineering operations. Current ICT solutions are mainly based on specialized, monolithic and heavyweight software packages that restrict the dissemination of dynamic information and its appropriate and timely presentation particularly to field engineers who operate in a resource constrained and less reliable environments. This thesis proposes a solution to these problems by recognizing that current monolithic ICT solutions for infrastructure management seek to meet the requirements of different human roles and operating environments (defined in this work as field and central sides). It proposes an architectural approach to providing dynamic, predictive, user-centric, device and platform independent access to consistent and up-to-date knowledge. This architecture integrates the components required to implement the functionalities of data gathering, data storage, simulation modelling, and information visualization and analysis. These components are tightly coupled in current implementations of software for analysing the behaviour of networks. The architectural approach, by contrast, requires they be kept as separate as possible and interact only when required using common and standard protocols. The thesis particularly concentrates on engineering practices in clean water distribution networks but the methods are applicable to other structural networks, for example, the electricity Grid. A prototype implementation is provided that establishes a dynamic hydraulic simulation model and enables the model to be queried via remote access in a device and platform independent manner.This thesis provides an extensive evaluation comparing the architecture driven approach with current approaches, to substantiate the above claims. This evaluation is conducted by the use of benchmarks that are currently published and accepted in the water engineering community. To facilitate this evaluation, a working prototype of the whole architecture has been developed and is made available under an open source licence.

Dans le contexte de transition vers un système électrique décentralisé et intelligent, nous abordons le problème de la gestion des flexibilités de consommation électriques. Nous développons différentes méthodes basées sur l'optimisation distribuée et la théorie des jeux.Nous commençons par adopter le point de vue d'un opérateur central en charge de la gestion des flexibilités de plusieurs agents. Nous présentons un algorithme distribué permettant le calcul des profils de consommations des agents optimaux pour l'opérateur.Cet algorithme garantit la confidentialité des agents~: les contraintes individuelles, ainsi que le profil individuel de consommation de chaque agent, ne sont jamais révélés à l'opérateur ni aux autres agents.Ensuite, nous adoptons dans un second modèle une vision plus décentralisée et considérons un cadre de théorie des jeux pour la gestion des flexibilités de consommation.Cette approche nous permet en particulier de modéliser les comportements stratégiques des consommateurs.Dans ce cadre, une classe de jeux adéquate est donnée par les jeux de congestion atomiques fractionnables.Nous obtenons plusieurs résultats théoriques concernant les équilibres de Nash dans cette classe de jeux, et nous quantifions l'efficacité de ces équilibres en établissant des bornes supérieures sur le prix de l'anarchie.Nous traitons la question du calcul décentralisé des équilibres de Nash dans ce contexte en étudiant les conditions et les vitesses de convergence des algorithmes de meilleure réponse et de gradient projeté.En pratique un opérateur peut faire face à un très grand nombre de joueurs, et calculer les équilibres d'un jeu de congestion dans ce cas est difficile.Afin de traiter ce problème, nous établissons des résultats sur l'approximation d'un équilibre dans les jeux de congestion et jeux agrégatifs avec un très grand nombre de joueurs et en présence de contraintes couplantes.Ces résultats, obtenus dans le cadre des inégalités variationnelles et sous certaines hypothèses de monotonie, peuvent être utilisés pour calculer un équilibre approché comme solution d'un problème de petite dimension.Toujours dans la perspective de modéliser un très grand nombre d'agents, nous considérons des jeux de congestion nonatomiques avec contraintes couplantes et avec une infinité de joueurs hétérogènes~: ce type de jeux apparaît lorsque les caractéristiques d'une population sont décrites par une fonction de distribution paramétrique.Sous certaines hypothèses de monotonie, nous prouvons que les équilibres de Wardrop de ces jeux, définis comme solutions d'une inégalité variationnelle de dimension infinie, peuvent être approchés par des équilibres de Wardrop symétriques de jeux annexes, solutions d'inégalités variationnelles de petite dimension.Enfin, nous considérons un modèle de jeu pour l'étude d'échanges d'électricité pair-à-pair au sein d'une communauté de consommateurs possédant des actifs de production électrique renouvelable.Nous étudions les équilibres généralisés du jeu obtenu, qui caractérisent les échanges possibles d'énergie et les consommations individuelles.Nous comparons ces équilibres avec la solution centralisée minimisant le coût social, et nous évaluons l'efficacité des équilibres via la notion de prix de l'anarchie
In the context of smart grid and in the transition to decentralized electric systems, we address the problem of the management of distributed electric consumption flexibilities. We develop different methods based on distributed optimization and game theory approaches.We start by adopting the point of view of a centralized operator in charge of the management of flexibilities for several agents. We provide a distributed and privacy-preserving algorithm to compute consumption profiles for agents that are optimal for the operator.In the proposed method, the individual constraints as well as the individual consumption profile of each agent are never revealed to the operator or the other agents.Then, in a second model, we adopt a more decentralized vision and consider a game theoretic framework for the management of consumption flexibilities.This approach enables, in particular, to take into account the strategic behavior of consumers.Individual objectives are determined by dynamic billing mechanisms, which is motivated by the modeling of congestion effects occurring on time periods receiving a high electricity load from consumers.A relevant class of games in this framework is given by atomic splittable congestion games.We obtain several theoretical results on Nash equilibria for this class of games, and we quantify the efficiency of those equilibria by providing bounds on the price of anarchy.We address the question of the decentralized computation of equilibria in this context by studying the conditions and rates of convergence of the best response and projected gradients algorithms.In practice an operator may deal with a very large number of players, and evaluating the equilibria in a congestion game in this case will be difficult.To address this issue, we give approximation results on the equilibria in congestion and aggregative games with a very large number of players, in the presence of coupling constraints.These results, obtained in the framework of variational inequalities and under some monotonicity conditions, can be used to compute an approximate equilibrium, solution of a small dimension problem.In line with the idea of modeling large populations, we consider nonatomic congestion games with coupling constraints, with an infinity of heterogeneous players: these games arise when the characteristics of a population are described by a parametric density function.Under monotonicity hypotheses, we prove that Wardrop equilibria of such games, given as solutions of an infinite dimensional variational inequality, can be approximated by symmetric Wardrop equilibria of auxiliary games, solutions of low dimension variational inequalities.Again, those results can be the basis of tractable methods to compute an approximate Wardrop equilibrium in a nonatomic infinite-type congestion game.Last, we consider a game model for the study of decentralized peer-to-peer energy exchanges between a community of consumers with renewable production sources.We study the generalized equilibria in this game, which characterize the possible energy trades and associated individual consumptions.We compare the equilibria with the centralized solution minimizing the social cost, and evaluate the efficiency of equilibria through the price of anarchy

Cette thèse porte sur les problèmes de sécurité sur le réseau électrique français exploité par RTE, le Gestionnaire de Réseau de Transport (GRT). Les progrès en matière d'énergie durable, d'efficacité du marché de l'électricité ou de nouveaux modes de consommation poussent les GRT à exploiter le réseau plus près de ses limites de sécurité. Pour ce faire, il est essentiel de rendre le réseau plus "intelligent". Pour s'attaquer à ce problème, ce travail explore les avantages des réseaux neuronaux artificiels. Nous proposons de nouveaux algorithmes et architectures d'apprentissage profond pour aider les opérateurs humains (dispatcheurs) à prendre des décisions que nous appelons " guided dropout ". Ceci permet de prévoir les flux électriques consécutifs à une modification volontaire ou accidentelle du réseau. Pour se faire, les données continues (productions et consommations) sont introduites de manière standard, via une couche d'entrée au réseau neuronal, tandis que les données discrètes (topologies du réseau électrique) sont encodées directement dans l'architecture réseau neuronal. L’architecture est modifiée dynamiquement en fonction de la topologie du réseau électrique en activant ou désactivant des unités cachées. Le principal avantage de cette technique réside dans sa capacité à prédire les flux même pour des topologies de réseau inédites. Le "guided dropout" atteint une précision élevée (jusqu'à 99% de précision pour les prévisions de débit) tout en allant 300 fois plus vite que des simulateurs de grille physiques basés sur les lois de Kirchoff, même pour des topologies jamais vues, sans connaissance détaillée de la structure de la grille. Nous avons également montré que le "guided dropout" peut être utilisé pour classer par ordre de gravité des évènements pouvant survenir. Dans cette application, nous avons démontré que notre algorithme permet d'obtenir le même risque que les politiques actuellement mises en œuvre tout en n'exigeant que 2 % du budget informatique. Le classement reste pertinent, même pour des cas de réseau jamais vus auparavant, et peut être utilisé pour avoir une estimation globale de la sécurité globale du réseau électrique
This thesis addresses problems of security in the French grid operated by RTE, the French ``Transmission System Operator'' (TSO). Progress in sustainable energy, electricity market efficiency, or novel consumption patterns push TSO's to operate the grid closer to its security limits. To this end, it is essential to make the grid ``smarter''. To tackle this issue, this work explores the benefits of artificial neural networks. We propose novel deep learning algorithms and architectures to assist the decisions of human operators (TSO dispatchers) that we called “guided dropout”. This allows the predictions on power flows following of a grid willful or accidental modification. This is tackled by separating the different inputs: continuous data (productions and consumptions) are introduced in a standard way, via a neural network input layer while discrete data (grid topologies) are encoded directly in the neural network architecture. This architecture is dynamically modified based on the power grid topology by switching on or off the activation of hidden units. The main advantage of this technique lies in its ability to predict the flows even for previously unseen grid topologies. The "guided dropout" achieves a high accuracy (up to 99% of precision for flow predictions) with a 300 times speedup compared to physical grid simulators based on Kirchoff's laws even for unseen contingencies, without detailed knowledge of the grid structure. We also showed that guided dropout can be used to rank contingencies that might occur in the order of severity. In this application, we demonstrated that our algorithm obtains the same risk as currently implemented policies while requiring only 2% of today's computational budget. The ranking remains relevant even handling grid cases never seen before, and can be used to have an overall estimation of the global security of the power grid

This thesis presents the study of the model order reduction for power grids and transmission networks. The specific focus has been the transient dynamics. A mathematical viewpoint has been adopted for model reduction. Power networks are huge and complex network, simulation for power grid analysis and design require large non-linear models to be solved. In the context of developing "Smart Grids" with the distributed generation of power, real time analysis of complex systems such as these needs fast, reliable and accurate models. In the current study we propose model order reduction methods both a-priori and a-posteriori suitable for dynamic models of power grids. The model that describes the transient dynamics of the power grids is complex non-linear swing dynamics model. The non-linearity of the swing dynamics model necessitates special attention to achieve maximum benefit from the model order reduction techniques. In the current research, POD and LATIN methods were applied initially with varying degrees of success. The method of TPWL has been proved as the best-suited model reduction method for swing dynamics model; this method combines POD with multiple linear approximations. For the transmission lines, a distributed parameters model in frequency-domain is used. PGD based reduced-order models are proposed for the DP model of transmission lines. A fully parametric problem with electrical parameters of transmission lines included as coordinates of the separated representation. The method was extended to present the solution of frequency-dependent parameters model for transmission lines.
Cette these présente l'étude de la réduction de modeles pour les réseaux électriques et les réseaux de transmission. Un point de vue mathématique a été adopté pour la réduction de modeles. Les réseaux électriques sont des réseaux immenses et complexes, dont l'analyse et la conception nécessite la simulation et la résolution de grands modeles non-linéaires. Dans le cadre du développement de réseaux électriques intelligents (smart grids) avec une génération distribuée de puissance, l'analyse en temps réel de systemes complexes tels que ceux-ci nécessite des modeles rapides, fiables et précis. Dans la présente étude, nous proposons des méthodes de réduction de de modeles a la fois a priori et a posteriori, adaptées aux modeles dynamiques des réseaux électriques. Un accent particulier a été mis sur la dynamique transitoire des réseaux électriques, décrite par un modele oscillant non­linéaire et complexe. La non-linéarité de ce modele nécessite une attention particuliere pour bénéficier du maximum d'avantages des techniques de réduction de modeles. lnitialement, des méthodes comme POD et LATIN ont été adoptées avec des degrés de succes divers. La méthode de TPWL, qui combine la POD avec des approximations linéaires multiples, a été prouvée comme étant la méthode de réduction de modeles la mieux adaptée pour le modele dynamique oscillant. Pour les lignes de transmission, un modele de parametres distribués en domaine fréquentiel est utilisé. Des modeles réduits de type PGD sont proposés pour le modele DP des lignes de transmission. Un probleme multidimensionnel entierement paramétrique a été formulé, avec les parametres électriques des lignes de transmission inclus comme coordonnées additionnelles de la représentation séparée. La méthode a été étendue pour étudier la solution du modele des lignes de transmission pour laquelle les parametres dépendent de la fréquence.
Esta tesis presenta un estudio de la reducción de modelos (MOR) para redes de transmisión y distribución de electricidad. El enfoque principal utilizado ha sido la dinámica transitoria y para la reducción de modelos se ha adoptado un punto de vista matemático. Las redes eléctricas son complejas y tienen un tamaño importante. Por lo tanto, el análisis y diseño de este tipo de redes mediante la simulación numérica, requiere la resolución de modelos no-lineales complejos. En el contexto del desarrollo de redes inteligentes, el objetivo es un análisis en tiempo real de sistemas complejos, por lo que son necesarios modelos rápidos, fiables y precisos. En el presente estudio se proponen diferentes métodos de reducción de modelos, tanto a priori como a posteriori, adecuados para modelos dinámicos de redes eléctricas. La dinámica transitoria de redes eléctricas, se describe mediante modelos dinámicos oscilatorios no-lineales. Esta no-linearidad del modelo necesita ser bien tratada para obtener el máximo beneficio de las técnicas de reducción de modelos. Métodos como la POD y la LATIN han sido inicialmente utilizados en esta problemática con diferentes grados de éxito. El método de TPWL, que combina la POD con múltiples aproximaciones lineales, ha resultado ser el mas adecuado para sistemas dinámicos oscilatorios. En el caso de las redes de transmisión eléctrica, se utiliza un modelo de parámetros distribuidos en el dominio de la frecuencia. Se propone reducir este modelo basándose en la PGD, donde los parámetros eléctricos de la red de transmisión son incluidos como coordenadas de la representación separada del modelo paramétrico. Este método es ampliado para representar la solución de modelos con parámetros dependientes de la frecuencia para las redes de transmisión eléctrica

As ever more challenging designs are required in present-day industries, the traditional trial-and-error procedure frequently used for designing mechanical parts slows down the design process and yields suboptimal designs, so that new approaches are needed to obtain a competitive advantage. With the ascent of the Finite Element Method (FEM) in the engineering community in the 1970s, structural shape optimization arose as a promising area of application. However, due to the iterative nature of shape optimization processes, the handling of large quantities of numerical models along with the approximated character of numerical methods may even dissuade the use of these techniques (or fail to exploit their full potential) because the development time of new products is becoming ever shorter. This Thesis is concerned with the formulation of a 3D methodology based on the Cartesian-grid Finite Element Method (cgFEM) as a tool for efficient and robust numerical analysis. This methodology belongs to the category of embedded (or fictitious) domain discretization techniques in which the key concept is to extend the structural analysis problem to an easy-to-mesh approximation domain that encloses the physical domain boundary. The use of Cartesian grids provides a natural platform for structural shape optimization because the numerical domain is separated from a physical model, which can easily be changed during the optimization procedure without altering the background discretization. Another advantage is the fact that mesh generation becomes a trivial task since the discretization of the numerical domain and its manipulation, in combination with an efficient hierarchical data structure, can be exploited to save computational effort. However, these advantages are challenged by several numerical issues. Basically, the computational effort has moved from the use of expensive meshing algorithms towards the use of, for example, elaborate numerical integration schemes designed to capture the mismatch between the geometrical domain boundary and the embedding finite element mesh. To do this we used a stabilized formulation to impose boundary conditions and developed novel techniques to be able to capture the exact boundary representation of the models. To complete the implementation of a structural shape optimization method an adjunct formulation is used for the differentiation of the design sensitivities required for gradient-based algorithms. The derivatives are not only the variables required for the process, but also compose a powerful tool for projecting information between different designs, or even projecting the information to create h-adapted meshes without going through a full h-adaptive refinement process. The proposed improvements are reflected in the numerical examples included in this Thesis. These analyses clearly show the improved behavior of the cgFEM technology as regards numerical accuracy and computational efficiency, and consequently the suitability of the cgFEM approach for shape optimization or contact problems.
La competitividad en la industria actual impone la necesidad de generar nuevos y mejores diseños. El tradicional procedimiento de prueba y error, usado a menudo para el diseño de componentes mecánicos, ralentiza el proceso de diseño y produce diseños subóptimos, por lo que se necesitan nuevos enfoques para obtener una ventaja competitiva. Con el desarrollo del Método de los Elementos Finitos (MEF) en el campo de la ingeniería en la década de 1970, la optimización de forma estructural surgió como un área de aplicación prometedora. El entorno industrial cada vez más exigente implica ciclos cada vez más cortos de desarrollo de nuevos productos. Por tanto, la naturaleza iterativa de los procesos de optimización de forma, que supone el análisis de gran cantidad de geometrías (para las se han de usar modelos numéricos de gran tamaño a fin de limitar el efecto de los errores intrínsecamente asociados a las técnicas numéricas), puede incluso disuadir del uso de estas técnicas. Esta Tesis se centra en la formulación de una metodología 3D basada en el Cartesian-grid Finite Element Method (cgFEM) como herramienta para un análisis numérico eficiente y robusto. Esta metodología pertenece a la categoría de técnicas de discretización Immersed Boundary donde el concepto clave es extender el problema de análisis estructural a un dominio de aproximación, que contiene la frontera del dominio físico, cuya discretización (mallado) resulte sencilla. El uso de mallados cartesianos proporciona una plataforma natural para la optimización de forma estructural porque el dominio numérico está separado del modelo físico, que podrá cambiar libremente durante el procedimiento de optimización sin alterar la discretización subyacente. Otro argumento positivo reside en el hecho de que la generación de malla se convierte en una tarea trivial. La discretización del dominio numérico y su manipulación, en coalición con la eficiencia de una estructura jerárquica de datos, pueden ser explotados para ahorrar coste computacional. Sin embargo, estas ventajas pueden ser cuestionadas por varios problemas numéricos. Básicamente, el esfuerzo computacional se ha desplazado. Del uso de costosos algoritmos de mallado nos movemos hacia el uso de, por ejemplo, esquemas de integración numérica elaborados para poder capturar la discrepancia entre la frontera del dominio geométrico y la malla de elementos finitos que lo embebe. Para ello, utilizamos, por un lado, una formulación de estabilización para imponer condiciones de contorno y, por otro lado, hemos desarrollado nuevas técnicas para poder captar la representación exacta de los modelos geométricos. Para completar la implementación de un método de optimización de forma estructural se usa una formulación adjunta para derivar las sensibilidades de diseño requeridas por los algoritmos basados en gradiente. Las derivadas no son sólo variables requeridas para el proceso, sino una poderosa herramienta para poder proyectar información entre diferentes diseños o, incluso, proyectar la información para crear mallas h-adaptadas sin pasar por un proceso completo de refinamiento h-adaptativo. Las mejoras propuestas se reflejan en los ejemplos numéricos presentados en esta Tesis. Estos análisis muestran claramente el comportamiento superior de la tecnología cgFEM en cuanto a precisión numérica y eficiencia computacional. En consecuencia, el enfoque cgFEM se postula como una herramienta adecuada para la optimización de forma.
Actualment, amb la competència existent en la industria, s'imposa la necessitat de generar nous i millors dissenys . El tradicional procediment de prova i error, que amb freqüència es fa servir pel disseny de components mecànics, endarrereix el procés de disseny i produeix dissenys subòptims, pel que es necessiten nous enfocaments per obtindre avantatge competitiu. Amb el desenvolupament del Mètode dels Elements Finits (MEF) en el camp de l'enginyeria en la dècada de 1970, l'optimització de forma estructural va sorgir com un àrea d'aplicació prometedora. No obstant això, a causa de la natura iterativa dels processos d'optimització de forma, la manipulació dels models numèrics en grans quantitats, junt amb l'error de discretització dels mètodes numèrics, pot fins i tot dissuadir de l'ús d'aquestes tècniques (o d'explotar tot el seu potencial), perquè al mateix temps els cicles de desenvolupament de nous productes s'estan acurtant. Esta Tesi se centra en la formulació d'una metodologia 3D basada en el Cartesian-grid Finite Element Method (cgFEM) com a ferramenta per una anàlisi numèrica eficient i sòlida. Esta metodologia pertany a la categoria de tècniques de discretització Immersed Boundary on el concepte clau és expandir el problema d'anàlisi estructural a un domini d'aproximació fàcil de mallar que conté la frontera del domini físic. L'utilització de mallats cartesians proporciona una plataforma natural per l'optimització de forma estructural perquè el domini numèric està separat del model físic, que podria canviar lliurement durant el procediment d'optimització sense alterar la discretització subjacent. A més, un altre argument positiu el trobem en què la generació de malla es converteix en una tasca trivial, ja que la discretització del domini numèric i la seua manipulació, en coalició amb l'eficiència d'una estructura jeràrquica de dades, poden ser explotats per estalviar cost computacional. Tot i això, estos avantatges poden ser qüestionats per diversos problemes numèrics. Bàsicament, l'esforç computacional s'ha desplaçat. De l'ús de costosos algoritmes de mallat ens movem cap a l'ús de, per exemple, esquemes d'integració numèrica elaborats per poder capturar la discrepància entre la frontera del domini geomètric i la malla d'elements finits que ho embeu. Per això, fem ús, d'una banda, d'una formulació d'estabilització per imposar condicions de contorn i, d'un altra, desevolupem noves tècniques per poder captar la representació exacta dels models geomètrics Per completar la implementació d'un mètode d'optimització de forma estructural es fa ús d'una formulació adjunta per derivar les sensibilitats de disseny requerides pels algoritmes basats en gradient. Les derivades no són únicament variables requerides pel procés, sinó una poderosa ferramenta per poder projectar informació entre diferents dissenys o, fins i tot, projectar la informació per crear malles h-adaptades sense passar per un procés complet de refinament h-adaptatiu. Les millores proposades s'evidencien en els exemples numèrics presentats en esta Tesi. Estes anàlisis mostren clarament el comportament superior de la tecnologia cgFEM en tant a precisió numèrica i eficiència computacional. Així, l'enfocament cgFEM es postula com una ferramenta adient per l'optimització de forma.
Marco Alacid, O. (2017). Structural Shape Optimization Based On The Use Of Cartesian Grids [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/86195
TESIS

Thesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, Engineering Systems Division, 2014.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 104-110).
As power systems adapt to include aging infrastructure, new socio-political priorities, and renewable electricity resources, grid operators look to a more flexible grid. Electricity storage flexibility is one strategy gaining interest. Clean energy advocates see benefits in terms of greater renewables integration and lower emissions; grid operators see storage as an improved security system in the face of supply and demand variability and uncertainty. However, as power systems are designed for reliable and efficient operations using available technologies, newer, better-performing technologies such as energy storage devices may not always win the market. Several market barriers to storage remain, including high storage capital costs and a lack of trusted tools for modeling and estimating the lifetime value of new capacity investments [1]. Most storage modeling strategies omit constraints that describe the technical operating boundaries of different power generating technologies, which can lead an overestimation of total operating costs for the power system [2]. I describe a mixed integer linear optimization framework for estimating the optimal control and value of energy storage in a virtual power generation system with economic, regulatory, and technical performance characteristics. The model consists of power plant commitment, dispatch, and selective capacity expansion constraints that simulate optimal investments and operations of the power generation system. A new formulation for modeling energy storage is also developed in order to improve the accuracy of round-trip efficiencies and allow for the inclusion of minimum storage output constraints. Using this model, I solve for break-even target prices for storage capital costs under a range of scenarios (storage futures scenarios). A second challenge slowing the adoption of storage is a lack of spending on performance improvements and cost-reductions. A two-factor learning curve and optimization approach is developed to solve for the optimal portfolio of research, development, demonstration, and diffusion investments (RDD&D) over multiple investment periods. Using the target capital costs from unit commitment model output as the investment model input value, innovating firms and policy planners may better identify cost targets and investment strategies for reaching target levels of storage deployment. Electricity storage becomes more valuable as net load variability increases. The impact of net load variability is tested by changing the level of renewable generation resources in the system. The current capital cost of storage-here, compressed air energy storage (CAES)-generally exceeds the target cost needed to make CAES economical when it is used to provide load following, load shifting, and operating reserve services in high-voltage power generation systems. Scenario analysis shows that when renewables generation reaches 35%, CAES becomes economical in limited quantities due to the added value from providing renewables integration and greater operating reserves. Using this framework, I identify different levels of cost reductions needed to drive improved adoption and make several RDD&D recommendations.
by Jacob Whitcomb.
S.M. in Engineering and Management

La résolution efficace de problèmes d'optimisation a permutation de grande taille nécessite le développement de méthodes hybrides complexes combinant différentes classes d'algorithmes d'optimisation. L'hybridation des metaheuristiques avec les méthodes exactes arborescentes, tel que l'algorithme du branch-and-bound (B&B), engendre une nouvelle classe d'algorithmes plus efficace que ces deux classes de méthodes utilisées séparément. Le défi principal dans le développement de telles méthodes consiste a trouver des liens ou connections entre les stratégies de recherches divergentes utilisées dans les deux classes de méthodes. Les Algorithmes Genetiques (AGs) sont des metaheuristiques, a base de population, tr'es populaires bas'es sur des op'erateurs stochastiques inspirés de la théorie de l'évolution. Contrairement aux AGs et aux m'etaheuristiques généralement, les algorithmes de B&B sont basées sur l'énumération implicite de l'espace de recherche représente par le moyen d'un arbre, dit arbre de recherche. Notre approche d'hybridation consiste a définir un codage commun des solutions et de l'espace de recherche ainsi que des opérateurs de recherche ad'equats afin de permettre un couplage efficace de bas niveau entre les deux classes de méthodes AGs et B&B. La représentation de l'espace de recherche par le moyen d'arbres est traditionnellement utilis'ee dans les algorithmes de B&B. Dans cette thèse, cette représentation a été adaptée aux metaheuristiques. L'encodage des permutations au moyen de nombres naturels faisant référence a l'ordre d'énumération lexicographique des permutations dans l'arbre du B&B, est proposé comme une nouvelle manière de représenter l'espace de recherche des problèmes 'a permutations dans les metaheuristiques. Cette méthode de codage est basée sur les propriétés mathématiques des permutations, 'a savoir les codes de Lehmer et les tables d'inversions ainsi que les système d'énumération factoriels. Des fonctions de transformation permettant le passage entre les deux représentations (permutations et nombres) ainsi que des opérateurs de recherche adaptes au codage, sont définis pour les problèmes 'a permutations généralisés. Cette représentation, désormais commune aux metaheuristiques et aux algorithmes de B&B, nous a permis de concevoir des stratégies d'hybridation et de collaboration efficaces entre les AGs et le B&B. En effet, deux approches d'hybridation entre les AGs et les algorithmes de B&B (HGABB et COBBIGA) bas'es sur cette représentation commune ont été proposées dans cette thèse. Pour validation, une implémentation a été réalisée pour le problème d'affectation quadratique 'a trois dimension (Q3AP). Afin de résoudre de larges instances de ce problème, nous avons aussi propose une parallélisation pour les deux algorithme hybrides, basée sur des techniques de décomposition d'espace (décomposition par intervalle) utilisées auparavant pour la parallélisation des algorithmes de B&B. Du point de vue implémentation, afin de faciliter de futurs conceptions et implémentations de méthodes hybrides combinant metaheuristiques et méthodes exacte arborescentes, nous avons développe une plateforme d'hybridation intégrée au logiciel pour metaheuristiques, ParadisEO. La nouvelle plateforme a été utilisée pour réaliser des expérimentations intensives sur la grille de calcul Grid'5000.

Projects relying on renewable energies are a suitable and sustainable option to electrify isolated communities autonomously. These systems produce electricity in a clean and environmentally respectful way and their cost is often lower than national grid extension. Hybrid systems that combine different energy resources (wind and solar) and distribution through microgrids are the most efficient design configurations. When considering hybrid systems and microgrids, the design of rural electrification projects is referred to as the AVEREMS problem. The optimization of the AVEREMS problem is a complex task that requires the use of specific support tools. In this context, some shortcomings have been encountered in the current state-of-the-art in the design of off-grid electrification projects based on renewable energies, in specific: the lack of knowledge about detailed wind resource studies for this kind of projects and the need of procedures for solving the AVEREMS problem considering generation also far from the demand in order to take advantage of best resource areas. The main objective of this thesis is to tackle these limitations by means of: 1) defining a method for detailed wind resource assessment in rural electrification projects, 2) the development and 3) application of procedures to solve the AVEREMS problem considering micro-scale resource variations and generation in every point of a community (being a demand or a no-demand point). Firstly, a method for detailed wind resource assessment is presented relying on the use of micro-scale wind flow models: the method is validated in two mountainous communities and applied for the design of a real project in Cape Verde. Then, different solving procedures are developed: first some indicators are proposed to support algorithms¿ design, and then two procedures (a deterministic heuristic and a metaheuristic algorithm) are presented in order to solve the AVEREMS problem. Different algorithm versions are analyzed in order to select the ones that give best results. The proposed algorithms, besides considering generation in every point of a certain area (being a demand or a no-demand point), enhance the performance of the currently available tools. Finally, the design of a real electrification project in Nicaragua is carried out including a micro-scale wind resource assessment and the application of the developed metaheuristic procedure for design optimization. The wind resource assessment method and the solving procedures developed in this Thesis can be easily applied to support the design of off-grid rural electrification projects with renewable energies. Their utilization will improve projects efficiency and sustainability reducing some of the technical issues that still limit their implementation in isolated communities.
Los proyectos de electrificación basados en energías renovables han demostrado ser una opción adecuada y sostenible para abastecer comunidades aisladas de forma autónoma. Estos sistemas producen energía de manera limpia y respetuosa del medio ambiente y su coste es a menudo inferior al de extender la red eléctrica nacional. Las configuraciones de diseño más fiables y eficientes utilizan sistemas híbridos que combinan varios recursos (eólico y solar) y distribución mediante microrredes. El diseño de proyectos de electrificación rural considerando sistemas híbridos y microrredes se ha definido como el problema AVEREMS. La optimización del problema AVEREMS es una tarea compleja que requiere el uso de herramientas de soporte. Actualmente, el proceso de diseño de proyectos de electrificación basados en energía renovables presenta algunas limitaciones. Entre ellas, destacan la falta de conocimientos sobre estudios del recurso eólico y la necesidad de procedimientos para resolver el problema AVEREMS incluyendo la generación alejada de los puntos de consumo para aprovechar las áreas de mayor potencial. El principal objetivo de esta tesis es abordar dichas limitaciones, mediante: 1) la definición de un método para evaluar en detalle el recurso eólico en proyectos de electrificación rural; 2) el desarrollo y 3) la implementación de procedimientos para resolver el problema AVEREMS considerando la variación del recurso a micro-escala y generación en todos los puntos (sean estos de consumo o de no-consumo) de una determinada área. Primero se presenta un método para realizar estudios del recurso eólico mediante el uso de modelos de flujo de viento a micro-escala. El método se valida en dos comunidades montañosas y se aplica para el diseño de proyectos reales en Cabo Verde. Sucesivamente, se desarrollan diferentes procedimientos resolutivos: primero se definen unos indicadores de soporte al diseño, y sucesivamente se presentan dos algoritmos (uno heurístico y otro meta-heurístico) para resolver el problema AVEREMS. Se analizan diferentes versiones de los algoritmos para finalmente seleccionar las que obtienen los mejores resultados. Además de considerar generación en todos los puntos (de consumo o de no-consumo) de una cierta área, los algoritmos propuestos mejoran considerablemente las prestaciones de los métodos disponibles actualmente. Finalmente, se analiza el diseño de un proyecto de electrificación en una comunidad rural en Nicaragua incluyendo la evaluación de recurso a micro-escala y la aplicación del algoritmo meta-heurístico para la optimización del diseño. La metodología para la evaluación del recurso eólico y los algoritmos resolutivos desarrollados en esta tesis se pueden fácilmente aplicar para suportar el diseño de proyectos de electrificación rural con energías renovables. Su utilización permitirá mejorar la eficiencia y sostenibilidad de estos proyectos reduciendo algunos de los problemas técnicos que limitan su implementación en comunidades aisladas

This research proposes, implements and analyses a novel framework for multiobjective optimisation through evolutionary computing aimed at, but not restricted to, real-world problems in the engineering design domain. Evolutionary algorithms have been used to tackle a variety of non-linear multiobjective optimisation problems successfully, but their success is governed by key parameters which have been shown to be sensitive to the nature of the particular problem, incorporating concerns such as the number of objectives and variables, and the size and topology of the search space, making it hard to determine the best settings in advance. This work describes a real-encoded multi-objective optimising evolutionary algorithm framework, incorporating a genetic algorithm, that uses self-adaptive mutation and crossover in an attempt to avoid such problems, and which has been benchmarked against both standard optimisation test problems in the literature and a real-world airfoil optimisation case. For this last case, the minimisation of drag and maximisation of lift coefficients of a well documented standard airfoil, the framework is integrated with a freeform deformation tool to manage the changes to the section geometry, and XFoil, a tool which evaluates the airfoil in terms of its aerodynamic efficiency. The performance of the framework on this problem is compared with those of two other heuristic MOO algorithms known to perform well, the Multi-Objective Tabu Search (MOTS) and NSGA-II, showing that this framework achieves better or at least no worse convergence. The framework of this research is then considered as a candidate for smart (electricity) grid optimisation. Power networks can be improved in both technical and economical terms by the inclusion of distributed generation which may include renewable energy sources. The essential problem in national power networks is that of power flow and in particular, optimal power flow calculations of alternating (or possibly, direct) current. The aims of this work are to propose and investigate a method to assist in the determination of the composition of optimal or high-performing power networks in terms of the type, number and location of the distributed generators, and to analyse the multi-dimensional results of the evolutionary computation component in order to reveal relationships between the network design vector elements and to identify possible further methods of improving models in future work. The results indicate that the method used is a feasible one for the achievement of these goals, and also for determining optimal flow capacities of transmission lines connecting the bus bars in the network.

Thesis (Master of Engineering in Electrical Engineering)--Cape Peninsula University of Technology, 2018.
A smart grid is an intelligent power delivery system integrating traditional and advanced control, monitoring, and protection systems for enhanced reliability, improved efficiency, and quality of supply. To achieve a smart grid, technical challenges such as voltage instability; power loss; and unscheduled power interruptions should be mitigated. Therefore, future smart grids will require intelligent solutions at transmission and distribution levels, and optimal placement & sizing of grid components for optimal steady state and dynamic operation of the power systems. At distribution levels, feeder reconfiguration and Distributed Generation (DG) can be used to improve the distribution network performance. Feeder reconfiguration consists of readjusting the topology of the primary distribution network by remote control of the tie and sectionalizing switches under normal and abnormal conditions. Its main applications include service restoration after a power outage, load balancing by relieving overloads from some feeders to adjacent feeders, and power loss minimisation for better efficiency. On the other hand, the DG placement problem entails finding the optimal location and size of the DG for integration in a distribution network to boost the network performance. This research aims to develop Particle Swarm Optimization (PSO) algorithms to solve the distribution network feeder reconfiguration and DG placement & sizing problems. Initially, the feeder reconfiguration problem is treated as a single-objective optimisation problem (real power loss minimisation) and then converted into a multi-objective optimisation problem (real power loss minimisation and load balancing). Similarly, the DG placement problem is treated as a single-objective problem (real power loss minimisation) and then converted into a multi-objective optimisation problem (real power loss minimisation, voltage deviation minimisation, Voltage stability Index maximisation). The developed PSO algorithms are implemented and tested for the 16-bus, the 33-bus, and the 69-bus IEEE distribution systems. Additionally, a parallel computing method is developed to study the operation of a distribution network with a feeder reconfiguration scheme under dynamic loading conditions.

Attempts to reduce greenhouse gas emissions are promising with the recent dramatic increase of installed renewable energy sources (RES) capacity. Integration of large intermittent renewable resources affects smart grid systems in several significant ways, such as transient and voltage stability, existing protection scheme, and power leveling and energy balancing. To protect the grid from threats related to these issues, utilities impose rigorous technical requirements, more importantly, focusing on fault ride through requirements and active/reactive power responses following disturbances. This dissertation is aimed at developing and verifying the advanced and algorithmic methods for specification of protection schemes, reactive power capability and power control requirements for interconnection of the RESs to the smart grid systems. The first findings of this dissertation verified that the integration of large RESs become more promising from the energy-saving, and downsizing perspective by introducing a resistive superconducting fault current limiter (SFCL) as a self-healing equipment. The proposed SFCL decreased the activation of the conventional control scheme for the wind power plant (WPP), such as dc braking chopper and fast pitch angle control systems, thereby increased the reliability of the system. A static synchronous compensator (STATCOM) has been proposed to assist with the uninterrupted operation of the doubly-fed induction generators (DFIGs)-based WTs during grid disturbances. The key motivation of this study was to design a new computational intelligence technique based on a multi-objective optimization problem (MOP), for the online coordinated reactive power control between the DFIG and the STATCOM in order to improve the low voltage ride-through (LVRT) capability of the WT during the fault, and to smooth low-frequency oscillations of the active power during the recovery. Furthermore, the application of a three-phase single-stage module-integrated converter (MIC) incorporated into a grid-tied photovoltaic (PV) system was investigated in this dissertation. A new current control scheme based on multivariable PI controller, with a faster dynamic and superior axis decoupling capability compared with the conventional PI control method, was developed and experimentally evaluated for three-phase PV MIC system. Finally, a study was conducted based on the framework of stochastic game theory to enable a power system to dynamically survive concurrent severe multi-failure events, before such failures turn into a full blown cascading failure. This effort provides reliable strategies in the form of insightful guidelines on how to deploy limited budgets for protecting critical components of the smart grid systems.

Au sein de la communauté scientifique, l’étude des réseaux d’énergie suscite un vif intérêt puisque ces infrastructures deviennent de plus en plus importantes dans notre monde moderne. Des outils mathématiques avancés et complexes sont nécessaires afin de bien concevoir et mettre en œuvre ces réseaux. La précision et l’optimalité sont deux caractéristiques essentielles pour leur conception. Bien que ces deux aspects soient au cœur des méthodes formelles, leur application effective reste largement inexplorée aux réseaux d’énergie. Cela motive fortement le travail développé dans cette thèse. Un accent particulier est placé sur le problème général de planification de la consommation d'énergie. Il s'agit d'un scénario dans lequel les consommateurs ont besoin d’une certaine quantité d’énergie et souhaitent que cette demande soit satisfaite dans une période spécifique (e.g., un Véhicule Électrique (VE) doit être rechargé dans une fenêtre de temps définie par son propriétaire). Par conséquent, chaque consommateur doit choisir une puissance de consommation à chaque instant (par un système informatisé), afin que l'énergie finale accumulée atteigne un niveau souhaité. La manière dont les puissances sont choisies est obtenue par l’application d’une « stratégie » qui prend en compte à chaque instant les informations pertinentes d'un consommateur afin de choisir un niveau de consommation approprié (e.g., l’énergie accumulée pour recharge le VE). Les stratégies peuvent être conçues selon une approche centralisée (dans laquelle il n'y a qu'un seul décideur qui contrôle toutes les stratégies des consommateurs) ou décentralisée (dans laquelle il y a plusieurs contrôleurs, chacun représentant un consommateur). Nous analysons ces deux scénarios dans cette thèse en utilisant des méthodes formelles, la théorie des jeux et l’optimisation. Plus précisément, nous modélisons le problème de planification de la consommation d'énergie à l'aide des processus de décision de Markov et des jeux stochastiques. Par exemple, l’environnement du système électrique, à savoir : la partie non contrôlable de la consommation totale (e.g., la consommation hors VEs), peut être représentée par un modèle stochastique. La partie contrôlable de la consommation totale peut s’adapter aux contraintes du réseau de distribution (e.g., pour ne pas dépasser la température maximale d'arrêt du transformateur électrique) et à leurs objectifs (e.g., tous les VEs soient rechargés). Cela peut être vu comme un système stochastique avec des multi-objectifs sous contraintes. Par conséquent, cette thèse concerne également une contribution aux modèles avec des objectives multicritères, ce qui permet de poursuivre plusieurs objectifs à la fois et une conception des stratégies qui sont fonctionnellement correctes et robustes aux changements de l'environnement
Within the research community, there is a great interest in exploring many applications of energy grids since these become more and more important in our modern world. To properly design and implement these networks, advanced and complex mathematical tools are necessary. Two key features for their design are correctness and optimality. While these last two properties are in the core of formal methods, their effective application to energy networks remains largely unexploited. This constitutes one strong motivation for the work developed in this thesis. A special emphasis is made on the generic problem of scheduling power consumption. This is a scenario in which the consumers have a certain energy demand and want to have this demand fulfilled before a set deadline (e.g., an Electric Vehicle (EV) has to be recharged within a given time window set by the EV owner). Therefore, each consumer has to choose at each time the consumption power (by a computerized system) so that the final accumulated energy reaches a desired level. The way in which the power levels are chosen is according to a ``strategy’’ mapping at any time the relevant information of a consumer (e.g., the current accumulated energy for EV-charging) to a suitable power consumption level. The design of such strategies may be either centralized (in which there is a single decision-maker controlling all strategies of consumers), or decentralized (in which there are several decision-makers, each of them representing a consumer). We analyze both scenarios by exploiting ideas originating from formal methods, game theory and optimization. More specifically, the power consumption scheduling problem can be modelled using Markov decision processes and stochastic games. For instance, probabilities provide a way to model the environment of the electrical system, namely: the noncontrollable part of the total consumption (e.g., the non-EV consumption). The controllable consumption can be adapted to the constraints of the distribution network (e.g., to the maximum shutdown temperature of the electrical transformer), and to their objectives (e.g., all EVs are recharged). At first glance, this can be seen as a stochastic system with multi-constraints objectives. Therefore, the contributions of this thesis also concern the area of multi-criteria objective models, which allows one to pursue several objectives at a time such as having strategy designs functionally correct and robust against changes of the environment

Dans le cadre du dimensionnement des systèmes de conversion statique d’énergie implantés dans des réseaux électriques, il est important de respecter certaines normes harmoniques. Pour caractériser celles-ci, on doit utiliser des approches des modélisations fréquentielles : soit numérique, soit semi-analytique. La deuxième est préférable pour à une procédure de dimensionnement par optimisation de système, qui requiert un modèle rapide et peut accepter beaucoup de contraintes. Cependant, la difficulté principale apparaît lors de la modélisation analytique des structures d’électronique de puissance, notamment celles ayant de la commutation naturelle.Cette thèse propose une approche de modélisation semi-analytique où la résolution des équations implicites est faite par Newton-Raphson ou SQP. Cependant, cette approche pose des difficultés de convergence de la méthode utilisée du point de vue numérique et du point de vue du mode de fonctionnement du convertisseur statique. Ainsi, cette thèse propose différentes alternatives pour les résoudre.En terme d’illustrations, cette thèse s’appuie particulièrement sur des applications avec des redresseurs à diodes qui sont largement utilisés dans les sous-stations de réseaux ferroviaires ou dans les réseaux d’avion,Notamment, un canal de puissance typique d’un Airbus de « nouvelle génération » sert d’application du point de vue de dimensionnement par optimisation
For the design of static energy conversion systems from power grids, harmonic standards have to berespected. To characterize them, frequency modeling approaches, either numerical or semi-analytical. Thesecond one is more advisable for the sizing by optimization of systems, where a fast model is required and manyconstraints have to be carried out. However, the main difficulties happen in the analytical modeling of powerelectronics structures, especially those with soft commutations.This thesis proposes a semi-analytical modeling approach where the solving of implicit equations iscarried out by Newton-Raphson or SQP. The convergence difficulties are analyzed in two viewpoints:numerically and according the operating mode of the static converters. Then, alternatives are proposed to solvethem.To illustrate the problematic, applications with diode rectifiers (widely used in railway electrical gridsubstations or airline electrical grid) are used. Especially, the sizing of power channel of « new generation» of anAirbus, is made by optimization, using the proposed modelling approach

Recently, significant changes in electric power systems such as rapid developmentof smart grid and electricity market and integration of non-dispatchablesources have added more complexity to the Power Flow Scheduling (PFS) andPower Balancing (PB) models. For instance, non-dispatchable sources introducean increasing level of uncertainty in the electricity market and power system operation.One of the solutions for handling these uncertainties in the power systemoperation is the improvement of system flexibility through a more efficient operationof power systems. On the other hand, efficient operation can be achieved bywell capturing variable behavior of uncertain sources such as wind power sourceswhich in turn demands efficient and robust PFS/PB models. This way, a moreflexible system, capable of efficiently accommodating higher levels of wind powerchanges, can be achieved. All these factors increase a need for PFS/PB models suchas Power Flow (PF) and Optimal Power Flow (OPF) models which can addressthese new challenges in an efficient, reliable, and economic way while supportingthe power system operation and control. In this regard, various solution methodshave been developed for solving different forms of PF/OPF formulation. The difficultyof solving OPF problems increases significantly with increasing network sizeand complexity. One of these complexities is how to model advanced controllable devices such as HVDC grids and Flexible AC Transmission Systems (FACTS) devices.Accurate handling of these complexities has limited the use of OPF in manyreal-world applications mainly because of its associated computational challenges.The main reasons behind computational challenges are nonlinearity and especiallynon-convexity of constraints representing power system and its components. Inthis regard, OPF problems are classified into two main groups. In the first group,researchers adopt Nonlinear programming (NLP) approach to fully represent thenonlinearity of the power system for the sake of accuracy but with the cost of complexityin the model. Computational and theoretical challenges associated withNLP approaches are then used as a motivation towards developing a more simplifiedOPF model, leading to the second group of OPF models known as LinearProgramming (LP) based OPF models. LP approaches are fast, reliable, and especiallyconvex, and therefore guarantee a global optimum to the simplified OPFproblem. The problem of LP approach to OPF is that the LP solution of OPF may not even be a feasible solution of original nonlinear OPF at all. Another issueassociated with LP models is that complex power system devices such as HVDClinks are difficult to be incorporated. These limitations have restricted the applicationof LP approaches for many OPF problems. According to the mentionedadvantages and disadvantages of NLP and LP based OPF models, what we seeks isan OPF model which can have main advantages of both LP OPF models (Efficientnumerical solvers) and full AC OPF models (Results accuracy). In this thesis, wedevelop convex optimization problems which can be adopted as both PF and OPFmodels which are capable of catching the nonlinear nature of power systems asmuch as possible while can be solved by efficient solution methods such as InteriorPoint Methods (IPMs). These OPF models can incorporate HVDC links, windfarm Multi Terminal HVDC (MTDC) grids, and shunt FACTS devices.

QC 20150521

The main body of this thesis consists of six appended papers. In the  first two, different  cooperative surveillance problems are considered. The second two consider different aspects of the trajectory planning problem, while the last two deal with observer design for mobile robotic and Euler-Lagrange systems respectively.In Papers A and B,  a combinatorial optimization based framework to cooperative surveillance missions using multiple Unmanned Ground Vehicles (UGVs) is proposed. In particular, Paper A  considers the the Minimum Time UGV Surveillance Problem (MTUSP) while Paper B treats the Connectivity Constrained UGV Surveillance Problem (CUSP). The minimum time formulation is the following. Given a set of surveillance UGVs and a polyhedral area, find waypoint-paths for all UGVs such that every point of the area is visible from  a point on a waypoint-path and such that the time for executing the search in parallel is minimized.  The connectivity constrained formulation  extends the MTUSP by additionally requiring the induced information graph to be  kept recurrently connected  at the time instants when the UGVs  perform the surveillance mission.  In these two papers, the NP-hardness of  both these problems are shown and decomposition techniques are proposed that allow us to find an approximative solution efficiently in an algorithmic manner.Paper C addresses the problem of designing a real time, high performance trajectory planner for an aerial vehicle that uses information about terrain and enemy threats, to fly low and avoid radar exposure on the way to a given target. The high-level framework augments Receding Horizon Control (RHC) with a graph based terminal cost that captures the global characteristics of the environment.  An important issue with RHC is to make sure that the greedy, short term optimization does not lead to long term problems, which in our case boils down to two things: not getting into situations where a collision is unavoidable, and making sure that the destination is actually reached. Hence, the main contribution of this paper is to present a trajectory planner with provable safety and task completion properties. Direct methods for trajectory optimization are traditionally based on a priori temporal discretization and collocation methods. In Paper D, the problem of adaptive node distribution is formulated as a constrained optimization problem, which is to be included in the underlying nonlinear mathematical programming problem. The benefits of utilizing the suggested method for  online  trajectory optimization are illustrated by a missile guidance example.In Paper E, the problem of active observer design for an important class of non-uniformly observable systems, namely mobile robotic systems, is considered. The set of feasible configurations and the set of output flow equivalent states are defined. It is shown that the inter-relation between these two sets may serve as the basis for design of active observers. The proposed observer design methodology is illustrated by considering a  unicycle robot model, equipped with a set of range-measuring sensors. Finally, in Paper F, a geometrically intrinsic observer for Euler-Lagrange systems is defined and analyzed. This observer is a generalization of the observer proposed by Aghannan and Rouchon. Their contractivity result is reproduced and complemented  by  a proof  that the region of contraction is infinitely thin. Moreover, assuming a priori bounds on the velocities, convergence of the observer is shown by means of Lyapunov's direct method in the case of configuration manifolds with constant curvature.
QC 20100622
TAIS, AURES

碩士
中華大學
機械工程學系碩士在職專班
99
Solder joint reliability is of great concern to semiconductor and electronic product manufacturers. Due to the relative size effect between solder ball and bump, the reliability of solder interconnections for large-size flip chip ball grid array (FCBGA) is investigating vigorously. The purpose of this study is to investigate the solder ball life prediction and optimum design of the large size FCBGA using finite element method and Taguchi method. A particular FCBGA package with package size of 31×31 mm2, chip size of 18×18 mm2, eutetic solder connections considering the bilinear strain hardebing plasticity and hyperbolic sine creep model, and thermal cycling test in ranging 0 °C to 100 °C was performed the overall analysis processes to predict the solder ball life. The most robust design of Taguchi method was applied to investigate the critical geometric parameters for optimum design of the large size FCBGA. In utilizing of DOE (design of experimental), one chosen heatsink, ball diameter, ball height, PCB thickness, core height, chip thickness, buildup height and ball upper pad diameter as paremeters to be studied. The Taguchi orthogonal array L18(21×37) was set up based on the heatsink with two levels (with and without) and the other parameters with three levels. This study observed that (1) With heatsink provides a better life; (2) The smaller buildup thickness and solder ball diameter has a better life performance; (3) The larger ball height and pad diameter has a better life performance; (4) The level 2 of PCB thickness, core thickness and chip thickness provides a better life.

碩士
元智大學
電機工程學系
106
Smart grid is a rising trend of the future power grid, nowadays, continuous innovation in electric vehicles (EVs) benefits the growth of its market share. Therefore, in the foreseeable future, its power system will have a tremendous impact on the scheduling of the overall electricity grid. Most of the optimization strategies only consider either the service revenue of the power supply terminal or the peak of the optimized power grid load. Due to the contradiction between them, most of the optimal scheduling strategies cannot effectively satisfy both aspects. The background of this research is involed in the intergration of a variety of electric vehicles into the grid. We studied the current development of the smart grid and electric vehicles all over the world, and established a mathematic model of charging and discharging for all kinds of electric vehicles. Besides, we systematically analyzed present scheduling strategies related to electric vehicles. This article focuses on electric vehicle charging and swapping station on the basis of primary power system. It is inevitable requirements of smart grid to ensure the load stability for the primary power supply end. In addition, to guarantee the income of primary power supply service with time-of-use price is also vital interests of the suppliers. Therefore, this paper presents a multi-objective optimization solution, which is expected to increase the daily load factor of electric vehicle charging and swapping stations, and at the same time, to ensure the service income of charging electric vehicles within an acceptable range.

This dissertation focuses on two specific problems related to the reliability of the modern power grid. The first part investigates the economic dispatch problem with uncertain power sources. The classic economic dispatch problem seeks generator power output levels that meet demand most efficiently; we add risk-awareness to this by explicitly modeling the uncertainty of intermittent power sources using chance-constrained optimization and incorporating the chance constraints into the standard optimal power flow framework. The result is a dispatch of power which is substantially more robust to random fluctuations with only a small increase in economic cost. Furthermore, it uses an algorithm which is only moderately slower than the conventional practice. The second part investigates “the power grid attack problem”: aiming to maximize disruption to the grid, how should an attacker distribute a budget of “damage” across the power lines? We formulate it as a continuous problem, which bypasses the combinatorial explosion of a discrete formulation and allows for interesting attacks containing lines that are only partially damaged rather than completely removed. The result of our solution to the attack problem can provide helpful information to grid planners seeking to improve the resilience of the power grid to outages and disturbances. Both parts of this dissertation include extensive experimental results on a number of cases, including many realistic large-scale instances.

碩士
國立高雄應用科技大學
電子工程系碩士班
104
With the depletion of fossil fuels, the use of renewable energy has become the nifty option to meet the energy demand. In order to meet the high load demand the use of renewable energy could be optimized, however, the renewable energy naturally is out of human control since the nature of renewable energy such as the wind and solar energy is uncertain. The hybrid system could be a well alternative to support the use of renewable energy. Using probability density function and sampling to define Estimation Point of the source, this study will examine the probability of generating power in the grid system including the management energy system between the source and the demand as well. The evolutionary algorithm such as Genetic Algorithm will be used to execute the optimization decision value for the optimal capacity size. The proposed method will present the solution of renewable energy more efficiently and in balance. The non-one-sided energy generation would support each other in the system as well. The variance of the load shifting as the demand management will show in various rate includes the generation sizing. The flexibility of the system which affects the capacity decision of the hybrid system is also assessed.

Indiana University-Purdue University Indianapolis (IUPUI)
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 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. 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. 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 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 generation events from transient swings caused by other events. 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. 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.

碩士
國立中正大學
資訊工程研究所
102
A smart grid is a modernized electrical grid that introduces a two-way communication where electricity and information can be exchanged between the utility company and micro-grids. Small scale generators constitute renewable energy resources including photovoltaic (PV), wind turbine, and fuel cell which are usually used to maintain the loads in a micro-grid. But intermittency of the generators caused by the unstable weather conditions reduced power quality, which can be improved by energy storage systems. Further, we can ease the stress of the utility company and reduce electricity cost by appropriately using energy storage systems (ESS) during peak electricity usage. Nevertheless, the use of ESS in micro-grids has introduced challenges of it own such as the prediction of electricity usage/generation, scheduling and control, battery lifetime, etc. To address the ESS issues in this Thesis, we propose a Model-Predictive Control (MPC)-based schedulingmethod for ESS in amicro-grid. By using a high accuracy load prediction model, we can effectively charge/discharge when and what amount of energy as required. Through a time window-based optimization, the proposed MPC-based scheduling for ESS increases cost reduction of electricity in a micro-grid by taking amount of prediction power required by loads, amount of prediction power supplied by generators, charge/discharge operations for ESS, and dynamic electricity price declared by the utility company into consideration. Further, we present the trade off between cost reduction of electricity and lifetime of ESS. A multi-agent system is used to model a micro-grid. A micro-grid intelligent agent (MIA) can participate in the electricity bidding market which works via an auction mechanism. Experiments show that theMPC-based scheduling method for ESS gives the highest cost reduction of 3.4% compared to other ESS strategies. Through bidding market, we can achieve an average cost saving of 35.25% with the first-price sealed auction and 34.86% with the second-price sealed auction.