Dans cette thèse, nous proposons des solutions parrallèles basées sur le systèmes actuel GPU (graphics processing unit) pour deux problèmes de minimisation de graphe Euclidien, à savoir le problème de forêt/arbre couvrant minimum Euclidien (EMSF / EMST) et le problème du voyageur commerce (TSP). Les solutions proposées résolvent également aussi le problème d'une paire bichromatique la plus proche (BCP), et suivent la technique de ``contrôle décentralisé, du parallélisme des données et des mémoires partagées par GPU".Nous proposons une technique de recherche dans le voisinage le plus proche de dimension K Euclidienne basée sur les approches classiques de NNS d’Elias qui divisent l’espace Euclidien en cellules congruentes et ne se chevauchant pas, où la taille des points de chaque cellule est délimitée. Nous proposons aussi une technique d'élagage pour obtenir le NNS à base de composants afin de trouver le point de sortie le plus proche de l'ensemble de points de requête de Q dans la complexité temporelle linéaire séquentielle lorsque les données sont uniformément réparties. Ces techniques sont utilisées conjointement avec deux GPU algorithmes proposés pour arbre traversement, à savoir la recherche en largeur bidirectionnelle GPU et la liste chaînée dynamique distribuée, afin d'adresser le BCP. Basé sur la solution BCP, un algorithme parallèle Divide and Conquer est implémenté pour construire EMSF et EMST totalement côté GPU. Le TSP est adressé avec différents algorithmes de recherche locaux parallèles 2-opt, dans lesquels nous proposons une méthodologie ``évaluation multiple K-opt, mouvements multiples K-opt" afin d’exécuter simultanément, sans interférence, des processus massifs 2-/3-opt mouvements qui se retrouvent globalement sur le même circuit TSP pour de nombreux bords. Cette méthodologie est expliquée en détail pour montrer comment nous obtenons un calcul haute performance à la fois du côté du GPU et CPU. Nous testons les solutions proposées et rapportons des résultats de comparaison expérimentale par rapport aux algorithmes de pointe. / In this thesis, we propose parallel solutions based on current graphics processing unit (GPU) system for two Euclidean graph minimization problems, namely the Euclidean minimum spanning forest/tree (EMSF/EMST) and the travelling salesman problem (TSP). The proposed solutions also solve the bichromatic closest pair (BCP) problem, and follow technique of ``decentralized control, data parallelism, GPU shared memories".We propose a Euclidean K-dimensional nearest neighbourhood search (NNS) technique based on classical Elias' NNS approaches that divide the Euclidean space into congruent and non-overlapping cells where size of points in each cell is bounded. We propose a pruning technique to obtain component-based NNS to find a query point set Q's closest outgoing point within sequential linear time complexity when the data is uniformly distributed. These techniques are used together with two proposed GPU tree traversal algorithms, namely the GPU two-direction Breadth-first search and distributed dynamic linked list, to address the BCP. Based on the BCP solution, a divide and conquer parallel algorithm is implemented for building EMSF and EMST totally on GPU side. The TSP is addressed with different parallel 2-opt local search algorithms, in which we propose a ``multiple K-opt evaluation, multiple K-opt moves" methodology in order to simultaneously execute, without interference, massive 2-/3-opt moves that are globally found on the same TSP tour for many edges. This methodology is explained in details to show how we obtain high performance computing both on GPU and CPU side. We test the proposed solutions and report experimental comparison results against the state-of-the-art algorithms.

Increasing societal demand for automation has led to considerable efforts to control large-scale complex systems, especially in the area of autonomous intelligent control methods. The control system of a large-scale complex system needs to satisfy four system level requirements: robustness, flexibility, reusability, and scalability. Corresponding to the four system level requirements, there arise four major challenges. First, it is difficult to get accurate and complete information. Second, the system may be physically highly distributed. Third, the system evolves very quickly. Fourth, emergent global behaviors of the system can be caused by small disturbances at the component level. The Multi-Agent Based Control (MABC) method as an implementation of distributed intelligent control has been the focus of research since the 1970s, in an effort to solve the above-mentioned problems in controlling large-scale complex systems. However, to the author's best knowledge, all MABC systems for large-scale complex systems with significant uncertainties are problem-specific and thus difficult to extend to other domains or larger systems. This situation is partly due to the control architecture of multiple agents being determined by agent to agent coupling and interaction mechanisms. Therefore, the research objective of this dissertation is to develop a comprehensive, generalized framework for the control system design of general large-scale complex systems with significant uncertainties, with the focus on distributed control architecture design and distributed inference engine design. A Hybrid Multi-Agent Based Control (HyMABC) architecture is proposed by combining hierarchical control architecture and module control architecture with logical replication rings. First, it decomposes a complex system hierarchically; second, it combines the components in the same level as a module, and then designs common interfaces for all of the components in the same module; third, replications are made for critical agents and are organized into logical rings. This architecture maintains clear guidelines for complexity decomposition and also increases the robustness of the whole system. Multiple Sectioned Dynamic Bayesian Networks (MSDBNs) as a distributed dynamic probabilistic inference engine, can be embedded into the control architecture to handle uncertainties of general large-scale complex systems. MSDBNs decomposes a large knowledge-based system into many agents. Each agent holds its partial perspective of a large problem domain by representing its knowledge as a Dynamic Bayesian Network (DBN). Each agent accesses local evidence from its corresponding local sensors and communicates with other agents through finite message passing. If the distributed agents can be organized into a tree structure, satisfying the running intersection property and d-sep set requirements, globally consistent inferences are achievable in a distributed way. By using different frequencies for local DBN agent belief updating and global system belief updating, it balances the communication cost with the global consistency of inferences. In this dissertation, a fully factorized Boyen-Koller (BK) approximation algorithm is used for local DBN agent belief updating, and the static Junction Forest Linkage Tree (JFLT) algorithm is used for global system belief updating. MSDBNs assume a static structure and a stable communication network for the whole system. However, for a real system, sub-Bayesian networks as nodes could be lost, and the communication network could be shut down due to partial damage in the system. Therefore, on-line and automatic MSDBNs structure formation is necessary for making robust state estimations and increasing survivability of the whole system. A Distributed Spanning Tree Optimization (DSTO) algorithm, a Distributed D-Sep Set Satisfaction (DDSSS) algorithm, and a Distributed Running Intersection Satisfaction (DRIS) algorithm are proposed in this dissertation. Combining these three distributed algorithms and a Distributed Belief Propagation (DBP) algorithm in MSDBNs makes state estimations robust to partial damage in the whole system. Combining the distributed control architecture design and the distributed inference engine design leads to a process of control system design for a general large-scale complex system. As applications of the proposed methodology, the control system design of a simplified ship chilled water system and a notional ship chilled water system have been demonstrated step by step. Simulation results not only show that the proposed methodology gives a clear guideline for control system design for general large-scale complex systems with dynamic and uncertain environment, but also indicate that the combination of MSDBNs and HyMABC can provide excellent performance for controlling general large-scale complex systems.

我們從計算股價的相關矩陣，然後利用隨機矩陣定理的結果，了解到股票市場並非符合隨機過程的預測，進而得知股票對股票之間具有關聯性，然其長時距下股票價格對數報酬的變化會呈現隨機行走的模式，因此我們對其結果提出二種不同的耦合隨機行走模型，試圖闡釋股票市場間的關聯性可融合到耦合隨機行走模型之中，並藉由均方對數報酬(mean square log-return，MSLR)來探討此事情。 最後，為了瞭解關聯性的關係，並利用其來了解股票市場內部結構的特性，因此我們利用股價的相關矩陣來建構最小展開樹進行分析，發現當時間尺度越大其圖形越密集，中心幾乎為「GE」這家公司，因此其股票市場具有一定的判斷指標。 / By means of calculating the correlation matrix of the price of stock and using the results of random matrix theorems，we learned that the stock market does not match the prediction of stochastic processes and the stock-stock is correlated。However，stock’s price log-return changes under long time scale will appear random walk model. Therefore，we propose two kinds of the different coupled random walk model，that try to explain the correlation between the stock markets can be integrated into the coupled random walk model，and using the mean square log-return( MSLR) to investigate this issue。 Finally，to understand the relationship of correlation matrix and by using it to know the characteristics of the underlying structure of the stock market，we use the correlation matrix of the price to construct the minimum spanning tree for analysis。The results showed that when the time scale is greater, the graphics are more intensive，and the center is almost the same company，"GE"， indicating that the stock market has a certain judgment index。

Made available in DSpace on 2014-12-17T15:47:03Z (GMT). No. of bitstreams: 1 SilviaMDMM_TESE.pdf: 3010194 bytes, checksum: 43610ec3f0a30c2e5ef7fb5c0b2dc5b0 (MD5) Previous issue date: 2013-12-16 / Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior / The Quadratic Minimum Spanning Tree Problem (QMST) is a version of the Minimum Spanning Tree Problem in which, besides the traditional linear costs, there is a quadratic structure of costs. This quadratic structure models interaction effects between pairs of edges. Linear and quadratic costs are added up to constitute the total cost of the spanning tree, which must be minimized. When these interactions are restricted to adjacent edges, the problem is named Adjacent Only Quadratic Minimum Spanning Tree (AQMST). AQMST and QMST are NP-hard problems that model several problems of transport and distribution networks design. In general, AQMST arises as a more suitable model for real problems. Although, in literature, linear and quadratic costs are added, in real applications, they may be conflicting. In this case, it may be interesting to consider these costs separately. In this sense, Multiobjective Optimization provides a more realistic model for QMST and AQMST. A review of the state-of-the-art, so far, was not able to find papers regarding these problems under a biobjective point of view. Thus, the objective of this Thesis is the development of exact and heuristic algorithms for the Biobjective Adjacent Only Quadratic Spanning Tree Problem (bi-AQST). In order to do so, as theoretical foundation, other NP-hard problems directly related to bi-AQST are discussed: the QMST and AQMST problems. Bracktracking and branch-and-bound exact algorithms are proposed to the target problem of this investigation. The heuristic algorithms developed are: Pareto Local Search, Tabu Search with ejection chain, Transgenetic Algorithm, NSGA-II and a hybridization of the two last-mentioned proposals called NSTA. The proposed algorithms are compared to each other through performance analysis regarding computational experiments with instances adapted from the QMST literature. With regard to exact algorithms, the analysis considers, in particular, the execution time. In case of the heuristic algorithms, besides execution time, the quality of the generated approximation sets is evaluated. Quality indicators are used to assess such information. Appropriate statistical tools are used to measure the performance of exact and heuristic algorithms. Considering the set of instances adopted as well as the criteria of execution time and quality of the generated approximation set, the experiments showed that the Tabu Search with ejection chain approach obtained the best results and the transgenetic algorithm ranked second. The PLS algorithm obtained good quality solutions, but at a very high computational time compared to the other (meta)heuristics, getting the third place. NSTA and NSGA-II algorithms got the last positions / O problema da ?rvore Geradora M?nima Quadr?tica (AGMQ) ? uma vers?o do problema da ?rvore Geradora M?nima na qual se considera, al?m dos custos lineares tradicionais, uma estrutura de custos quadr?tica. Tal estrutura quadr?tica modela efeitos de intera??o entre pares de arestas. Os custos lineares e quadr?ticos s?o somados para compor o custo total da ?rvore geradora, que deve ser minimizado. Quando as intera??es s?o restritas ?s arestas adjacentes, o problema ? denominado ?rvore Geradora M?nima Quadr?tica em Adjac?ncia de Arestas (AGMQA). A AGMQA e a AGMQ s?o problemas NP-dif?ceis que modelam diversos problemas de projeto de redes de transporte e distribui??o. Em geral, a AGMQA emerge como um modelo mais apropriado para a modelagem de problemas reais. Embora, na literatura, os custos lineares e quadr?ticos sejam somados, em aplica??es reais, os custos linear e quadr?tico podem ser conflitantes. Neste caso, seria mais interessante considerar os custos separadamente. Neste sentido, a Otimiza??o Multiobjetivo prov? uma modelagem mais realista para os problemas da AGMQ e da AGMQA. Uma revis?o do estado da arte, at? o momento, n?o foi capaz de encontrar qualquer trabalho que investigue esses problemas sob um ponto de vista biobjetivo. O objetivo desta Tese ?, pois, o desenvolvimento de algoritmos exatos e heur?sticos para o Problema Biobjetivo da ?rvore Geradora Quadr?tica em Adjac?ncia de Arestas (AGQA-bi). Para tanto, como fundamenta??o te?rica, discutem-se outros problemas NP-dif?ceis diretamente relacionados ? AGQA-bi, a saber: AGMQ e AGMQA. Algoritmos exatos backtracking e branch-and-bound s?o propostos para o problema-alvo desta investiga??o. Os algoritmos heur?sticos desenvolvidos s?o: busca local Pareto Local Search, Busca Tabu com ejection chain, Algoritmo Transgen?tico, NSGA-II e uma hibridiza??o das duas ?ltimas propostas mencionadas denominada NSTA. Os algoritmos propostos s?o comparados entre si por meio da an?lise de seus desempenhos em experimentos computacionais com casos de teste adaptados da literatura da AGMQ. No que se refere aos algoritmos exatos, a an?lise considera, em especial, o tempo de execu??o. No caso dos algoritmos heur?sticos, al?m do tempo de execu??o, a qualidade do conjunto de aproxima??o gerado ? avaliada. Indicadores de qualidade s?o empregados para aferir tal informa??o. Ferramentas estat?sticas apropriadas s?o usadas na an?lise de desempenho dos algoritmos exatos e heur?sticos. Para o conjunto de inst?ncias utilizado e considerando os crit?rios de qualidade dos conjuntos de aproxima??o gerados e tempo de execu??o dos algoritmos, os experimentos mostraram que o algoritmo de Busca Tabu com ejection chain obteve melhores resultados e que o algoritmo transgen?tico ficou em segundo lugar. A busca local PLS obteve solu??es de qualidade, mas a um tempo computacional muito alto se comparado ?s outras (meta)heur?sticas. Nesse sentido, ocupa a terceira coloca??o. Por fim, ficaram os algoritmos NSTA e NSGAII

Made available in DSpace on 2014-12-17T15:47:45Z (GMT). No. of bitstreams: 1 GivanaldoRS.pdf: 1524067 bytes, checksum: d73e18e4ae3a0bffab7711efc808bffa (MD5) Previous issue date: 2006-05-19 / Combinatorial optimization problems have the goal of maximize or minimize functions defined over a finite domain. Metaheuristics are methods designed to find good solutions in this finite domain, sometimes the optimum solution, using a subordinated heuristic, which is modeled for each particular problem. This work presents algorithms based on particle swarm optimization (metaheuristic) applied to combinatorial optimization problems: the Traveling Salesman Problem and the Multicriteria Degree Constrained Minimum Spanning Tree Problem. The first problem optimizes only one objective, while the other problem deals with many objectives. In order to evaluate the performance of the algorithms proposed, they are compared, in terms of the quality of the solutions found, to other approaches / Os problemas de otimiza??o combinat?ria t?m como objetivo maximizar ou minimizar uma fun??o definida sobre um certo dom?nio finito. J? as metaheur?sticas s?o procedimentos destinados a encontrar uma boa solu??o, eventualmente a ?tima, consistindo na aplica??o de uma heur?stica subordinada, a qual tem que ser modelada para cada problema espec?fico. Este trabalho apresenta algoritmos baseados na t?cnica de otimiza??o por nuvem de part?culas (metaheur?stica) para dois problemas de otimiza??o combinat?ria: o Problema do Caixeiro Viajante e o Problema da ?rvore Geradora M?nima Restrita em Grau Multicrit?rio. O primeiro ? um problema em que apenas um objetivo ? otimizado, enquanto o segundo ? um problema que deve lidar com m?ltiplos objetivos. Os algoritmos propostos s?o comparados a outras abordagens para o mesmo problema em quest?o, em termos de qualidade de solu??o, a fim de verificar a efici?ncia desses algoritmos

Made available in DSpace on 2014-12-17T15:47:55Z (GMT). No. of bitstreams: 1 SilviaMDM_DISSERT.pdf: 1535044 bytes, checksum: 925f2f885f42335d55c35aa64bb4d026 (MD5) Previous issue date: 2011-02-17 / Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior / The Multiobjective Spanning Tree is a NP-hard Combinatorial Optimization problem whose application arises in several areas, especially networks design. In this work, we propose a solution to the biobjective version of the problem through a Transgenetic Algorithm named ATIS-NP. The Computational Transgenetic is a metaheuristic technique from Evolutionary Computation whose inspiration relies in the conception of cooperation (and not competition) as the factor of main influence to evolution. The algorithm outlined is the evolution of a work that has already yielded two other transgenetic algorithms. In this sense, the algorithms previously developed are also presented. This research also comprises an experimental analysis with the aim of obtaining information related to the performance of ATIS-NP when compared to other approaches. Thus, ATIS-NP is compared to the algorithms previously implemented and to other transgenetic already presented for the problem under consideration. The computational experiments also address the comparison to two recent approaches from literature that present good results, a GRASP and a genetic algorithms. The efficiency of the method described is evaluated with basis in metrics of solution quality and computational time spent. Considering the problem is within the context of Multiobjective Optimization, quality indicators are adopted to infer the criteria of solution quality. Statistical tests evaluate the significance of results obtained from computational experiments / A ?rvore Geradora Multiobjetivo ? um problema de Otimiza??o Combinat?ria NP-?rduo. Esse problema possui aplica??o em diversas ?reas, em especial, no projeto de redes. Nesse trabalho, prop?e-se uma solu??o para o problema em sua vers?o biobjetivo por meio de um Algoritmo Transgen?tico, denominado ATIS-NP. A Transgen?tica Computacional ? uma t?cnica metaheur?stica da Computa??o Evolucion?ria cuja inspira??o est? na coopera??o (e n?o na competi??o) como fator de maior influ?ncia para a evolu??o. O algoritmo proposto ? a evolu??o de um trabalho que j? originou dois outros algoritmos transgen?ticos. Nesse sentido, os algoritmos previamente desenvolvidos tamb?m s?o apresentados. Essa pesquisa compreende ainda uma an?lise experimental que visa obter informa??es quanto ao desempenho do ATIS-NP quando comparado a outros algoritmos. Para tanto, o ATIS-NP ? comparado aos dois algoritmos anteriormente implementados, bem como a outro transgen?tico proposto na literatura para o problema tratado. Os experimentos computacionais abrangem ainda a compara??o do algoritmo desenvolvido a duas abordagens recentes da literatura que obt?m excelentes resultados, um GRASP e um gen?tico. A efici?ncia do m?todo apresentado ? avaliada com base em medidas de qualidade de solu??o e tempo computacional despendido. Uma vez que o problema se insere no contexto da Otimiza??o Multiobjetivo, indicadores de qualidade s?o utilizados para inferir o crit?rio de qualidade de solu??es obtidas. Testes estat?sticos avaliam a signific?ncia dos resultados obtidos nos experimentos computacionais

Made available in DSpace on 2014-12-17T15:48:01Z (GMT). No. of bitstreams: 1 PatriciaMLLD_DISSERT.pdf: 2062279 bytes, checksum: edf20f81d921e118846850abb8ec8a1d (MD5) Previous issue date: 2012-03-05 / The Multiobjective Spanning Tree Problem is NP-hard and models applications in several areas. This research presents an experimental analysis of different strategies used in the literature to develop exact algorithms to solve the problem. Initially, the algorithms are classified according to the approaches used to solve the problem. Features of two or more approaches can be found in some of those algorithms. The approaches investigated here are: the two-stage method, branch-and-bound, k-best and the preference-based approach. The main contribution of this research lies in the fact that no research was presented to date reporting a systematic experimental analysis of exact algorithms for the Multiobjective Spanning Tree Problem. Therefore, this work can be a basis for other research that deal with the same problem. The computational experiments compare the performance of algorithms regarding processing time, efficiency based on the number of objectives and number of solutions found in a controlled time interval. The analysis of the algorithms was performed for known instances of the problem, as well as instances obtained from a generator commonly used in the literature / O Problema da ?rvore Geradora Multiobjetivo ? NP-?rduo e modela aplica??es em diversas ?reas. Esta pesquisa apresenta uma an?lise experimental de diferentes estrat?gias utilizadas na literatura para desenvolver algoritmos exatos para resolver o problema. Inicialmente, os algoritmos s?o classificados de acordo com as abordagens utilizadas para resolver o problema. Caracter?sticas de duas ou mais abordagens podem ser encontradas em alguns desses algoritmos. As abordagens aqui investigadas s?o: o m?todo duas fases, branch-and-bound, k-best e a abordagem baseada em prefer?ncia. A principal contribui??o deste trabalho est? no fato de que nenhuma pesquisa desenvolvida at? o momento relata uma an?lise sistem?tica experimental de algoritmos exatos para o problema da ?rvore Geradora Multiobjetivo. Portanto, este trabalho pode ser uma base para outras pesquisas que lidam com o mesmo problema. Os experimentos computacionais comparam o desempenho de algoritmos em rela??o ao tempo de processamento, ? efici?ncia com base no n?mero de objetivos e no n?mero de solu??es encontradas em um intervalo de tempo controlado. A an?lise dos algoritmos foi realizada para inst?ncias conhecidas do problema, bem como para inst?ncias obtidas a partir de um gerador bastante utilizado na literatura

This dissertation investigates in which conditions the optimal configuration of an electric power network is a minimum length spanning tree, and in which conditions it is shortest path tree configuration. For this purpose the dissertation, it applies computational optimization mathematical models of an optimal local access network design problem. The focus of the study is the 13.8 kV spacer cable primary radial networks. Applied models seek for the balance betweenfixedcostsandvariablecosts.Savedvaluesfromanoptimalnetworkcouldbeapplied to increase the range of the network and people reached as well. The bibliographic research is compound by three parts: graph theory, local access network optimization models, and distribution network costs. Research methodology includes the choice of the distribution system, determination of fixed and variable costs, choice and implementation of the local access network optimization models, tests in hypothetical and realistic systems by using the CPLEX solver, analysis of the resulting configuration, and construction of graphics to facilitate the results evaluation. It was found that the relationship between fixed costs and variable costs influences the optimal configuration of the distribution network in such a way that a low value of the quotient between fixed costs and variable costs contributes to a shortest path tree. On the other hand, a high quotient between fixed costs and variable costs contributes to a minimum length spanning tree configuration. However, others parameters must be considered to determine the network configuration such as extension, arches demand and quantity of arches. / O presente trabalho visa investigar sob quais condições a configuração ótima de uma rede de distribuição elétrica é uma árvore geradora mínima (AGM) e sob quais é uma árvore de caminhos mínimos (ACM). Utilizando, para isso, modelos matemáticos computacionais de otimização topológica de redes de utilidade pública. As redes de distribuição estudadas foram do tipo aérea radial primária protegida (ARPP) com nível de tensão em 13,8 kV. Os modelos utilizados prezam pelo equilíbrio entre o custo de investimento inicial (fixo) e os custos decorrentes da transferência de energia elétrica (variável). Os valores economizados através de uma configuração ótima da rede podem ser convertidos em investimentos para aumentar o número de pessoas com acesso aos recursos energéticos com eficiência e qualidade. A revisão bibliográfica foi dividida em três partes: teoria dos grafos, modelos de otimização de redes de acesso local e custos de redes de distribuição. A metodologia utilizada compreendeu as seguintes etapas: escolha do tipo de sistema de distribuição, determinação dos custos fixo e variável, escolha e implementação (GAMS) dos modelos, testes com exemplos de redes usando o solver CPLEX, análise das configurações resultantes e elaboração de gráficos para facilitar a avaliação dos resultados. Os resultados mostraram que a relação entre o custo fixo β e o custo variável γ exerce influência determinante na configuração ótima de uma rede de distribuição ARPP. Um valor baixo de β/γ, favorece a ACM. Já valores elevados de β/γ, conduzem a solução para uma AGM. No entanto, essa relação não é o único fator que determina a configuração da rede, outros parâmetros como extensão, demanda dos nós e quantidade de possíveis arcos influenciam de forma significativa na solução apresentada.

A propriedade de auto-cura, em redes inteligente de distribuição de energia elétrica, consiste em encontrar uma proposta de reconfiguração do sistema de distribuição com o objetivo de recuperar parcial ou totalmente o fornecimento de energia aos clientes da rede, na ocorrência de uma falha na rede que comprometa o fornecimento. A busca por uma solução satisfatória é um problema combinacional cuja complexidade está ligada ao tamanho da rede. Um método de busca exaustiva se torna um processo muito demorado e muitas vezes computacionalmente inviável. Para superar essa dificuldade, pode-se basear nas técnicas de geração de árvores de extensão mínima do grafo, representando a rede de distribuição. Porém, a maioria dos estudos encontrados nesta área são implementações centralizadas, onde proposta de reconfiguração é obtida por um sistema de supervisão central. Nesta dissertação, propõe-se uma implementação distribuída, onde cada chave da rede colabora na elaboração da proposta de reconfiguração. A solução descentralizada busca uma redução no tempo de reconfiguração da rede em caso de falhas simples ou múltiplas, aumentando assim a inteligência da rede. Para isso, o algoritmo distribuído GHS é utilizado como base na elaboração de uma solução de auto-cura a ser embarcada nos elementos processadores que compõem as chaves de comutação das linhas da rede inteligente de distribuição. A solução proposta é implementada utilizando robôs como unidades de processamento que se comunicam via uma mesma rede, constituindo assim um ambiente de processamento distribuído. Os diferentes estudos de casos testados mostram que, para redes inteligentes de distribuição compostas por um único alimentador, a solução proposta obteve sucesso na reconfiguração da rede, indiferentemente do número de falhas simultâneas. Na implementação proposta, o tempo de reconfiguração da rede não depende do número de linhas nela incluídas. A implementação apresentou resultados de custo de comunicação e tempo dentro dos limites teóricos estabelecidos pelo algoritmo GHS. / The characteristic of self-healing, in smart grids, consists of finding a proposal for a reconfiguration of distribution system aiming at restoring the power, partially or completely to supply the network clients, in the event of network failure, which compromises the energy supply. The search for a satisfactory solution is a combinatorial problem whose complexity is proportional to the network size. An exhaustive search-based method is a time-consuming process and often computationally not viable. To overcome this difficulty, techniques for generating minimal spanning trees of the graph, which represents the smart grid, are exploited. However, the majority of studies in this area provide centralized implementations, where the solution for reconfiguration is achieved by a central control system. In this dissertation, we propose a distributed implementation, where each of the network switch collaborates in the development of the solution for reconfiguration. The proposed decentralized solution seeks a reduction in terms of the network reconfiguration time, in case of a single or multiple failures, thus increasing network intelligence. In this purpose, the GHS distributed algorithm is used as a basis for developing a self-healing solution to be embedded in the processing elements that are included within the line commutation switches of smart grid. The proposed solution is implemented using robots as processing units, which communicate via the same network, thereby creating a distributed processing environment. The several tested case studies show that, for smart grids that to have a single distribution feeder, the proposed solution allowed for a successful reconfiguration of the network, regardless of the number of simultaneous failures. In the proposed implementation, the network reconfiguration time does not depend on the number of buses and lines included. The implementation presents results of communication cost and time within the theoretical bounds of the GHS algorithm.

A propriedade de auto-cura, em redes inteligente de distribuição de energia elétrica, consiste em encontrar uma proposta de reconfiguração do sistema de distribuição com o objetivo de recuperar parcial ou totalmente o fornecimento de energia aos clientes da rede, na ocorrência de uma falha na rede que comprometa o fornecimento. A busca por uma solução satisfatória é um problema combinacional cuja complexidade está ligada ao tamanho da rede. Um método de busca exaustiva se torna um processo muito demorado e muitas vezes computacionalmente inviável. Para superar essa dificuldade, pode-se basear nas técnicas de geração de árvores de extensão mínima do grafo, representando a rede de distribuição. Porém, a maioria dos estudos encontrados nesta área são implementações centralizadas, onde proposta de reconfiguração é obtida por um sistema de supervisão central. Nesta dissertação, propõe-se uma implementação distribuída, onde cada chave da rede colabora na elaboração da proposta de reconfiguração. A solução descentralizada busca uma redução no tempo de reconfiguração da rede em caso de falhas simples ou múltiplas, aumentando assim a inteligência da rede. Para isso, o algoritmo distribuído GHS é utilizado como base na elaboração de uma solução de auto-cura a ser embarcada nos elementos processadores que compõem as chaves de comutação das linhas da rede inteligente de distribuição. A solução proposta é implementada utilizando robôs como unidades de processamento que se comunicam via uma mesma rede, constituindo assim um ambiente de processamento distribuído. Os diferentes estudos de casos testados mostram que, para redes inteligentes de distribuição compostas por um único alimentador, a solução proposta obteve sucesso na reconfiguração da rede, indiferentemente do número de falhas simultâneas. Na implementação proposta, o tempo de reconfiguração da rede não depende do número de linhas nela incluídas. A implementação apresentou resultados de custo de comunicação e tempo dentro dos limites teóricos estabelecidos pelo algoritmo GHS. / The characteristic of self-healing, in smart grids, consists of finding a proposal for a reconfiguration of distribution system aiming at restoring the power, partially or completely to supply the network clients, in the event of network failure, which compromises the energy supply. The search for a satisfactory solution is a combinatorial problem whose complexity is proportional to the network size. An exhaustive search-based method is a time-consuming process and often computationally not viable. To overcome this difficulty, techniques for generating minimal spanning trees of the graph, which represents the smart grid, are exploited. However, the majority of studies in this area provide centralized implementations, where the solution for reconfiguration is achieved by a central control system. In this dissertation, we propose a distributed implementation, where each of the network switch collaborates in the development of the solution for reconfiguration. The proposed decentralized solution seeks a reduction in terms of the network reconfiguration time, in case of a single or multiple failures, thus increasing network intelligence. In this purpose, the GHS distributed algorithm is used as a basis for developing a self-healing solution to be embedded in the processing elements that are included within the line commutation switches of smart grid. The proposed solution is implemented using robots as processing units, which communicate via the same network, thereby creating a distributed processing environment. The several tested case studies show that, for smart grids that to have a single distribution feeder, the proposed solution allowed for a successful reconfiguration of the network, regardless of the number of simultaneous failures. In the proposed implementation, the network reconfiguration time does not depend on the number of buses and lines included. The implementation presents results of communication cost and time within the theoretical bounds of the GHS algorithm.