"Maintaining an efficient and reliable infrastructure requires continuous monitoring and control. In order to accomplish these tasks, algorithms are needed to process large sets of data and for modeling based on these processed data sets. For this reason, computationally efficient and accurate modeling algorithms along with data compression techniques and optimal yet practical control methods are in demand. These tools can help model structures and improve their performance. In this thesis, these two aspects are addressed separately. A principal component analysis based adaptive neuro-fuzzy inference system is proposed for fast and accurate modeling of time-dependent behavior of a structure integrated with a smart damper. Since a smart damper can only dissipate energy from structures, a challenge is to evaluate the dissipativity of optimal control methods for smart dampers to decide if the optimal controller can be realized using the smart damper. Therefore, a generalized deterministic definition for dissipativity is proposed and a commonly used controller, LQR is proved to be dissipative. Examples are provided to illustrate the effectiveness of the proposed modeling algorithm and evaluating the dissipativity of LQR control method. These examples illustrate the effectiveness of the proposed modeling algorithm and dissipativity of LQR controller."

Este trabalho aborda o problema de modelagem de séries de vazões afluentes aos aproveitamentos hidrelétricos. A previsão de vazão natural fluvial é realizada semanalmente para 158 usinas hidrelétricas do Sistema Interligado Nacional (SIN), pois trata-se de insumo fundamental para o planejamento e operação do sistema elétrico brasileiro. Diversos modelos são utilizados na determinação destas previsões, entre os quais podem ser citados os modelos físicos, os estatísticos e aqueles que aplicam sistemas inteligentes. Apesar de contínuos aprimoramentos terem sido incorporados ao processo de previsão de vazão, existem alguns aproveitamentos hidrelétricos para os quais os resultados de estimação têm apresentado grandes desvios. Neste contexto, com a motivação de se obter uma resposta acurada, investigam-se os sistemas fuzzy como modelos concorrentes aplicados à previsão de vazões semanais. O objetivo do trabalho é reduzir os erros de estimação para uma usina piloto, incorporando à previsão de vazão os dados de precipitação. Para a construção da série histórica de precipitação média da bacia hidrográfica, fez-se uma exaustiva pesquisa por estações pluviométricas, seguida por tratamento de dados de medição e método de interpolação. Ao final do trabalho, é apresentada uma análise comparativa entre os resultados obtidos com o Modelo Autorregressivo Periódico (PAR) e o sistema de inferência fuzzy. Com base no desempenho observado, superior ao modelo autorregressivo, comprova-se a adequação do modelo proposto para a modelagem do processo hidrológico. / This work addresses the modelling problem of hydropower plants reservoir streamflow series. The natural streamflow forecasting for 157 hydroelectric power plants of the National Interconnected System - NIS is updated on a weekly basis, which is an essential input for the planning and operation of the Brazilian Electric Power System. Several models are used to determine this prediction, such as physicals, statisticals and the ones that use intelligent systems. Despite the improvements to natural streamflow forecasting, substantial deviation has been found for the expected results of some hydropower plants. Highlighted the importance of this variable, fuzzy systems applied to weekly streamflows forecasts will be investigated as alternative models, in order to obtain better results. The purpose of this work is to reduce the estimation errors for a pilot hydropower plant, incorporating precipitation data into the forecast. Therefore, an exhaustive research to acquire data from hydrometeorological stations was conducted. After being treated, a variable selection method was applied to the data, defining the most relevant input variables for the prediction model. At the end, a comparative analysis shows that the fuzzy model presents a better performance than the periodic autoregressive model used by ONS to plan the operation of the electric power system.

Este trabalho tem por objetivo localizar faltas em sistemas de distribuição de energia elétrica utilizando apenas dados de tensão pós-falta. Não são utilizados dados de corrente pois sua medição não é um requisito mínimo dos medidores inteligentes. Os dados coletados são fornecidos a um sistema fuzzy multicamadas. A seleção dos dados de entrada para o treinamento do sistema inteligente é uma característica exclusiva deste trabalho, por ser continuação da metodologia desenvolvida durante o mestrado do autor desta tese. O cenário estudado é um alimentador de um sistema de distribuição real, com 1600 barras e 505 transformadores. Para efeito de comparação de resultados, a distância da falta é obtida de duas formas: pela composição das estimações das impedâncias de sequência zero e positiva e pela estimação direta da distância. Os resultados se mostram satisfatórios, com erros condizentes aos que são encontrados na literatura, sendo que a estimação direta da distância da falta mostrou resultados ligeiramente melhores. / This work aims to locate faults in electrical energy distribution systems using only post-fault voltage data. Current data is not used because their measurement is not a minimum requirement of smart meters. The collected data is applied to a multilayer fuzzy inference system. The way voltage data is selected to train the fuzzy system is an exclusive feature of this work, since it is a continuation of the methodology developed during the master\'s dissertation of this thesis author. The scenario studied is a feeder of an actual distribution system, with 1600 buses and 505 transformers. For the purpose of results comparison, the distance of the fault is obtained in two ways: by the composition of the zero and positive sequence impedances estimations and by the direct estimation of the distance. The results are satisfactory, with errors that are consistent with those found in the literature, while the direct estimation of the fault distance presented slightly better results.

Orientador: Suely Cunha Amaro Mantovani / Resumo: No estudo dos manipuladores são utilizados os conceitos da cinemática direta e a inversa. No cálculo da cinemática direta tem-se a facilidade da notação de Denavit-Hartenberg, mas o desafio maior é a resolução da cinemática inversa, que se torna mais complexa conforme aumentam os graus de liberdade do manipulador, além de apresentar múltiplas soluções. As variáveis angulares obtidas pelas equações da cinemática inversa são utilizadas pelo controlador, para posicionar o órgão terminal do manipulador em um ponto específico de seu volume de trabalho. Na busca de alternativas para contornar estes problemas, neste trabalho utilizam-se os Modelos Adaptativos de Inferência Neuro-Fuzzy - ANFIS para a resolução da cinemática inversa, por meio de simulações, para obter o posicionamento de um manipulador robótico de 5 graus de liberdade, composto por sete servomotores controlados pela plataforma de desenvolvimento Intel® Galileo Gen 2, usado como caso de estudo. Nas simulações usamse ANFIS com uma arquitetura com três e quatro funções de pertinência de entrada, do tipo gaussiana. O desempenho da arquitetura da ANFIS implementada foi comparado com uma Rede Perceptron Multicamadas, demonstrando com os resultados favoráveis a ANFIS, a sua capacidade de aprender e resolver com baixo erro quadrático médio e com precisão, a cinemática inversa para o manipulador em estudo. Verifica-se também, que a performance das ANFIS melhora, quanto à precisão dos resultados, demonstrado pelo desvio médio d... (Resumo completo, clicar acesso eletrônico abaixo) / Abstract: In the study of manipulator’s, the concepts of direct and inverse kinematics are used. In the computation of forward kinematics, it has of the ease of Denavit-Hartenberg notation, but the biggest challenge is the resolution of the inverse kinematics, which becomes more complex as the manipulator's degrees of freedom increase, besides presenting multiple solutions. The angular variables obtained by the inverse kinematics equations are used by the controller to position the terminal organ of the manipulator at a specific point in its work volume. In the search for alternatives to overcome these problems, in this work, the Adaptive Neuro-Fuzzy Inference Models (ANFIS) are used to solve the inverse kinematics, by means of simulations, to obtain the positioning of a robot manipulator of 5 degrees of freedom, consisting of seven servomotors controlled by the Intel® Galileo Gen 2 development platform, used as a case's study . In the simulations ANFIS's architecture are used three and four Gaussian membership functions of input. The performance of the implemented ANFIS architecture was compared to a Multi-layered Perceptron Network, demonstrating with the favorable results the ANFIS, its ability to learn and solve with low mean square error and with precision, the inverse kinematics for the manipulator under study. It is also verified that the performance of the ANFIS improves, as regards the accuracy of the results in the training process, , demonstrated by the mean deviation of the... (Complete abstract click electronic access below) / Mestre

An automatically trained, statistically based, fuzzy inference system that functions as a classifier is produced. The hybrid system is designed specifically to be used as a decision support system. This hybrid system has several features which are of direct and immediate utility in the field of decision support, including a mechanism for the discovery of domain knowledge in the form of explanatory rules through the examination of training data; the evaluation of such rules using a simple probabilistic weighting mechanism; the incorporation of input uncertainty using the vagueness abstraction of fuzzy systems; and the provision of a strong confidence measure to predict the probability of system failure. <br /><br /> Analysis of the hybrid fuzzy system and its constituent parts allows commentary on the weighting scheme and performance of the "Pattern Discovery" system on which it is based. <br /><br /> Comparisons against other well known classifiers provide a benchmark of the performance of the hybrid system as well as insight into the relative strengths and weaknesses of the compared systems when functioning within continuous and mixed data domains. <br /><br /> Classifier reliability and confidence in each labelling are examined, using a selection of both synthetic data sets as well as some standard real-world examples. <br /><br /> An implementation of the work-flow of the system when used in a decision support context is presented, and the means by which the user interacts with the system is evaluated. <br /><br /> The final system performs, when measured as a classifier, comparably well or better than other classifiers. This provides a robust basis for making suggestions in the context of decision support. <br /><br /> The adaptation of the underlying statistical reasoning made by casting it into a fuzzy inference context provides a level of transparency which is difficult to match in decision support. The resulting linguistic support and decision exploration abilities make the system useful in a variety of decision support contexts. <br /><br /> Included in the analysis are case studies of heart and thyroid disease data, both drawn from the University of California, Irvine Machine Learning repository.

An automatically trained, statistically based, fuzzy inference system that functions as a classifier is produced. The hybrid system is designed specifically to be used as a decision support system. This hybrid system has several features which are of direct and immediate utility in the field of decision support, including a mechanism for the discovery of domain knowledge in the form of explanatory rules through the examination of training data; the evaluation of such rules using a simple probabilistic weighting mechanism; the incorporation of input uncertainty using the vagueness abstraction of fuzzy systems; and the provision of a strong confidence measure to predict the probability of system failure. <br /><br /> Analysis of the hybrid fuzzy system and its constituent parts allows commentary on the weighting scheme and performance of the "Pattern Discovery" system on which it is based. <br /><br /> Comparisons against other well known classifiers provide a benchmark of the performance of the hybrid system as well as insight into the relative strengths and weaknesses of the compared systems when functioning within continuous and mixed data domains. <br /><br /> Classifier reliability and confidence in each labelling are examined, using a selection of both synthetic data sets as well as some standard real-world examples. <br /><br /> An implementation of the work-flow of the system when used in a decision support context is presented, and the means by which the user interacts with the system is evaluated. <br /><br /> The final system performs, when measured as a classifier, comparably well or better than other classifiers. This provides a robust basis for making suggestions in the context of decision support. <br /><br /> The adaptation of the underlying statistical reasoning made by casting it into a fuzzy inference context provides a level of transparency which is difficult to match in decision support. The resulting linguistic support and decision exploration abilities make the system useful in a variety of decision support contexts. <br /><br /> Included in the analysis are case studies of heart and thyroid disease data, both drawn from the University of California, Irvine Machine Learning repository.

A majority of utilities conduct maintenance of transmission line components based on the results of routine visual inspection. The inspection is normally done by inspectors who detect defects by visually checking transmission line components either from the air (in helicopters), from the ground (by using high-powered binoculars) or from the top of the structure (by climbing the structure). The main problems with visual inspection of transmission lines are that the determination of the defects varies depending on the inspectors' knowledge and experience and that the defects are often reported qualitatively using vague and linguistic terms such as "medium crack", "heavy rust", "small deflection". As a result of these drawbacks, there is a large variance and inconsistency in defect reporting (which, in time, makes it difficult for the utility to monitor the condition of the components) leading to ineffective or wrong maintenance decisions. The use of inspection guides has not been able to fully address these uncertainties. This thesis reports on the application of a visual inspection methodology that is aimed at addressing the above-mentioned problems. A knowledge-based Fuzzy Inference System (FIS) is designed using Matlab's Fuzzy Logic Toolbox as part of the methodology and its application is demonstrated on utility visual inspection practice of porcelain cap and pin insulators. The FIS consists of expert-specified input membership functions (representing various insulator defect levels), output membership functions (indicating the overall conditions of the insulator) and IF-THEN rules. Consistency in the inspection results is achieved because the condition of the insulator is inferred using the same knowledge-base in the FIS rather than by individual inspectors. The output of the FIS is also used in a mathematical model that is developed to suggest appropriate component replacement date. It is hoped that the methodology that is introduced in this research will help utilities achieve better maintenance management of transmission line assets.

Esta dissertação testa e compara dois tipos de modelagem para previsão de uma mesma série temporal. Foi observada uma série temporal de distribuição de energia elétrica e, como estudo de caso, optou-se pela região metropolitana do Estado da Bahia. Foram testadas as combinações de três variáveis exógenas em cada modelo: a quantidade de clientes ligados na rede de distribuição de energia elétrica, a temperatura ambiente e a precipitação de chuvas. O modelo linear de previsão de séries temporais utilizado foi um SARIMAX. A modelagem de inteligência computacional utilizada para a previsão da série temporal foi um sistema de Inferência Fuzzy. Na busca de um melhor desempenho, foram feitos testes de quais variáveis exógenas melhor influenciam no comportamento da energia distribuída em cada modelo. Segundo a avaliação dos testes, o sistema Fuzzy de previsão foi o que obteve o menor erro. Porém dentre os menores erros, os resultados dos testes também indicaram diferentes variáveis exógenas para cada modelo de previsão. / This dissertation tests and compares two types of predicting models to the same time series. A time series of electricity distribution was observed and, as a case study, were opted for the metropolitan region of Bahia State. Three exogenous variables were tested in each model: the number of customers connected to the electricity distribution network, the temperature and the precipitation of rain. The linear model time series forecasting used was a SARIMAX. The modelling of computational intelligence used to predict the time series was a Fuzzy Inference System. For better performance, in each model was tested all the exogenous variables to fit the influence in the energy distributed. According to the evaluation of the tests, the Fuzzy forecasting system presented the lowest error. But among the smallest errors, the results of the tests also indicated different exogenous variables for each forecast model.

Resumo: Este trabalho tem por objetivo principal apresentar o desenvolvimento de um sistema inteligente, utilizando a Teoria Fuzzy, para estimar valores aproximados da densidade do solo a partir de medidas diretas (campo) sem a necessidade de ensaios laboratoriais e, consequentemente, identificar a compactação do solo por meio destes valores estimados. A densidade do solo é um dos principais parâmetros utilizado para a identificação do grau de compactação do solo, e está relacionada com outros parâmetros tais como a resistência à penetração do solo, o teor de água e a textura do solo. Para o desenvolvimento do trabalho foram considerados três parâmetros do solo: a resistência à penetração representado pelo índice de cone (em kPa), o teor de água dado pela umidade do solo (em porcentagem, %), e a textura dada pela quantidade de argila presente no solo (em porcentagem, %). Foram, ainda, considerados solos preparados (passagem de arado, de grade, de escarificador, e outros) e solos não preparados (nenhum tipo de preparado ou em solo de plantio direto). Segundo a porcentagem de argila no solo, estes foram divididos em solo tipo I (teor de argila menor que 30%), solo tipo II (teor de argila entre 30% e 50%), solo tipo III (teor de argila maior que 50%) para o solo não preparado, e solo tipo I (teor de argila menor que 30%) e solo tipo III (teor de argila maior que 50%) para o solo preparado. O modelo matemático proposto para determinar as estimativas da densidade do solo foi desenvolvido com base em dados experimentais representados pelas três características do solo: índice de cone, umidade e argila. Utilizando os dados experimentais os modelos foram identificados por meio de um algoritmo neuro-fuzzy, em função da resistência à penetração, teor de água e textura do solo, onde se pode analisar a densidade do solo para os distintos valores das variáveis de entradas...(Resumo completo, clicar acesso eletrônico abaixo) / Abstract: The present work aims to develop a intelligent system using fuzzy theory in order to estimate approximate values for the soil density taking in account direct measurements (in loco) disregarding laboratorial essays and, consequently, to identify the compactation of the soil through those estimated values. The soil density is one of the main parameters used to identify the soil compactation level, and it is also related to other parameters such as resistance to the soil penetration, water content and soil texture. Three soil parameters were considered for the development of this work: resistance to the soil penetration represented by the cone index (in kPa), the water content given by the soil humidity (percentage, %), and the texture given by the quantity of clay present in the soil (percentage, %). Also, prepared soils were considered (plough step, grid, disk harrow, and others) as well as non prepared soils (no kind of soil preparation or direct planted soil). According to the percentage of clay in the soil, they were classified as soil type I (clay content less than 30%), soil type II (clay content between 30% and 50%), soil type III (clay content higher than 50%) for the case of non prepared soil. For the case of prepared soil it was considered only soils type I (clay content less than 30%) and type III (clay content higher than 50%). The mathematical model considered to estimate the soil density was developed on the basis of given experimental data having the three soil characteristics: Cone index, humidity and clay content. Using the experimental data the models were identified by means of a neuro-fuzzy algorithm in function of the resistance to the penetration, water content and soil texture, through which one can analyze the soil density for different values of the model entrance variables. The experimental data and the estimated ones by the model...(Complete abstract click electronic access below) / Orientador: José Ângelo Cagnon / Coorientador: Ivan Nunes da Silva / Banca: Paulo José Amaral Serni / Banca: José Alfredo Covolan Ulson / Banca: Diogenes Pereira Gonzaga / Banca: Messias Meneguette Junior / Doutor

Esta dissertação testa e compara dois tipos de modelagem para previsão de uma mesma série temporal. Foi observada uma série temporal de distribuição de energia elétrica e, como estudo de caso, optou-se pela região metropolitana do Estado da Bahia. Foram testadas as combinações de três variáveis exógenas em cada modelo: a quantidade de clientes ligados na rede de distribuição de energia elétrica, a temperatura ambiente e a precipitação de chuvas. O modelo linear de previsão de séries temporais utilizado foi um SARIMAX. A modelagem de inteligência computacional utilizada para a previsão da série temporal foi um sistema de Inferência Fuzzy. Na busca de um melhor desempenho, foram feitos testes de quais variáveis exógenas melhor influenciam no comportamento da energia distribuída em cada modelo. Segundo a avaliação dos testes, o sistema Fuzzy de previsão foi o que obteve o menor erro. Porém dentre os menores erros, os resultados dos testes também indicaram diferentes variáveis exógenas para cada modelo de previsão. / This dissertation tests and compares two types of predicting models to the same time series. A time series of electricity distribution was observed and, as a case study, were opted for the metropolitan region of Bahia State. Three exogenous variables were tested in each model: the number of customers connected to the electricity distribution network, the temperature and the precipitation of rain. The linear model time series forecasting used was a SARIMAX. The modelling of computational intelligence used to predict the time series was a Fuzzy Inference System. For better performance, in each model was tested all the exogenous variables to fit the influence in the energy distributed. According to the evaluation of the tests, the Fuzzy forecasting system presented the lowest error. But among the smallest errors, the results of the tests also indicated different exogenous variables for each forecast model.