M. Tech. Electrical Engineering. / This dissertation presents an effective method to determine current/voltage harmonic emission levels caused by various devices in the network. The method is implemented based on the existing power monitoring devices installed in power generation plants. The Digital Fault Recorders (DFRs) are used in this work as the monitoring devices. Due to the advancement of data processing and communication capabilities of such devices, a large amount of information generated by DFRs is concentrated in the utilitys head office. This data is archived for permanent storage and analysis. The presented work aims to assist the power utilities in harmonic invigilation problems based on DFRs.

Electric power systems have been in existence for over a century. Electric power transmission line systems play an important role in carrying electrical power to customers everywhere. The number of transmission lines in power systems is increasing as global demand for power has increased. Parallel transmission lines are widely used in the modern transmission system for higher reliability. The parallel lines method has economic and environmental advantages over single circuit. A fault that occurs on a power transmission line will cause long outage time if the fault location is not located as quickly as possible. The faster the fault location is found, the sooner the system can be restored and outage time can be reduced. The main focus of this research is to develop a new accurate fault location algorithm for parallel transmission lines to identify the fault location for long double-circuit transmission lines, taking into consideration mutual coupling impedance, mutual coupling admittance, and shunt capacitance of the line. In this research, the equivalent PI circuit based on a distributed parameter line model for positive, negative, and zero sequence networks have been constructed for system analysis during the fault. The new method uses only the voltage and current from one end of parallel lines to calculate the fault distance. This research approaches the problem by derivation all equations from positive sequence, negative sequence, and zero sequence network by using KVL and KCL. Then, the fault location is obtained by solving these equations. EMTP has been utilized to generate fault cases under various fault conditions with different fault locations, fault types and fault resistances. Then the algorithm is evaluated using the simulated data. The results have shown that the developed algorithm can achieve highly accurate estimates and is promising for practical applications.

This research develops a fault diagnosis method for complex systems in the presence of uncertainties and possibility of multiple solutions. Fault diagnosis is a challenging problem because data used in diagnosis contain random errors and often systematic errors as well. Furthermore, fault diagnosis is basically an inverse problem so that it inherits unfavorable characteristics of inverse problems: The existence and uniqueness of an inverse solution are not guaranteed and the solution may be unstable. The weighted least squares method and its variations are traditionally used for solving inverse problems. However, the existing algorithms often fail to identify multiple solutions if they are present. In addition, the existing algorithms are not capable of selecting variables systematically so that they generally use the full model in which may contain unnecessary variables as well as necessary variables. Ignoring this model uncertainty often gives rise to, so called, the smearing effect in solutions, because of which unnecessary variables are overestimated and necessary variables are underestimated. The proposed method solves the inverse problem using Bayesian inference. An engineering system can be parameterized using state variables. The probability of each state variable is inferred from observations made on the system. A bias in an observation is treated as a variable, and the probability of the bias variable is inferred as well. To take the uncertainty of model structure into account, multiple Bayesian models are created with various combinations of the state variables and the bias variables. The results from all models are averaged according to how likely each model is. Gibbs sampling is used for approximating updated probabilities. The method is demonstrated for two applications: the status matching of a turbojet engine and the fault diagnosis of an industrial gas turbine. In the status matching application only physical faults in the components of a turbojet engine are considered whereas in the fault diagnosis application sensor biases are considered as well as physical faults. The proposed method is tested in various faulty conditions using simulated measurements. Results show that the proposed method identifies physical faults and sensor biases simultaneously. It is also demonstrated that multiple solutions can be identified. Overall, there is a clear improvement in ability to identify correct solutions over the full model that contains all state and bias variables.

This dissertation presents a model-based reasoning architecture with a two fold purpose: to detect and classify component faults from observable system behavior, and to generate fault propagation models so as to make a more accurate estimation of current operational risks. It incorporates a novel approach to system level diagnostics by addressing the need to reason about low-level inaccessible components from observable high-level system behavior. In the field of complex system maintenance it can be invaluable as an aid to human operators. The first step is the compilation of the database of functional descriptions and associated fault-specific features for each of the system components. The system is then analyzed to extract structural information, which, in addition to the functional database, is used to create the structural and functional models. A fault-symptom matrix is constructed from the functional model and the features database. The fault threshold levels for these symptoms are founded on the nominal baseline data. Based on the fault-symptom matrix and these thresholds, a diagnostic decision tree is formulated in order to intelligently query about the system health. For each faulty candidate, a fault propagation tree is generated from the structural model. Finally, the overall system health status report includes both the faulty components and the associated at risk components, as predicted by the fault propagation model.

Thesis (Ph. D.)--Missouri University of Science and Technology, 2008. / Vita. The entire thesis text is included in file. Title from title screen of thesis/dissertation PDF file (viewed May 27, 2008) Includes bibliographical references.

Thesis (M.App.Sc.) - Carleton University, 2007. / Includes bibliographical references (p. 123-128). Also available in electronic format on the Internet.

Fundação Araucária, CNPq / O motor de indução trifásico ocupa uma posição de destaque na produção de força eletromotriz e isso o torna vastamente utilizado em aplicações industriais. Consequentemente, também fica submetido às condições de funcionamento e manutenção das máquinas como um todo, bem como das falhas que os mesmos estão sujeitos. Assim, este trabalho propõe um método alternativo aos tradicionais para detecção de falhas em rolamentos de motores de indução trifásicos ligados diretamente a rede elétrica. Os objetivos consistem na utilização de uma abordagem neural capaz de classificar a existência de falha de rolamento com um alto percentual de acerto. Analisando para isto, no domínio do tempo, um semiciclo das tensões de alimentação e das correntes de estator dos motor em estudo. A proposta é validada através de ensaios experimentais num computador e de forma on-line embarcada num DSP. Como conseqüência do trabalho tem-se a criação de um banco de dados de falhas, com mais de mil ensaios envolvendo as principais falhas encontradas em motores de indução trifásicos. Estes ensaios são realizados contemplando as condições de desbalanço de tensão de alimentação e com várias situações de carga mecânica no eixo da máquina. / The three phase induction motor occupies a prominent position in the production of electromotive force and this makes it widely used in industrial applications. Consequently, it is also subjected to the conditions of operation and maintenance of the machines as a whole, as well as faults which they are subject. Thus, this paper proposes an alternative method to traditional in fault detection in bearing of induction motors connected directly to the power grid. The objectives consist in using a neural approach able to classify the existence of bearing fault with a high percentage of correct. Analyzing for this, in the time domain, one half cycle of the voltages and currents of stator the motor in study. The proposal is validated through experimental tests on a computer and monitoring on-line embedded in a DSP. As a result, the work has the creation of a database of failure, with more than a thousand trials involving the main flaws found in three phase induction motors. These tests are performed considering the conditions of voltage supply unbalanced and with several situations of mechanical load on the machine shaft.

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / Fundação Araucária de Apoio ao Desenvolvimento Científico e Tecnológico do Paraná / O motor de indução trifásico do tipo gaiola de esquilo é a principal máquina de conversão eletromecânica devido a sua robustez e facilidade de manutenção, sendo indispensável nos processos produtivos industriais. Por sua grande importância, pesquisas na área de identificação de falhas são realizadas constantemente com o objetivo de diminuir as taxas de manutenções corretivas e permitir uma manutenção programada, diminuindo perdas no processo produtivo, decorrentes de paradas indesejadas. Com esse propósito, o presente trabalho propõe o estudo e desenvolvimento de uma metodologia alternativa que utiliza a Evolução Diferencial para identificação de falhas de curto-circuito de estator em motores de indução conectados diretamente à rede elétrica, por meio dos sinais de tensão e corrente aquisitados no domínio do tempo. O algoritmo de Evolução Diferencial é utilizado para estimar os parâmetros elétricos do motor de indução, a partir do modelo do circuito elétrico equivalente e a identificação da falha ocorre a partir do cálculo da variação percentual da indutância de magnetização estimada com o motor sem falhas. A base de dados utilizada para o trabalho foi obtida por meio de experimentos laboratoriais realizados com dois motores diferentes de 1 CV e um motor de 2 CV, sob condições de variação de conjugado, tensões equilibradas e desequilibradas. / The squirrel cage three-phase induction motor is the main electromechanical conversion machine due to its robustness and easy maintenance, therefore it is indispensable in the industrial production processes. Due to its great importance, surveys related to its fault identification are conducted constantly, in order to reduce the corrective maintenance rates and allow a scheduled maintenance, reducing the losses in the production process, due to unexpected stops. With this purpose, this work proposes the study and development of an alternative methodology based on Differential Evolution algorithm to identify stator short-circuit failures in induction motors connected directly on the electric grid, through its voltage and current signals acquired in the time domain. This Differential Evolution algorithm is used to estimate the induction motor electric parameters based on its equivalent electric circuit model and the fault identification occurs in result of the calculation of the estimated magnetization inductance percentage variation considering a healthy motor. The database used for this work was obtained through laboratory experiments performed with two different types of 1 CV motor and a 2 CV motor under different conditions of torque variation and unbalanced voltages.

É notável que o Sistema Elétrico de Potência (SEP) está constantemente exposto a adversidades ocasionadas por forças da natureza ou desgastes naturais de seus componentes. A localização com certa exatidão de uma falta no Sistema de Transmissão de Energia Elétrica (STEE), ocasionada por uma destas adversidades, contribui para a confiabilidade e segurança da operação do SEP. Neste contexto, este trabalho apresenta um método computacional de Localização de Faltas (LDF), desenvolvido em ambiente MATLAB®, baseado na Impedância Aparente (IA), utilizando dados de apenas um terminal, sendo o equacionamento desenvolvido em componentes simétricas. Este método tem como dados de entrada as componentes fundamentais de tensão e corrente do terminal local e a Potência de Curto Circuito (SCC) do sistema equivalente conectado ao terminal remoto. Para validar o método proposto foram realizadas diversas simulações para todos os tipos de faltas shunt, contemplando inúmeras condições destas, utilizando para isso, o programa computacional ATP-EMTP, alimentado com dados de parâmetros reais de um sistema de transmissão. Objetivando analisar a eficiência deste método, realizou-se a comparação de seus resultados com os resultados obtidos por um método estado-da-arte. A partir de análises e comparações, os resultados alcançados pertencentes ao método de LDF proposto, apresentam-se promissores para a LDF em sistemas de transmissão. / Electric Power Systems (EPS) are constantly exposed to adversities caused by forces of nature and components lifetime. The precise fault location in the Transmission System (TEPS), caused by these adversities, contributes to the reliability and safety the operation of the EPS. In this context, this work presents a computational method for fault location (FL), developed in MATLAB ® environment, based on the Apparent Impedance (AI) of the transmission line. The proposed method uses only one terminal data and is developed in symmetrical components equations. This method has as input data the fundamental components of voltage and current of the local terminal and the short circuit power (SCP) of the equivalent system connected to the remote terminal. The proposed method is validated through several simulations for all fault types, covering many fault conditions, making use of the software ATP-EMTP, fed with data from the actual parameters of a real life transmission system. Aiming to analyze the efficiency of this method, a comparison was made against the an state-of-the-art method. From analysis and comparison of the results obtained, is observed that the proposed scheme is capable to obtain precise fault location estimations and presents as promising method for the FL in transmission systems.

É notável que o Sistema Elétrico de Potência (SEP) está constantemente exposto a adversidades ocasionadas por forças da natureza ou desgastes naturais de seus componentes. A localização com certa exatidão de uma falta no Sistema de Transmissão de Energia Elétrica (STEE), ocasionada por uma destas adversidades, contribui para a confiabilidade e segurança da operação do SEP. Neste contexto, este trabalho apresenta um método computacional de Localização de Faltas (LDF), desenvolvido em ambiente MATLAB®, baseado na Impedância Aparente (IA), utilizando dados de apenas um terminal, sendo o equacionamento desenvolvido em componentes simétricas. Este método tem como dados de entrada as componentes fundamentais de tensão e corrente do terminal local e a Potência de Curto Circuito (SCC) do sistema equivalente conectado ao terminal remoto. Para validar o método proposto foram realizadas diversas simulações para todos os tipos de faltas shunt, contemplando inúmeras condições destas, utilizando para isso, o programa computacional ATP-EMTP, alimentado com dados de parâmetros reais de um sistema de transmissão. Objetivando analisar a eficiência deste método, realizou-se a comparação de seus resultados com os resultados obtidos por um método estado-da-arte. A partir de análises e comparações, os resultados alcançados pertencentes ao método de LDF proposto, apresentam-se promissores para a LDF em sistemas de transmissão. / Electric Power Systems (EPS) are constantly exposed to adversities caused by forces of nature and components lifetime. The precise fault location in the Transmission System (TEPS), caused by these adversities, contributes to the reliability and safety the operation of the EPS. In this context, this work presents a computational method for fault location (FL), developed in MATLAB ® environment, based on the Apparent Impedance (AI) of the transmission line. The proposed method uses only one terminal data and is developed in symmetrical components equations. This method has as input data the fundamental components of voltage and current of the local terminal and the short circuit power (SCP) of the equivalent system connected to the remote terminal. The proposed method is validated through several simulations for all fault types, covering many fault conditions, making use of the software ATP-EMTP, fed with data from the actual parameters of a real life transmission system. Aiming to analyze the efficiency of this method, a comparison was made against the an state-of-the-art method. From analysis and comparison of the results obtained, is observed that the proposed scheme is capable to obtain precise fault location estimations and presents as promising method for the FL in transmission systems.