Os Sistemas de Distribuição de Energia Elétrica (SDEE) estão constantemente expostos à ocorrência de faltas, o que torna de primordial importância para as concessionárias que estas sejam localizadas com rapidez e precisão. Com isso, a degradação da confiabilidade do sistema e as perdas econômicas são minimizadas. Com este intuito, diversas pesquisas vêm sendo conduzidas nas últimas décadas tendo em vista o desenvolvimento de métodos computacionais para Localização de Faltas (LF) em SDEE. Embora as técnicas disponíveis atualmente sejam capazes de estimar a distância das faltas com relativa exatidão, algumas características intrínsecas aos SDEE ainda impõem limitações à LF em alimentadores radiais. Em geral, o aspecto econômico requer o uso de dados provenientes de um único terminal de medição. Ainda, a quantidade significativa de ramais laterais tipicamente presentes nos SDEE faz com que o problema das múltiplas estimativas da LF seja considerado como a principal limitação à efetividade das técnicas existentes na atualidade. Neste contexto, este trabalho apresenta uma metodologia baseada na análise dos transitórios de alta frequência gerados pelas faltas, a qual é capaz de fornecer uma estimativa única para a LF em SDEE ramificados a partir de medições somente no terminal local. O ramal em falta e a distância da falta em relação ao terminal de medição são determinados através da comparação entre as frequências características identificadas no espectro do transitório, e as frequências teóricas calculadas para os possíveis caminhos de propagação. Uma das principais contribuições deste trabalho consiste na formulação de um modelo detalhado das linhas de distribuição desequilibradas. A formulação proposta é baseada na modificação nas equações de Carson, considerando a dependência da frequência e a inclusão dos efeitos pelicular e da corrente de retorno pela terra para altas frequências. Como resultado, esta abordagem permite uma maior exatidão na determinação dos parâmetros modais que descrevem a propagação de transitórios em SDEE desequilibrados, eliminando as aproximações adotadas pela maioria das técnicas propostas na literatura atual. A técnica proposta foi avaliada considerando simulações de faltas nos alimentadores IEEE 13 e 34 barras através do ATP/EMTP. Os resultados apresentados incluem diversos cenários de faltas, bem como a comparação com um método de LF para SDEE considerado estado da arte atualmente. / Electric power distribution systems (EPDS) are continuously exposed to faults, therefore fast and accurate fault location is of paramount importance for utilities. Thus, degradation of system reliability and economic losses are minimized. In this sense, several studies have been conducted in the last decades aiming to the development of computational methods for Fault Localization (FL) in EPDS. Although the currently available techniques are able to estimate fault distance with relative accuracy, some intrinsic characteristics of EPDS still impose limitations to FL in radial feeders. In general, the economic aspect requires use of data from one-terminal measurements. Also, typical EPDS have a large number of branches, which makes the problem of multiple FL estimates the main limitation to the effectiveness of the existing techniques. In this context, this work presents a methodology based on the analysis of the high frequency transient generated by faults, which is able to provide a unique FL estimate in branched EPDS by using only one-terminal measurements. The faulted branch and the fault distance from the measurement terminal are determined by correlating the characteristic frequencies identified in the transient spectrum and theoretical frequencies calculated for the possible propagation paths. One of the main contributions of this work is the formulation of a detailed model of unbalanced distribution lines. The proposed formulation is based on the modification of Carson’s equations, considering frequency dependence and inclusion of skin effects and the ground current return at high frequencies. As a result, this approach allows a greater accuracy in determining the modal parameters that describe the transients’ propagation in unbalanced EPDS, thus eliminating the approximations adopted by most of the techniques proposed in the current literature. The proposed technique was evaluated considering fault simulations in the IEEE 13 and 34 nodes feeders through the ATP/EMTP. Results presented include several fault scenarios as well as the comparison with a FL method for SDEE currently considered as the state of the art.

Os afundamentos de tensão são distúrbios que afetam grandemente a qualidade da energia fornecida aos consumidores, se mostrando especialmente danosos para os consumidores industriais que dependem de tensões em níveis adequados para a continuidade de seus processos de fabricação. O desempenho das redes elétricas frente aos afundamentos de tensão é usualmente mensurado através de indicadores, que dentre outros propósitos, fornecem as informações necessárias ao planejamento de ações voltadas à mitigação dos efeitos dos afundamentos de tensão causados por faltas. Em geral, as regiões da rede vulneráveis a este tipo de distúrbio são identificadas através de medições em pontos de interesse ao longo da rede. No entanto, esta abordagem se mostra restritiva, pois abrange áreas pontuais e intervalos de tempo pouco representativos. Neste contexto, o presente trabalho propõe uma metodologia para estimar os indicadores de afundamentos de tensão, utilizando medições em um único terminal da rede. A técnica é baseada na determinação da localização das faltas para estimar as magnitudes dos afundamentos em todos os nós da rede. Estas informações são utilizadas para caracterizar o desempenho dos sistemas de distribuição em relação aos afundamentos de tensão que ocorrem em um dado período de estudo. A validação da metodologia foi efetuada a partir de simulações de faltas nos sistemas testes IEEE 13 nós e 34 nós. Os testes incluem a análise da sensibilidade da técnica frente a variações no carregamento da rede, considerando diversos cenários de faltas. / Voltage sags are disturbances that greatly affect the power quality supplied to customers and are especially damaging to industrial customers which rely on adequate voltage levels for the continuity of manufacturing processes. Electrical networks’ performance against voltage sags is usually measured by indices that among other purposes, provide the necessary information for planning actions aiming to mitigate the effects of voltage sags caused by faults. In general, areas vulnerable to this type of disturbance are identified by measurements at points of interest along the network. However, this approach is restrictive, since it covers punctual areas and reduced time intervals. In this context, this work proposes a methodology for estimating voltage sags indices using measurements at a single terminal. The technique is based on the fault location to estimate the magnitudes of voltage sags in all network nodes. This information is used to evaluate the performance of distribution systems in relation to the occurrence of voltage sags in a given period of study. The method’s validation was performed by fault simulations in the IEEE 13 node and 34 node test systems. The tests include a sensitivity analysis considering load variation and several fault scenarios.

Fault location reveals the exact information needed for utility crews to timely and promptly perform maintenance and system restoration. Therefore, accurate fault location is a key function in reducing outage time and enhancing power system reliability. Modern power systems are witnessing a trend of integrating more distributed generations (DG) into the grid. DG power outputs may be intermittent and can no longer be treated as constants in fault location method development. DG modeling is also difficult for fault location purpose. Moreover, most existing fault location methods are not applicable to simultaneous faults. To solve the challenges, this dissertation proposes three impedance-based fault location algorithms to pinpoint simultaneous faults for power transmission systems and distribution systems with high penetration of DGs. The proposed fault location algorithms utilize the voltage and/or current phasors that are captured by phasor measurement units. Bus impedance matrix technique is harnessed to establish the relationship between the measurements and unknown simultaneous fault locations. The distinct features of the proposed algorithms are that no fault types and fault resistances are needed to determine the fault locations. In particular, Type I and Type III algorithms do not need the information of source impedances and prefault measurements to locate the faults. Moreover, the effects of shunt capacitance are fully considered to improve fault location accuracy. The proposed algorithms for distribution systems are validated by evaluation studies using Matlab and Simulink SimPowerSystems on a 21 bus distribution system and the modified IEEE 34 node test system. Type II fault location algorithm for transmission systems is applicable to untransposed lines and is validated by simulation studies using EMTP on a 27 bus transmission system. Fault area identification method is proposed to reduce the number of line segments to be examined for fault location. In addition, an optimal fault location method that can identify possible bad measurement is proposed for enhanced fault location estimate. Evaluation studies show that the optimal fault location method is accurate and effective. The proposed algorithms can be integrated into the existing energy management system for enhanced fault management capability for power systems.

Nowadays power transmission networks are capable of delivering contracted power from any supplier to any consumer over a large geographic area under market control, and thus transmission lines are incorporated with FACTs series compensated devices to increase the power transfer capability with improvement to system integrity. Conventional fault location methods developed in the past many years are not suitable for FACTs transmission networks. The obvious reason is that FACTs devices in transmission networks introduce non-linearity in the system and hence linear fault detection methods are no longer valid. Therefore, it is still a matter of research to investigate developing new fault detection techniques to cater for modern transmission network configurations and solve implementation issues maintaining required accuracy. This PhD research work is based on developing an accurate and robust new fault location algorithm for series compensated high voltage transmission lines, considering many issues such as transmission line models, configurations with series compensation features. Building on the existing knowledge, a new algorithm has been developed for the estimation of fault location using the time domain approach. In this algorithm, instantaneous fault signals from the transmission line ends are measured and applied to the algorithm to calculate the distance to fault. The new algorithm was tested on two port transmission line model developed using EMTP/ATP software and measured fault data from the simulations are exported to the MATLAB space to run the algorithm. Broad range of faults has been simulated considering various fault cases to test the algorithm and statistical results obtained. It was observed that the accuracy of location of fault on series compensated transmission line using this algorithm is in the range from 99.7 % to 99.9% in 90% of fault cases. In addition, this algorithm was further improved considering many practical issues related to modern series compensated transmission lines (with TCSC var compensators) achieving similar accuracies in the estimation of fault location.

University of Macau / Faculty of Science and Technology / Department of Electromechanical Engineering

Imaging of current in internal conductors through magnetic field detection by magnetic force microscopy (MFM) is of growing interest in the analysis of integrated circuits (ICs). This thesis presents a systematic study of the MFM based mapping of current in model circuits by using force and force gradient techniques. In comparing these two techniques, force was found to have a much higher signal to noise ratio (from ~150 to ~580 times) than force gradient at large tip-sample distances considering the presence of thick overlayers in ICs. As a result, force will have better sensitivity and can therefore be used to detect much smaller minimum currents. We have achieved a sensitivity of ~0.64 µA per square-root Hertz in air and ~0.095 µA per squre-root Hertz in vacuum for force with a pinning field with a probe-circuit separation of 1.0 µm. We conclude that the force technique is superior for the application of MFM current imaging of buried conductors, albeit with reduced spatial resolution. Numerical modeling of the MFM images has shown that the simple point probe approximation is insufficient to model MFM images. An extended model, which considers realistic MFM probe geometries and the forces acting on the whole probe, has been shown to be necessary. Qualitative and quantitative comparisons of the experimental and simulation results with this model are in agreement to within experimental uncertainty. The comparisons suggested that the CoCr film thickness is not uniform on the probe, which was verified by scanning electron microscope cross-section images of the probes cut by a focused ion beam. Most notably, the CoCr film was 1.5 times thicker on the cantilever than on the tip. Based on the simulation and experimental results, we have devised a method to accurately locate the current path from MFM images with submicrometer uncertainty. The method was tested for different patterns of model conducting lines. It was shown to be a useful technique for fault location in IC failure analysis when current flows through the devices buried under overlayers and no topographic features are on the surface to provide clues about the positions of the devices. / October 2007

Optimum Sensor Localization/Selection in A Diagnostic/Prognostic Architecture Guangfan Zhang 107 Pages Directed by Dr. George J. Vachtsevanos This research addresses the problem of sensor localization/selection for fault diagnostic purposes in Prognostics and Health Management (PHM)/Condition-Based Maintenance (CBM) systems. The performance of PHM/CBM systems relies not only on the diagnostic/prognostic algorithms used, but also on the types, location, and number of sensors selected. Most of the research reported in the area of sensor localization/selection for fault diagnosis focuses on qualitative analysis and lacks a uniform figure of merit. Moreover, sensor localization/selection is mainly studied as an open-loop problem without considering the performance feedback from the on-line diagnostic/prognostic system. In this research, a novel approach for sensor localization/selection is proposed in an integrated diagnostic/prognostic architecture to achieve maximum diagnostic performance. First, a fault detectability metric is defined quantitatively. A novel graph-based approach, the Quantified-Directed Model, is called upon to model fault propagation in complex systems and an appropriate figure-of-merit is defined to maximize fault detectability and minimize the required number of sensors while achieving optimum performance. Secondly, the proposed sensor localization/selection strategy is integrated into a diagnostic/prognostic system architecture while exhibiting attributes of flexibility and scalability. Moreover, the performance is validated and verified in the integrated diagnostic/prognostic architecture, and the performance of the integrated diagnostic/prognostic architecture acts as useful feedback for further optimizing the sensors considered. The approach is tested and validated through a five-tank simulation system. This research has led to the following major contributions: ??generalized methodology for sensor localization/selection for fault diagnostic purposes. ??quantitative definition of fault detection ability of a sensor, a novel Quantified-Directed Model (QDG) method for fault propagation modeling purposes, and a generalized figure of merit to maximize fault detectability and minimize the required number of sensors while achieving optimum diagnostic performance at the system level. ??novel, integrated architecture for a diagnostic/prognostic system. ??lidation of the proposed sensor localization/selection approach in the integrated diagnostic/prognostic architecture.

This thesis presents an on-line particle-filtering-based framework for fault diagnosis and failure prognosis in nonlinear, non-Gaussian systems. The methodology assumes the definition of a set of fault indicators, which are appropriate for monitoring purposes, the availability of real-time process measurements, and the existence of empirical knowledge (or historical data) to characterize both nominal and abnormal operating conditions. The incorporation of particle-filtering (PF) techniques in the proposed scheme not only allows for the implementation of real time algorithms, but also provides a solid theoretical framework to handle the problem of fault detection and isolation (FDI), fault identification, and failure prognosis. Founded on the concept of sequential importance sampling (SIS) and Bayesian theory, PF approximates the conditional state probability distribution by a swarm of points called particles and a set of weights representing discrete probability masses. Particles can be easily generated and recursively updated in real time, given a nonlinear process dynamic model and a measurement model that relates the states of the system with the observed fault indicators. Two autonomous modules have been considered in this research. On one hand, the fault diagnosis module uses a hybrid state-space model of the plant and a particle-filtering algorithm to (1) calculate the probability of any given fault condition in real time, (2) estimate the probability density function (pdf) of the continuous-valued states in the monitored system, and (3) provide information about type I and type II detection errors, as well as other critical statistics. Among the advantages offered by this diagnosis approach is the fact that the pdf state estimate may be used as the initial condition in prognostic modules after a particular fault mode is isolated, hence allowing swift transitions between FDI and prognostic routines. The failure prognosis module, on the other hand, computes (in real time) the pdf of the remaining useful life (RUL) of the faulty subsystem using a particle-filtering-based algorithm. This algorithm consecutively updates the current state estimate for a nonlinear state-space model (with unknown time-varying parameters) and predicts the evolution in time of the fault indicator pdf. The outcome of the prognosis module provides information about the precision and accuracy of long-term predictions, RUL expectations, 95% confidence intervals, and other hypothesis tests for the failure condition under study. Finally, inner and outer correction loops (learning schemes) are used to periodically improve the parameters that characterize the performance of FDI and/or prognosis algorithms. Illustrative theoretical examples and data from a seeded fault test for a UH-60 planetary carrier plate are used to validate all proposed approaches. Contributions of this research include: (1) the establishment of a general methodology for real time FDI and failure prognosis in nonlinear processes with unknown model parameters, (2) the definition of appropriate procedures to generate dependable statistics about fault conditions, and (3) a description of specific ways to utilize information from real time measurements to improve the precision and accuracy of the predictions for the state probability density function (pdf).

As the popularity of building automation system (BAS) increases, there is an increasing need to understand/analyze the HVAC system behavior with the monitoring data. However, the current constraints prevent FDD technology from being widely accepted, which include: 1)Difficult to understand the diagnostic results; 2)FDD methods have strong system dependency and low adaptability; 3)The performance of FDD methods is still not satisfactory; 4)Lack of information. This thesis aims at removing the constraints, with a specific focus on air handling unit (AHU), which is one of the most common HVAC components in commercial buildings. To achieve the target, following work has been done in the thesis. On understanding the diagnostic results, a standard information structure including probability, criticality and risk is proposed. On improving method's adaptability, a low system dependency FDD method: rule augmented CUSUM method is developed and tested, another highly adaptable method: principal component analysis (PCA) method is implemented and tested. On improving the overall FDD performance (detection sensitivity and diagnostic accuracy), a hypothesis that using integrated approach to combine different FDD methods could improve the FDD performance is proposed, both deterministic and probabilistic integration approaches are implemented to verify this hypothesis. On understanding the value of information, the FDD results for a testing system under different information availability scenarios are compared. The results show that rule augmented CUSUM method is able to detect the abrupt faults and most incipient faults, therefore is a reliable method to use. The results also show that overall improvement of FDD method is possible using Bayesian integration approach, given accurate parameters (sensitivity and specificity), but not guaranteed with deterministic integration approach, although which is simpler to use. The study of information availability reveals that most of the faults can be detected in low and medium information availability scenario, moving further to high information availability scenario only slightly improves the diagnostic performance. The key message from this thesis to the community is that: using Bayesian approach to integrate high adaptable FDD methods and delivering the results in a probability context is an optimal solution to remove the current constraints and push FDD technology to a new position.

Thesis (Ph. D.)--University of Texas at Austin, 2000. / Vita. Includes bibliographical references (leaves 81-85). Available also in a digital version from Dissertation Abstracts.