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Transmission lines of any voltage level are subject to faults. To speed up repairs and restoration of power, it is important to know where the fault is located. A fault location algorithm’s result is influenced by a series of modeling equations, setting parameters and system factors reflected in voltage and current inputs. The factors mentioned are subject to sources of uncertainty including measurement and signal processing errors, setting errors and incomplete modeling of a system under fault conditions. These errors have affected the accuracy of the distance to fault calculation. Accurate fault location reduces operating costs by avoiding lengthy and expensive patrols. Accurate fault location speeds up repairs and restoration of lines, ultimately reducing revenue loss caused by outages. In this thesis, we have reviewed the fault location algorithms and also how the uncertainty affects the results of fault location. Sensitivity analysis is able to analyze how the variation in the output of the fault location algorithms can be allocated to the variation of uncertain factors. In this research, we have used global sensitivity analysis to determine the most contributed uncertain factors and also the interaction of the uncertain factors. We have chosen Analysis of Variance (ANOVA) decomposition as our global sensitivity analysis. ANOVA decomposition shows us the insight of the fault location, such as relations between uncertain factors of the fault location. Quasi regression technique has also been used to approximate a function. In this research, the transmission line fault location system is fitted into the ANOVA decomposition using quasi regression. From the approximate function, we are able to get the variance of the sensitivity of fault location to uncertain factors using Monte Carlo method. In this research, we have designed novel methodology to test the fault location algorithms and compare the fault location algorithms. In practice, such analysis not only helps in selecting the optimal locator for a specific application, it also helps in the calibration process. / Thesis (M.Eng.Sc.) -- University of Adelaide, School of Electrical and Electronic Engineering, 2009

Fundação de Amparo a Pesquisa no Estado do Rio Grande do Sul / The wireless sensor networks have made great progress in the last decade and are increasingly present in several fields like security and monitoring of persons, animals or items, medicine, military area and many others that, due to technological developments, have become viable. These networks are formed by sensor nodes that have extremely limited resources, such as processing and data storage capacity, data transmission rate and energy available for operation. Thus, in networks with hundreds or even thousands of nodes, it is unfeasible to locate each one of them with global positioning devices, because those will considerably increase the cost and power consumption. As localization knowledge by the nodes is required in applications such as tracking, monitoring and environmental data collection, location algorithms were created to cheapen and/or improve this task. Thus, this master thesis presents the development of a low complexity iterative multilateration method, since most location algorithms uses some kind of multilateration. To prove this new method efficiency, a simple simulator based on the Matlab software was created in order to evaluate, in terms of location error, accuracy and robustness in a scenario with random arrangement of the reference nodes, log-normal propagation model with shadowing and received signal strength distance estimation. Under these conditions, the presented multilateration method presents inconsiderable loss of accuracy in comparison to the maximum likelihood method and also a low number of iterations is required. In this way was possible to increase the location algorithms accuracy without this entailing an increase in complexity and power consumption. / As redes de sensores sem fio tiveram um grande progresso na última década e estão cada vez mais presentes em diversos campos como a segurança e monitoramento de pessoas, animais ou itens, medicina, área militar entre muitas outras que, devido à evolução tecnológica, tornaram-se viáveis. As principais características dos nós sensores, denominados nodos, constituintes destas redes são as de possuírem recursos extremamente limitados, sendo estes a capacidade de processamento e de armazenamento de dados, taxa de transmissão de dados e energia disponível para operação. Deste modo, em redes com centenas ou até milhares de nodos, seria inviável que todos estes possuíssem dispositivos de posicionamento global para se localizarem, pois estes acarretariam num considerável aumento de custo e consumo de potência. Como o conhecimento de sua localização, por parte dos nodos, é necessário em aplicações como rastreamento, monitoramento e coleta de dados ambientais, foram criados algoritmos de localização com a função de viabilizar e/ou tornar mais precisa esta tarefa. Assim, neste trabalho é apresentado o desenvolvimento de um método de multilateração iterativo de baixa complexidade para o uso em algoritmos de localização. Para provar que este novo método é eficiente, foi criado um simulador simples baseado no software Matlab com o intuito de avaliar, em termos de erro na localização, a precisão e robustez deste em cenários com disposição aleatória dos nodos de referência, modelo de propagação log-normal com sombreamento e estimação das distâncias através da potência do sinal recebido. Nestas condições, o método de multilateração desenvolvido apresentou uma perda de precisão desconsiderável em relação ao de máxima verossimilhança e com um baixo número de iterações. Desta forma foi possível aumentar a precisão dos algoritmos de localização sem que isto acarrete num aumento de complexidade e de consumo de potência.

The rapid growth of the electric power system has in recent decades resulted in an increase of the number of transmission lines and total power outage in Norway. The challenge of a fast growing electrical grid has also resulted in huge increases of overhead lines and their total length. These lines are experiencing faults due to various reasons that cause major disruptions and operating costs of the transmission system operator (TSO). Thus, it’s important that the location of faults is either known or can be estimated with reasonably high accuracy. This allows the grid owner to save money and time for inspection and repair, as well as to provide a better service due to the possibility of faster restoration of power supply and avoiding blackouts.  Fault detection and classification on transmission lines are important tasks in order to protect the electrical power system. In recent years, the power system has become more complicated under competitive and deregulated environments and a fast fault location technique is needed to maintain security and supply in the grid. This thesis compares and evaluates different methods for classification of fault type and calculation of conventional one-side and two-side based fault location algorithms for distance to fault estimation.  Different algorithm has been implemented, tested and verified to create a greater understanding of determinants facts that affect distance to faults algorithm’s accuracy.  Implemented algorithm has been tested on the data generated from a number of simulations in Simulink for a verification process in implemented algorithms accuracy. Two types of fault cases have also been simulated and compared for known distance to fault estimation.