In modern power transmission systems, the double-circuit line structure is increasingly adopted. However, due to the mutual coupling between the parallel lines it is quite challenging to design accurate fault location algorithms. Moreover, the widely used series compensator and its protective device introduce harmonics and non-linearities to the transmission lines, which make fault location more difficult. To tackle these problems, this dissertation is committed to developing advanced fault location methods for double-circuit and series-compensated transmission lines. Algorithms utilizing sparse measurements for pinpointing the location of short-circuit faults on double-circuit lines are proposed. By decomposing the original network into three sequence networks, the bus impedance matrix for each network with the addition of the fictitious fault bus can be formulated. It is a function of the unknown fault location. With the augmented bus impedance matrices the sequence voltage change during the fault at any bus can be expressed in terms of the corresponding sequence fault current and the transfer impedance between the fault bus and the measured bus. Resorting to VCR the superimposed sequence current at any branch can be expressed with respect to the pertaining sequence fault current and transfer impedance terms. Obeying boundary conditions of different fault types, four different classes of fault location algorithms utilizing either voltage phasors, or phase voltage magnitudes, or current phasors, or phase current magnitudes are derived. The distinguishing charactristic of the proposed method is that the data measurements need not stem from the faulted section itself. Quite satisfactory results have been obtained using EMTP simulation studies. A fault location algorithm for series-compensated transmission lines that employs two-terminal unsynchronized voltage and current measurements has been implemented. For the distinct cases that the fault occurs either on the left or on the right side of the series compensator, two subroutines are developed. In additon, the procedure to identify the correct fault location estimate is described in this work. Simulation studies carried out with Matlab SimPowerSystems show that the fault location results are very accurate.

Submitted by Renata Lopes (renatasil82@gmail.com) on 2016-01-08T10:36:58Z No. of bitstreams: 1 wanderantunesgasparvalente.pdf: 4197156 bytes, checksum: 5b667869c3bb237e570559ddf4cbb30d (MD5) / Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2016-01-25T16:56:26Z (GMT) No. of bitstreams: 1 wanderantunesgasparvalente.pdf: 4197156 bytes, checksum: 5b667869c3bb237e570559ddf4cbb30d (MD5) / Made available in DSpace on 2016-01-25T16:56:26Z (GMT). No. of bitstreams: 1 wanderantunesgasparvalente.pdf: 4197156 bytes, checksum: 5b667869c3bb237e570559ddf4cbb30d (MD5) Previous issue date: 2015-02-09 / O presente trabalho fundamenta-se no método das segmentações geométricas sucessivas (MSGS) para a construção de uma rede neural artificial capaz de gerar tanto a topologia da rede quanto o peso dos neurônios sem a especificação de parâmetros iniciais. O MSGS permite identificar um conjunto de hiperplanos no espaço Rn que, quando combinados adequadamente, podem separar duas ou mais classes de dados. Especificamente neste trabalho é empregado um aprimoramento ao MSGS com base em estimativas de densidade por kernel. Utilizando-se KDE, é possível encontrar novos hiperplanos de separação de forma mais consistente e, a partir daí, conduzir à classificação de dados com taxas de acerto superiores à técnica originalmente empregada. Neste trabalho, o MSGS aprimorado é empregado satisfatoriamente pela primeira vez para a identificação de padrões em sistemas de energia elétrica. O método foi ajustado para a classificação de faltas incipientes em transformadores de potência e os resultados apresentam índices de acerto superiores a trabalhos correlatos. O MSGS aprimorado também foi adaptado para classificar e localizar faltas inter-circuitos em linhas áreas de transmissão em circuito duplo, obtendo resultados positivos em comparação com a literatura científica. / This work is based on the method of successive geometric segmentations (SGSM) for the construction of an artificial neural network capable of generating both the network topology as the weight of neurons without specifying initial parameters. The MSGS allows to identify a set of hyperplanes in the Rn space that when properly combined, can separate two or more data classes. Specifically in this work is used an improvement to SGSM based on kernel density estimates (KDE). Using KDE, it is possible to find new hyperplanes of separation more consistently and, from there, lead to data classification with accuracy rates higher than originally technique. In this paper, the improved SGSM is first used satisfactorily to identify patterns in electrical power systems. The method has been adjusted to the classification of incipient faults in power transformers and the results have achieved rates above related work. The improved SGSM has also been adapted to classify and locate inter-circuit faults on double circuit overhead transmission lines with positive results compared with the scientific literature.