INFORMAÇÕES SOBRE DIREITOS AUTORAIS

As obras disponibilizadas nesta Biblioteca Digital foram publicadas sob expressa autorização dos respectivos autores, em conformidade com a Lei 9610/98.

A consulta aos textos, permitida por seus respectivos autores, é livre, bem como a impressão de trechos ou de um exemplar completo exclusivamente para uso próprio. Não são permitidas a impressão e a reprodução de obras completas com qualquer outra finalidade que não o uso próprio de quem imprime.

A reprodução de pequenos trechos, na forma de citações em trabalhos de terceiros que não o próprio autor do texto consultado,é permitida, na medida justificada para a compreeensão da citação e mediante a informação, junto à citação, do nome do autor do texto original, bem como da fonte da pesquisa.

A violação de direitos autorais é passível de sanções civis e penais.
Coleção Digital

Título: IDENTIFICATION SYSTEM OF FAULTS IN TRANSMISSION LINES BASED ON NEURAL NETWORKS
Instituição: PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO - PUC-RIO
Autor(es):
Número do Conteúdo: 3509
Catalogação:  20/05/2003 Idioma(s):  PORTUGUESE - BRAZIL

Tipo:  TEXT Subtipo:
Natureza:  SCHOLARLY PUBLICATION
When a kind of fault occurs in a Transmission Line, its exact location is essential for a fast reclosing of the Electric System. Methods that use voltages and currents from only one terminal contain simplifications that can to cause mistakes. This paper presents an investigation about application of Artificial Neural Network (ANN) obtaining a new way of identification for the type of the short circuit and its location, using data obtained only in one terminal. The work consists on the following 4 main parts: bibliographical study of Neural Networks area; simulations of faults in order to obtain of patterns; definition and implementation of Neural Networks models for identification and location of the fault; and studies of cases. In the bibliographical study step on ANN, it was verified that the topologies for the more usual nets are Feed- Forward,that can have one or more layers of Processor Elements (PE), being the nets with multiple layers the most complete configuration. For the net training, the more used learning algorithm is Back Propagation. Product of this bibliographical study presents in this work a summary about ANN. In the faults simulations in order to obtain the training patterns and test, it was used an automatic system that, through the combination of several parameters of the electric system, generates different fault situations. This system uses as base the program Alternative Transient Program - ATP. In this work the electric system is represented by a Transmission Line of 345 KV, with equivalent sources representing a real system of Furnas Centrais Elétricas. All the voltages and currents signs used are represented by fasors of 60 Hz, obtained from Discret Fourier Transformer (DFT). The ANN models for identification and fault location were implemented with subroutines of neural network of the program MATLAB version 6.0, represented by Multi Layer Perceptron, with algorithm Back Propagation with tax of adaptive learning and the term momentum fixed. Only one model of ANN identifies which phases (A, B, C and T) are involved, classifying the fault type, that can be: Singlephase; Phase-to-Phase; Double Phase-to-Ground or Three-phase. For the fault location, they were defined 4 architectures of ANN, one for each type of fault. The activation of each topology of ANN for location is defined depending on of the fault type classified in the identification model with ANN. In the stage of cases study the representation of each model of ANN was tested using cases of tests in other fault situations, different from the training groups. The ANN of fault identification was evaluated for situations of faults involving other Transmission Line, with different voltage levels. The results of 4 ANNs of fault location were compared with the obtained results using the traditional method, so much for the simulated cases as for some real situations of fault. The use of ANNs for the identification and the fault location has shown quite efficient for the analyzed cases, proving the applicability of the neural networks in that problem.