Fault location in transmission line using self-organizing neural network
R. Salat , Stanisław Osowski
AbstractThe paper presents the application of self-organizing neural network for the location of the fault in transmission line and estimation of the parameter of the faulty element. The location of fault is done on the basis of the measurement of some node voltages of the line and appropriate preprocessing to enhance the differences between different faults. The hybrid neural network is used to solve the problem. The self-organizing layer of this network is used as the classifier. The output postprocessing MLP structure realizes the association of the place of fault and its parameter with the measured set of node voltages. The results of computer experiments are given in the paper and discussed
|Book||5th International Conference on Signal Processing Proceedings, 2000. WCCC-ICSP 2000, vol. 3, 2000|
|Keywords in English||Circuit faults, Distributed parameter circuits, Electrical resistance measurement, fault location, hybrid neural network, impedance, Intelligent networks, multilayer perceptron, multilayer perceptrons, neural networks, node voltage measurement, output postprocessing MLP structure, parameter estimation, pattern classification, power engineering computing, preprocessing, self-organising feature maps, self-organizing layer, self-organizing neural network, transmission line, Transmission line measurements, transmission lines, transmission line theory, Voltage|
|Publication indicators||= 2; = 4.0|
|Citation count*||4 (2013-01-30)|
* presented citation count is obtained through Internet information analysis and it is close to the number calculated by the Publish or Perish system.