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The Scientific World Journal
Volume 2014, Article ID 176463, 13 pages
Research Article

A Modular Neural Network Scheme Applied to Fault Diagnosis in Electric Power Systems

1Área de Control Peninsular, CFE, Instituto Tecnológico de Mérida, Departamento de Eléctrica, C10 No. 312-A Fraccionamiento Gonzalo Guerrero, 97118 Mérida, YUC, Mexico
2Departamento de Ingeniería de Sistemas y Automática, Universitat Politècnica de València, C. Vera 14, 46022 Valencia, Spain

Received 2 May 2014; Revised 19 August 2014; Accepted 25 August 2014; Published 17 September 2014

Academic Editor: Hongbo Zhao

Copyright © 2014 Agustín Flores et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This work proposes a new method for fault diagnosis in electric power systems based on neural modules. With this method the diagnosis is performed by assigning a neural module for each type of component comprising the electric power system, whether it is a transmission line, bus or transformer. The neural modules for buses and transformers comprise two diagnostic levels which take into consideration the logic states of switches and relays, both internal and back-up, with the exception of the neural module for transmission lines which also has a third diagnostic level which takes into account the oscillograms of fault voltages and currents as well as the frequency spectrums of these oscillograms, in order to verify if the transmission line had in fact been subjected to a fault. One important advantage of the diagnostic system proposed is that its implementation does not require the use of a network configurator for the system; it does not depend on the size of the power network nor does it require retraining of the neural modules if the power network increases in size, making its application possible to only one component, a specific area, or the whole context of the power system.