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Fault classification and location of power transmission lines using artificial neural network

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3 Author(s)
M. Tarafdar Hagh ; Department of Electrical and Computer Engineering, University of Tabriz, Iran ; K. Razi ; H. Taghizadeh

This paper describes the application of an artificial neural network (ANN) based algorithm with modular structure to the fault classification and location of a single-circuit high voltage transmission line. Different fault types containing single-phase to ground, two-phase, two-phase to ground and three-phase are considered. The variation of fault resistance is considered, too. The operation of proposed strategy is not dependent on fault inception angle (FIA). A new classification method is proposed for decreasing of training time and dimensions of ANN. Using the proposed method, high accuracy of fault classification is achieved. Fundamental component of pre-fault and post-fault positive sequence component of currents and voltages of three phases have been used as inputs to proposed ANN. The output of the ANN is the estimated fault location. A two machine power system model is simulated by PSCAD/EMTDC to obtain the mentioned voltages and currents values. The neural network toolbox of MATLAB is used for training and testing of ANN.

Published in:

2007 International Power Engineering Conference (IPEC 2007)

Date of Conference:

3-6 Dec. 2007