M Karimi - Electrical and Robotics faculty, Shahrood University of Technology, Shahrood, Iran
M Banejad - Electrical and Robotics faculty, Shahrood University of Technology, Shahrood, Iran
H Hassanpour - IT and Computer faculty, Shahrood University of Technology, Shahrood, Iran
A Moeini - Electrical and Robotics faculty, Shahrood University of Technology, Shahrood, Iran

This paper present a new intelligent approach to identify fault types and phases. A fault classification method using self-organizing map (SOM) neural network (NN) is used to classify various patterns of associated voltages and currents of fault phenomena. First difference between this paper and pervious researches is proposing a novel classification criterion. In this paper is proposed to use symmetrical components and phasor futures of both voltage and current as criterion parameters. Second difference is application of SOMNN for classification purpose. Because of using novel effective criterion parameters, it is possible to use very simple NN such as SOM. Performance of the proposed method is evaluated on test power system. Simulation results shows that the proposed approach can be used as an effective tool for high speed relaying.

Fault Classification, Fault Voltage, FaultCurrent, Self-organizing Map Neural Network,Symmetrical Component