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An accurate fault classification algorithm for double end fed parallel transmission lines based on application of artificial neural networks is presented in this paper. The proposed method uses the voltage and current available at only the local end of line. This method is virtually independent of the effects of remote end infeed and is insensitive to the variation of fault inception angle and fault location. The Simulation results show that phase-to-phase faults can be correctly detected, classified and located within one cycle after the inception of fault. Large number of faults simulations using MATLABÂ®7.01 have demonstrated the accuracy and effectiveness of the proposed algorithm. The proposed scheme allows the protection engineers to increase the reach setting i.e. greater portion of line length can be protected as compared to conventional techniques. The technique neither requires a communication link to retrieve the remote end data nor zero sequence current compensation for healthy phases.