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This paper presents a novel approach to fault detection, faulted phase selection, and direction estimation based on artificial neural networks (ANNs). The suggested approach uses the finite impulse response artificial neural network (FIRANN) with the same structure and parameters in each relaying location. Our main objective in this work is to find a fast relay design with a detection time not dependent on fault conditions (i.e., current transformer saturation, dynamic arcing faults, short-circuit level, and system topology) and that uses only unfiltered voltage and current samples at 2 kHz. The suggested relay, which we have named FIRANN-DSDST, is composed of a FIRANN together with post-processing elements. The FIRANN is trained globally using training patterns from more than one relaying position in order to be as general as possible. The FIRANN is trained using an improved training algorithm, which …