An ANN based approach to improve the speed of a differential equation based distance relaying algorithm

This paper presents an artificial neural network (ANN) based approach to improve the speed of a differential equation based distance relaying algorithm. As the differential equation used for the transmission line protection is valid only at low frequencies, the distance relaying algorithm requires a lowpass filter, removing frequency components higher than those for relaying. However, the lowpass filter causes the time delay of the components for relaying. Thus, the calculated resistances and reactances do not converge directly to the fault distance even after data window occupies post fault data. Faults with the same fault inception angle have similar shapes of impedance loci. If an ANN is trained with the shape of various impedance loci for fault distances and fault inception angles, it can predict the fault distance with some values of calculated resistances and reactances before they converge to the fault distance. Therefore, the ANN can improve the speed of the distance relaying algorithm without affecting its accuracy. Moreover, the proposed approach can speed up more when a higher sampling rate is employed. The proposed approach was tested in three rates of 24, 48 and 96 samples/cycle (s/c) in a 345 (kV) transmission system and compared with the conventional distance relaying algorithm without ANNs from the speed and accuracy viewpoints. As a result, the approach can improve the speed of the relaying algorithm