Faulted phase selection on double circuit transmission line using wavelet transform and neural network

Modern numerical relays often incorporate the logic for combined single and three-phase auto-reclosing scheme; single phase to earth faults initiate single-phase tripping and reclosure, and all the other faults initiate three-phase tripping and reclosure. Accurate faulted phase selection is required for such a scheme. This paper presents a novel scheme for detection and classification of faults on double circuit transmission line. The proposed approach uses combination of wavelet transform and neural network, to solve the problem. While wavelet transform is a powerful mathematical tool which can be employed as a fast and very effective means of analyzing power system transient signals, artificial neural network has a ability to classify non-linear relationship between measured signals by identifying different patterns of the associated signals. The proposed algorithm consists of time-frequency analysis of fault generated transients using wavelet transform, followed by pattern recognition using artificial neural network to identify the faulted phase. MATLAB/Simulink software is used to generate fault signals and verify the correctness of the algorithm. The adaptive discrimination scheme is tested by simulating different types of fault and varying fault resistance, fault location and fault inception time, on a given power system model. The simulation results show that the proposed phase selector scheme is able to identify faulted phase on the double circuit transmission line rapidly and correctly.