A DFT-ED based approach for detection and classification of faults in electric power transmission networks

Abstract

Abstract This paper proposes a fast and reliable fault detection and classification scheme for electric power transmission networks using the estimated Euclidean distance between successive samples of actuating signal. In the proposed method, magnitudes of fundamental components of three-phase current phasors estimated through discrete Fourier transform are used as actuating signal. Performance of the proposed method is tested for numerous fault cases (symmetrical and unsymmetrical faults with varying fault inception angle, fault type, fault location and fault resistance) and non-fault cases (switching on/off of large loads and capacitor banks) by generating data through MATLAB/Simulink software on a two-bus test power system. Results clearly shows that using the proposed technique a fast and reliable fault detection and classification task can be accomplished.