Abstract
A method for detecting and classifying transmission line faults using cross-correlation and k-Nearest Neighbor(k$-NN) has been presented in this article. A unique analogy between the cross-correlogram obtained from the sound phase and a faulty phase in an electric power system, defined here as the fault correlogram, and a normal electrocardiogram (ECG) of human heart has been validated in the proposed work. The proposed method uses synthetic fault data within half cycle of pre-fault and half cycle of post-fault to detect and classify the different faults under varying fault parameters. EMTP/ATP software has been used as the platform to carry out simulation of the power system network followed by signal processing in MATLAB.