Fault Classification and Ground detection using Support Vector Machine

This paper presents a new approach for the fault classification and ground detection in transmission line in large power system networks using support vector machine (SVM). The proposed method uses post fault current and voltage samples for 1/4th cycle (5 samples) from the inception of the fault as inputs to the SVM. SVM-1 is trained with current and voltage samples to provide faulty phase involved and SVM-2 is trained with peak of the ground current to provide the involvement of the ground in the fault process. The SVMs are trained with Gaussian kernel with different parameter values to get the most optimized classifier. The proposed method converges very fast and thus provides fast and accurate protection scheme for distance relaying