A novel fault features extraction scheme for power transmission line fault diagnosis

This paper proposes a novel transmission line fault detection and classification scheme, based on a single-end measurements using time shift invariant property of a sinusoidal waveform. Various types of faults at different locations, fault resistance and fault inception angles on a 400 kV - 361.65 km power system transmission line are investigated. The scheme is used to extract distinctive fault features over 1 over 8 of a cycle and 1 over 2 of a cycle data windows. The performance of the feature extraction scheme was tested on a machine intelligent platform WEKA by using two types of classifiers, Fuzzy logic reasoning (FLR), and support vector machine (SVM). The result shows that, the scheme can classify all types of short circuit faults on a doubly fed transmission lines. Accuracy between 95.95% and 100% is achieved.