High Impedance Fault Detection Based on HS-Transform and Decision Tree Techniques

This paper presents a high impedance fault (HIF) detection scheme based on the Hubbard-Stanovich (HS) transform and decision tree (DT). The HS-transform is used to extract features from the fault signal. The extracted features are used as input to train the DT classifier. Different cases are studied including HIF, load switching, capacitor switching and normal current. The proposed method is tested using a 22 kV practical network and implemented on the MATLAB/Simulink platform. The classification analysis was performed using a data science platform called WEKA. The presented results show that the proposed scheme is able to distinguish HIF from other power system operational cases. The proposed method showed good accuracy results.