High Impedance Fault Detection in Electrical Power Feeder by Wavelet and GNN

The distribution feeder faults need to be detected and isolated in a reliable and accurate manner, for maintaining the efficient and reliable operation of distribution electrical power systems. A number of techniques are available for detecting and classifying the fault. However, the results are not satisfactory in case of high impedance fault (HIF) occurs on distribution feeder due to very low value of fault current. Keeping in view of aforesaid situation, a new approach based on generalized neural network (GNN) and wavelet transform is presented here for HIF detection. Wavelet transform is used to obtained the information from the measured faulty current in terms of standard deviation of wavelet coefficients. The obtained features are then used as an input to the GNN model for the detection of HIF on a given distribution feeder. The values obtained from GNN algorithm are compared with ANN and well established mathematical models and are found more accurate. All the calculations are done in Simulink/MATLAB