Fault detection and classification in a distribution network integrated with distributed generators

This paper develops a methodology for application in distribution network fault detection and classification. The proposed methodology is based on wavelet energy spectrum entropy decomposition of disturbance waveforms to extract characteristic features by using level-4 db4 wavelet coefficients. Thus, few input features are required for the implementation. Different simulation scenarios encompassing various fault types at several locations with different load angles, fault resistances, fault inception angles, and load switching are applied to the IEEE 34 Node Test Feeder. In particular, the effects of system changes were investigated by integrating various Distributed Generators (DGs) into the distribution feeder. Extensive studies, verification, and analysis made from the application of this technique validate the approach. Comparison with statistical methods based on standard deviation and mean absolute deviation has shown that the method based on log energy entropy is very reliable, accurate, and robust.