Bilayered fault detection and classification scheme for low-voltage DC microgrid with weighted KNN and decision tree

Abstract

DC microgrids (DCMGs) are gaining popularity mainly in shipboards and isolated power systems for their modest control and higher efficiency. However, the newer conception and less literature are forcing the operators to take up tough challenges for smoother operation of the overall system. Protection areas of DCMGs are still immature because of the inexperience of handling DC faults and less availability of DC circuit breakers (DCCBs) in outsized range. In this work, the fault detection and classification issue in low-voltage-DC-microgrid (LVDCMG) has been solved with a bilayered machine learning scheme based on weighted K-nearest neighbor (WKNN) and decision tree (DT). WKNN accurately detects the fault in the line, and DT classifies the fault as PG or PP for further corrective measures. The developed technique has been implemented in an LVDCMG with PV and battery energy sources. Voltage and current measurements from the network have been used as training samples to the classifier models. It has been noticed that the proposed bilayered protection scheme is giving 100% detection accuracy. The outcome of the proposed structure seems to be promising and has been compared with some of the existing machine learning (ML)-based fault detection techniques for microgrids (MGs).