Fault Detection and Classification in Hybrid Shipboard Microgrids

Shipboard microgrids (SMGs) have played a significant role in the concept of all-electric ships (AES), with increasing power demand from loads during operation. The system stability and reliability are pivotal for supporting various aspects of SMG operation. Faults on bus or transmission line in SMGs, even minor faults, can result in catastrophic consequences such as an abrupt disruption in the current, voltage, or frequency signals. Detection and classification of faults are essential and important for system restoration. To maintain system supply reliability, a wavelet transform (WT) approach is proposed in this paper to extract the faulty signal information by performing different levels of signal decomposition. The daubechies wavelet “db4” is used and implemented with various signal level decompositions to obtain the signal detailed coefficient and compare it with adaptive threshold values to detect and classify faulty phases. The fault location was carried out by obtaining the local fault information on transmission line or bus of SMGs. MATLAB Simulation results of a practical hybrid SMG are presented to ensure the performance of proposed method for fault detection and classification on transmission line.