Fast detection and isolation of faults in a DC microgrid is of particular importance. Fast tripping protection (i) increases the lifetime of power electronics (PE) switches by avoiding high fault current magnitudes and (ii) enhances the controllability of PE converters. This thesis proposes a traveling wave (TW) based scheme for fast tripping protection of DC microgrids. The proposed scheme utilizes a discrete wavelet transform (DWT) to calculate the high-frequency components of DC fault currents. Multiresolution analysis (MRA) using DWT is utilized to detect TW components for different frequency ranges. The Parseval energy calculated from the MRA coefficients are then used to demonstrate a quantitative relationship between that energy and the fault current signal energy. The calculated Parseval energy values are used to train a Support Vector Machine classifier to identify the fault type and a Gaussian Process regression engine to estimate the fault location on the DC cables. The proposed approach is verified by simulating two microgrid test systems in PSCAD/EMTDC.
microgrid, fault detection, traveling waves, Parseval energy, MRA, DWT
Level of Degree
First Committee Member (Chair)
Second Committee Member
Third Committee Member
Montoya, Rudy. "DC Microgrid Fault Detection Using Multiresolution Analysis of Traveling Waves." (2022). https://digitalrepository.unm.edu/me_etds/189