Enhanced Protection Algorithm for Zonal DC Microgrids

Abstract

Conventional protection methods are constrained for effectively identifying and classifying faults within dc microgrid setups because of the integration of diverse power electronic-based generators and dc loads. For dc microgrids configured in zonal topology with bidirectional power flow, designing effective protection becomes even more complex. Ensuring reliable power supply to consumers while preventing the unnecessary disconnection of renewable resources underscores the importance of selectivity in protection schemes. Addressing this challenge requires advanced, intelligent fault detection schemes. This article proposes a high-speed fault identification algorithm built upon the current derivative signals at both line terminals. Upon fault detection, the type of fault is determined by the current derivative characteristics on the positive and negative poles of either side of a line. MATLAB/Simulink simulations of a zonal dc microgrid with various generating units and loads validate the developed algorithm, considering a range of fault conditions, including internal, external, high-resistance, and noisy conditions. Simulation results demonstrate the ability of the technique to segregate internal and external faults and accurately classify them. Furthermore, the suggested scheme is evaluated on a hardware testbed, confirming its efficacy in real-world dc microgrid applications.