Optimized Fault Detection and Control for Enhanced Reliability and Efficiency in DC Microgrids

Abstract This paper presents a comprehensive framework for fault detection, control, and operation within a DC microgrid (DCMG) incorporating diverse energy sources like wind, solar photovoltaic (PV), battery energy storage systems (BESS), utility grid, fuel cells (FC), and load. The DCMG faces challenges due to intermittent faults in the DC link and the necessity to distinguish between low and high fault levels. A resistance-based fault detection scheme is proposed to address these issues, enabling efficient fault detection without necessitating a complete shutdown of the DCMG. Perturb and Observe (P&O) techniques are employed for PV and wind power tracking, while proportional-integral (PI) controllers are utilized for FC and BESS control. In mitigating voltage and current (V-I) fluctuations, fuzzy logic controllers (FLCs) exhibit superior performance compared to traditional PI methods. For the favorable variation of the DC-link V-I level, the traditional PI controller is tuned with a genetic algorithm (GA-PIC) based optimization technique and evolution-inspired PI controller. Additionally, PI controllers undergo optimization via a genetic algorithm (GA), ensuring adherence to V-I limits. The proposed method to investigate fault responses is validated on test systems developed in the OPAL-RT simulator under different scenarios. It Demonstrates improvements over un-optimized counterparts with optimized configuration. This research advances DCMGs by improving their efficiency, stability, and performance.