Optimized fault detection and control for enhanced reliability and efficiency in DC microgrids

This paper introduces a comprehensive framework for fault detection and control in DC microgrids (DCMGs) integrating diverse energy sources. A resistance-based fault detection scheme is proposed to address intermittent DC link faults, enabling efficient operation without complete system shutdown. Perturb and Observe (P&O) techniques are employed for PV and wind power tracking, while proportional-integral (PI) controllers manage fuel cell (FC) and battery energy storage systems (BESS). Fuzzy logic controllers (FLCs) demonstrate superior performance over traditional PI controllers in mitigating voltage and current (V-I) fluctuations. To optimize DC-link V-I levels, a genetic algorithm-tuned PI controller (GA-PIC) and evolution-inspired PI controller are utilized. The proposed method is validated using Opal-RT simulations under various scenarios, demonstrating improved performance over un-optimized configurations. The key achievement of this research is a validated, optimized control and protection scheme that significantly enhances the stability and reliability of DCMGs under fault conditions. Specifically, the work develops a distributed fault detection and control method to improve protection and address stability and power quality in DCMGs. It also presents a GA-based PI-optimized controller for DCMGs with FC and battery storage, and an optimized controller integrating FLCs and GA-tuned PI-Cs to reduce V-I fluctuations. Furthermore, an integrated DC protection scheme is implemented, demonstrating enhanced fault detection speed and accuracy compared to individual schemes. The effectiveness of the proposed GA-PI-C is validated through Opal RT real-time simulations, confirming the efficacy of FLCs in dynamic system responses and contributing to more robust and reliable DCMG operation.