Microgrids are a viable approach for improving energy resiliency and sustainability. Current microgrid systems frequently suffer from inadequate fault identification and compensation techniques, resulting in operational inefficiencies and grid instability. Conventional control strategies may not effectively manage changing environmental conditions and variations in load, requiring a more adaptable and intelligent approach. The paper presents a sophisticated method that combines Fuzzy Logic Controllers (FLCs) with Synchrophasor Technology (ST) to tackle these challenges. This work aims to develop and execute a thorough solution for microgrids' fault identification, compensation, and power management. The paper proposed a MATLAB/Simulink model that includes a Solar Photovoltaic (SPV) system with an FLC-based Maximum Power Point Tracking (MPPT), a Diesel Generator (DG), a Battery Energy Storage System (BESS) with an FLC-based Battery Management System (BMS), a Voltage Source Inverter (VSI), and a Unified Power Flow Controller (UPFC) with a FLC. Phasor Measurement Units (PMUs) equipped with ST are used to monitor and identify the anomalies in the system. The analysis of the simulation data provides evidence of the efficacy of the suggested approach. The SPV system demonstrates strong performance in producing voltage and current, successfully adjusting to different irradiance levels. The MPPT algorithm based on FLC guarantees the highest power efficiency. Additionally, the BESS and UPFC, controlled by FLCs, enhance grid stability and power quality. By integrating with synchrophasor-enabled PMUs, the microgrid may achieve accurate fault detection, and thus, the operator can take corrective actions to guarantee dependable operation even under challenging circumstances. Furthermore, the reliability and efficiency of the proposed system were confirmed using a Real-Time Digital Simulator (RTDS) using RSCAD with PMUs in real-world environments. By employing hardware-based PMUs, the system can accurately monitor grid interactions in real-time, enabling prompt detection of disruptions and proactive deployment of control measures. The RTDS results showcased the system's ability to maintain grid stability and efficiently manage faults, as evidenced by the precise current and voltage magnitudes measurements and their phase angles, frequency, and Rate of Change of Frequency (ROCOF) during fault scenarios.
