This paper proposes a novel energy management strategy for microgrids, integrating a Fuzzy-Tuned Generalized Predictive Controller (Fuzzy-GPC) with dynamic fault detection, load prioritization, and weather-adaptive regulation. The controller dynamically adjusts key parameters –predictive horizon \(N_p\) and regularization parameter \(\lambda\) – in real-time, responding to system conditions such as battery state of charge (SOC), fault severity, and solar irradiance fluctuations. A multi-layer simulation framework is developed, employing a master-slave inverter configuration and categorizing loads into critical, deferrable, and non-critical types. Three disturbance scenarios are simulated: high-frequency voltage harmonics, under-frequency, and over-frequency grid deviations. The adaptive controller not only ensures steady-state voltage stability and power balance but also optimizes energy storage management and load-shedding decisions to preserve essential services. Quantitative results demonstrate significant improvements in disturbance rejection ( \(35\%\) faster recovery), fault-tolerant operation ( \(98\%\) detection accuracy), and energy utilization efficiency ( \(22\%\) reduction in storage losses). Using fuzzy logic, the controller achieves smooth parameter adaptation without abrupt adjustments, ensuring the best closed-loop robustness under severe fault conditions. Detection is performed using hybrid indicators based on energy variation, spectral content, and frequency deviation, achieving fast and reliable fault isolation. The proposed control strategy validates its ability to operate autonomously in unpredictable and constrained environments, highlighting its suitability for next-generation intelligent microgrids that require resilience, adaptability, and operational precision.
