Transformer-Enhanced Intelligent Microgrid Self-Healing: Integrating Large Language Models and Adaptive Optimization for Real-Time Fault Detection and Recovery

The rapid proliferation of renewable energy integration and escalating grid operational complexity have intensified demands for resilient self-healing mechanisms in modern power systems. Conventional approaches relying on static models and heuristic rules exhibit limitations in addressing dynamic fault propagation and multi-modal data fusion. This study proposes a Transformer-enhanced intelligent microgrid self-healing framework that synergizes large language models (LLMs) with adaptive optimization, achieving three key innovations: (1) A hierarchical attention mechanism incorporating grid impedance characteristics for spatiotemporal feature extraction, (2) Dynamic covariance estimation Kalman filtering with wavelet packet energy entropy thresholds (Daubechies-4 basis, 6-level decomposition), and (3) A grouping-stratified ant colony optimization algorithm featuring penalty-based pheromone updating. Validated on IEEE 33/100-node systems, our framework demonstrates 96.7% fault localization accuracy (23% improvement over STGCN) and 0.82-s protection delay, outperforming MILP-based methods by 37% in reconfiguration speed. The system maintains 98.4% self-healing success rate under cascading faults, resolving 89.3% of phase-to-ground faults within 500 ms through adaptive impedance matching. Field tests on 220 kV substations with 45% renewable penetration show 99.1% voltage stability (±5% deviation threshold) and 40% communication efficiency gains via compressed GOOSE message parsing. Comparative analysis reveals 12.6× faster convergence than conventional ACO in 1000-node networks, with 95.2% robustness against ±25% load fluctuations. These advancements provide a scalable solution for real-time fault recovery in renewable-dense grids, reducing outage duration by 63% in multi-agent simulations compared to centralized architectures.