Research Index

A novel approach combining digital twin technology with bio-inspired reinforcement learning to create self-healing protection systems for microgrids. The system learns from both simulated and real-world scenarios to adaptively optimize protection settings while maintaining grid stability, drawing inspiration from biological immune systems' ability to learn and adapt to new threats.

A novel approach combining quantum annealing, bio-inspired algorithms, and digital twin technology to optimize microgrid formation and restoration during natural disasters. The system uses real-time sensor data and quantum-classical hybrid computing to dynamically reconfigure microgrids while maintaining frequency stability and minimizing energy losses.

A novel framework that applies LLM reasoning calibration techniques to optimize microgrid control decisions in real-time. By combining SEAL's thought-steering approach with power systems domain knowledge, the system can generate more efficient and reliable control strategies while reducing computational overhead.