Secondary restoration of islanded alternating current microgrids under a neural inverse optimal control

A novel control strategy for secondary restoration of an islanded microgrid is proposed, focusing on restoring the frequency and voltage magnitude of an inverter-based distributed generator. A neural inverse optimal controller is integrated into the secondary control layer with a higher-order neural network trained using an extended Kalman filter (EKF). A practical and effective design strategy is provided. Unlike traditional approaches that rely on accurate mathematical models, the combination of inverse optimal control and neural networks does not require an accurate model and is more relevant to real-world engineering scenarios. To enhance the secondary control, the EKF optimization parameters are used in conjunction with the inverse optimal control to achieve more accurate and effective repair results. The synergistic effect improves control performance and ensures superior secondary recovery. Real-time validation is performed through rigorous simulations on the StarSim hardware-in-the-loop experimental platform.