Resilient Control of Converter-Based Microgrids Enhanced by Deep Learning

Converter-based power plants will become the primary source of electrical energy in the future. These types of power plants need to be enhanced in terms of their resilience as well as increasing their share. This article proposes an improved combined control method aimed at enhancing resilience. The method focuses on solving grid-connected converter challenges under unbalanced disturbed grid voltage conditions. These conditions are more commonly observed in converter-based microgrids due to structural and operational issues, such as weak grids, un-transposed lines, single-phase loads, etc. The current references in the proposed method are calculated to minimize their dependency on measured grid voltage and system parameters as much as possible. Also, the proposed integrated method is designed to handle severe load changes and symmetrical voltage sags. Moreover, a resilient control system should be capable of interacting with the system and learning new information from events in accordance with the resilience definitions. Thus, the proposed method incorporates reinforcement learning as an added value to enhance the level of resilience in a more comprehensive and effective manner. Simulation and experimental results demonstrate that the proposed method reduces current THD by about 35%, decreases switching losses by 50%, and eliminates overshoots under dynamic conditions compared to conventional methods. These findings confirm the effectiveness and superiority of the proposed method in improving the resilience and performance of GCCs.