Computationally-efficient hierarchical optimal controller for grid-tied cascaded multilevel inverters

This paper proposes a hierarchical optimal active and reactive power control while minimizing the switching events for three-phase grid-tied cascaded multilevel inverters (CMI) for battery energy storage applications. Model predictive control (MPC) is known as a potential approach for multi-objective control schemes in single-loop manner for power electronics interfaces. However, MPC schemes are suffering from high computational burden. Furthermore, one of the main challenges while designing MPC is tuning of the cost function weight factors in multi-objective control schemes. The weight factors design and tuning directly affect the performance and robustness of the MPC. The proposed high performance hierarchical multi-objective optimal controller doesn't have the aforementioned limitations of the MPC. The proposed control scheme utilizes a dynamic look-up matrix as an internal optimizer tool. The redundant switching states are cycled to equalize the power drawn from the independent battery energy storage sources while achieving a minimum energy control. The theoretical analysis and case studies verify robustness, fast dynamic response, and computational efficiency of the proposed multi-criteria optimal controller.