Nonexcessive-Delta V and Low Complexity Model Predictive Control Based on Finite-State Machine for Three-Level Three-Phase Inverters

For three-level three-phase inverters, a novel finite-state machine-based model predictive control (FSM-MPC) is proposed. The state transition diagram of FSM is regarded as the operation guideline to avoid excessive voltage jumps (Delta V). No more than five voltage vectors (VVs) are selected as FSM-MPC's candidates VVs, based on the VV reference and the previous optimal VV. And the cost function is simplified in terms of time duration. Thus, evaluating a candidate VV requires a very low computation cost. Without losing control performance, the proposed FSM-MPC's execution time is decreased to 50% of theMPC algorithm, which enumerates all basic VVs to get global optimal VV and performance. Compared with the existing simplified algorithms, the proposed FSM-MPC makes current harmonics lower, and its average switching frequency is 33% less, which means much less switching loss. Furthermore, the proposed algorithm is robust when the electric-circuit parameters are mismatched in the control system. Experimental results are provided to validate the advantages of the proposed algorithm.

Automated summary

This study proposes a novel finite-state machine-based model predictive control method for three-level three-phase inverters. The state transition diagram of the finite-state machine is used as the operation guideline to avoid excessive voltage jumps. The algorithm selects no more than five voltage vectors as candidate vectors, based on the reference and previous optimal vector. By simplifying the cost function in terms of time duration, the computation cost for evaluating a candidate vector is significantly reduced. The proposed FSM-MPC algorithm achieves a 50% decrease in execution time compared to the MPC algorithm, while maintaining control performance.