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The conventional model predictive power control suffers from heavy computational burden and large power ripple. To address the problem, this paper proposes a simplified three-vector-based model predictive direct power control, which can greatly optimize the control set. Firstly, a switching table is designed to exclude the redundant virtual voltage vectors (VVs). Only half of the candidate VVs are retained. Then, the effect of each VV on power is analyzed in detail. The deviation of reactive power is used as the evidence to further simplify the control set. In this way, the prediction behavior is reduced from 12 to 2, which effectively reduces the computational burden. In addition, the concept of power deadbeat is employed to determine the duty cycles of the two virtual VVs and one zero VV. Thus, the power ripples and current harmonics can be considerably suppressed. With this proposed method, the calculation complexity is reduced, and the steady-state performance is improved. The simulation and experimental results are given to verify the proposed method. [ABSTRACT FROM AUTHOR]

The main challenges for Finite Set-model Predictive Torque and Flux Control (MPTC) include huge calculation amount, large torque ripple, and the selection of weighting factor. To deal with these challenges and achieve the fastest torque response, a novel MPTC strategy with virtual voltage vector-based pattern is proposed in this paper. In each control period, a desired voltage vector is computed based on the maximum torque variation rate. The virtual desired voltage vector is combined by using basic voltage vectors with the minimum switching times. The switching pattern between two adjacent control periods is optimized by using a cost function in realtime. Compared with the traditional MPTC approach, the novel strategy has multifaceted improved performances: faster torque response, less calculation amount, lower switching frequency, reduced torque ripple, higher efficiency and no weighting factor. The proposed method is contrasted with traditional MPTC method and a well-known generalized multiple-vector-based MPC method via experiments. [ABSTRACT FROM AUTHOR]