Abstract

Background: The double-end H-bridge inverters can realize independent control of the stator voltage in each phase and have flexible advantages of modulation and fault tolerance; thus, they are more suitable for multi-phase fault-tolerant motor drives. However, due to the increase of voltage vectors and the coupling between the electromagnetic and non-electromagnetic quantities, the normal control strategies for traditional three-phase motor drives do not work anymore. Objective: This paper aimed to propose a simplified model predictive current control strategy based on the vector space decomposition (VSD) and the vectors’ gradual simplification for the three-level six-phase H-bridge inverters. Methods: Firstly, the 729 physical variables were decomposed and mapped onto the fundamental αβ subspace, harmonic xy subspace, and the zero-sequence o1o2 subspace based on the VSD. Then, to eliminate the influence of the harmonic and zero-sequence components on the control performance and make an easy digital implementation, vectors’ simplification has been proposed based on the in-depth analysis of the relationship between the voltage vectors mapped onto different subspaces and vectors’ stratification. With the simplification method, the number of voltage vectors was simplified from 729 to 12, and then the selected voltage vectors were used in the rolling optimization of the model predictive current control (MPCC) to choose the optimal one. Finally, sufficient experiments were carried out including static and dynamic conditions, different modulation index and power factor, etc., to verify the feasibility of the proposed strategy. Results: The simulation and experimental results show that with the simplified MPCC strategy, both the static and dynamic performances are relatively good, and the THDs of the phase current under different modulations and power factors are relatively low. Conclusion: The proposed MPCC algorithm for the three-level six-phase H-bridge inverters has shown obvious improvement in solving the control problems of multi-vectors and complex redundancy issues.

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