Three-Vector-Based Low Complexity Model Predictive Control for Soft Open Point

Soft open point (SOP) can improve the flexibility and reliability of power supplies; thus, they are widely used in distribution network systems. Traditional single-vector model predictive control (SV-MPC) can quickly and flexibly control the power and current at both ports of the SOP. However, SV-MPC can only select one voltage vector in a sampling time, producing large current ripples, and power fluctuations. In order to solve the above problems, this paper proposes a three-vector-based low complexity model predictive control (TV-MPC). In the proposed control method, two effective voltage vectors and one zero voltage vector are selected in a sampling time. For the two-port SOP, methods are given to judge the sectors on both sides and select the voltage vectors. Furthermore, the calculation method of the distribution time is proposed as well. Finally, the effectiveness of the proposed method is verified by steady-state and dynamic-state simulation results compared with the SV-MPC.

Space Vector Modulation (SVM)-Based Common-Mode Current (CMC) Reduction Method of H8 Inverter for Permanent Magnet Synchronous Motor (PMSM) Drives

This paper proposes a space vector modulation (SVM)-based common-mode (CM) currents reduction method of an H8 inverter for permanent magnet synchronous motor (PMSM) drives. There are power quality issues in the PMSM drive systems, such as current distortions and CM electromagnetic interference (EMI) due to the fast-switching operation of the inverter. These issues are related to CM voltage (CMV) and CM current (CMC). Although several studies have been conducted to reduce the CMV and CMC, some CMV variations and CMCs are still generated in the real implementation. Unlike conventional methods, the proposed method selects the voltage vectors with similar CMV levels and arranges them considering the series-connected switch operation of the H8 inverter in a voltage vector modulation sequence. At a low modulation index (MI), the proposed method completely restricts the CMV variations into six times. At high MI, the proposed method synthesizes the reference voltage vector differently, depending on the position of the reference vector, to reduce both current distortions and CMCs. The validity of the proposed method is verified through simulations and experimental results.

A Computationally Efficient Model Predictive Control of Six-Phase Induction Machines Based on Deadbeat Control

Model predictive current control (MPCC) has recently become a viable alternative for multiphase electric drives, because it easily exploits the inherent advantages of multi-phase machines. However, the prediction in MPCC requires a high number of voltage vectors (VVs), being therefore computationally demanding. In that regard, this paper proposes a computationally efficient MPCC of an asymmetrical six-phase induction machine drive (ASIMD) that reduces the number of VVs used for prediction. By using the characteristics of the deadbeat control (DB), the proposed method obtains a reference voltage vector (RVV), where its position will serve as a reference and integrates the MPCC scheme. Only 4 out of 13 predictions are needed to determine the best VV, dramatically reducing the algorithm computation. Experimental results for a six-phase case study compare the standard MPCC with the suggested method, confirming that deadbeat model predictive current control (DB-MPCC) shows that the execution time can be shortened by 48.8% and successfully improve the motor performance and efficiency.

Multi-Virtual-Vector Model Predictive Current Control for Dual Three-Phase PMSM

This paper proposes a multi-virtual-vector model predictive control (MPC) for a dual three-phase permanent magnet synchronous machine (DTP-PMSM), which aims to regulate the currents in both fundamental and harmonic subspace. Apart from the fundamental α-β subspace, the harmonic subspace termed x-y is decoupled in multiphase PMSM according to vector space decomposition (VSD). Hence, the regulation of x-y currents is of paramount importance to improve control performance. In order to take into account both fundamental and harmonic subspaces, this paper presents a multi-virtual-vector model predictive control (MVV-MPC) scheme to significantly improve the steady performance without affecting the dynamic response. In this way, virtual vectors are pre-synthesized to eliminate the components in the x-y subspace and then a vector with adjustable phase and amplitude is composed of two effective virtual vectors and a zero vector. As a result, an enhanced current tracking ability is acquired due to the expanded output range of the voltage vector. Lastly, both simulation and experimental results are given to confirm the feasibility of the proposed MVV-MPC for DTP-PMSM.