TY - JOUR
T1 - Elimination of Harmonic Currents Using a Reference Voltage Vector Based-Model Predictive Control for a Six-Phase PMSM Motor
AU - Luo, Yixiao
AU - Liu, Chunhua
PY - 2019/7
Y1 - 2019/7
N2 - This paper proposes a novel dead-beat current control (DBCC) based model predictive control for an asymmetrical six-phase permanent magnet synchronous machine (PMSM). First, the solution of DBCC is adopted to obtain the expected reference voltage vector (RVV). Then, two groups of virtual vectors, in the total number of 24 with different magnitudes, are defined for the sake of current harmonics suppression. Subsequently, two in-phase virtual vectors closest to the RVV are selected as the prediction vectors. The next step is to define a cost function which is composed of the error between the RVV and the available prediction vectors. Then, the selected two virtual vectors are evaluated and the one that minimizes the cost function will be applied in the next instant. So, only two prediction vectors need to be evaluated and the computation burden is highly alleviated. The weighting factor involved in predictive torque control is avoided. In addition, to achieve the readily implementation with standard PWM switching sequence, 18 virtual vectors are artfully replaced by their equivalent virtual vectors. Finally, the proposed method is comparatively studied and compared with the conventional model predictive current control method. Experimental results are offered to confirm the effectiveness of the proposed method.
AB - This paper proposes a novel dead-beat current control (DBCC) based model predictive control for an asymmetrical six-phase permanent magnet synchronous machine (PMSM). First, the solution of DBCC is adopted to obtain the expected reference voltage vector (RVV). Then, two groups of virtual vectors, in the total number of 24 with different magnitudes, are defined for the sake of current harmonics suppression. Subsequently, two in-phase virtual vectors closest to the RVV are selected as the prediction vectors. The next step is to define a cost function which is composed of the error between the RVV and the available prediction vectors. Then, the selected two virtual vectors are evaluated and the one that minimizes the cost function will be applied in the next instant. So, only two prediction vectors need to be evaluated and the computation burden is highly alleviated. The weighting factor involved in predictive torque control is avoided. In addition, to achieve the readily implementation with standard PWM switching sequence, 18 virtual vectors are artfully replaced by their equivalent virtual vectors. Finally, the proposed method is comparatively studied and compared with the conventional model predictive current control method. Experimental results are offered to confirm the effectiveness of the proposed method.
KW - Cost function
KW - current harmonics
KW - Deadbeat current control
KW - Harmonic analysis
KW - model predictive control
KW - multiphase machine
KW - PMSM motor
KW - Predictive control
KW - Predictive models
KW - reference voltage vector
KW - six-phase machine
KW - Stators
KW - Switches
KW - Torque
UR - http://www.scopus.com/inward/record.url?scp=85054641427&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85054641427&origin=recordpage
U2 - 10.1109/TPEL.2018.2874893
DO - 10.1109/TPEL.2018.2874893
M3 - RGC 21 - Publication in refereed journal
VL - 34
SP - 6960
EP - 6972
JO - IEEE Transactions on Power Electronics
JF - IEEE Transactions on Power Electronics
SN - 0885-8993
IS - 7
ER -