Weightless Model Predictive Torque Control of Induction Motor with Simplified Candidate Voltage Vectors Set

被引:1
作者
Li, Yaohua [1 ]
Chen, Guixin [1 ]
Liu, Zikun [1 ]
Wang, Xiaoyu [1 ]
Liu, Dongmei [1 ]
Ren, Chao [1 ]
机构
[1] Changan Univ, Sch Automobile, Xian, Peoples R China
来源
6TH IEEE INTERNATIONAL CONFERENCE ON PREDICTIVE CONTROL OF ELECTRICAL DRIVES AND POWER ELECTRONICS (PRECEDE 2021) | 2021年
关键词
induction motor; model predictive torque control; deadbeat torque; simplified voltage vectors; weightless; extended voltage vectors;
D O I
10.1109/PRECEDE51386.2021.9680974
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
In order to reduce the torque ripple, flux ripple and calculation burden of the conventional model predictive torque control (MPTC) of induction motors (IM), and avoid the design of weight coefficients, a weightless MPTC method with simplified voltage vectors (VVs) based on deadbeat (DB) is proposed. The duty cycle of the VVs is calculated through DB method, and then the MPTC link is performed after VVs are corrected to select the optimal VV. Based on the DB-MPTC, the symmetrical distribution of the duty cycle with the VVs is used to reduce the number of non-zero VVs traversed by half. Secondly, the cost function in the algorithm only contains the flux term, and has almost no effect on the control performance, thus eliminating the weight coefficient and further simplifying the MPTC model. Finally, the influence of extending VVs on DB-MPTC is briefly studied. The experimental results prove that through the method, the torque ripple is improved by 64.92%, flux ripple is improved by 52.24%, current total harmonic distortion (THD) is optimized from 12.68% to 5.60%, the design of weight coefficient can be effectively avoided, and the calculation burden is also reduced by 48.61%, so the control and real-time performance is improved.
引用
收藏
页码:251 / 256
页数:6
相关论文
共 14 条
[1]   Optimized Design of Discrete Traction Induction Motor Model at Low-Switching Frequency [J].
Diao, Li-Jun ;
Sun, Da-Nan ;
Dong, Kan ;
Zhao, Lei-Ting ;
Liu, Zhi-Gang .
IEEE TRANSACTIONS ON POWER ELECTRONICS, 2013, 28 (10) :4803-4810
[2]   Optimal finite state predictive direct torque control without weighting factors for motor drive applications [J].
Ipoum-Ngome, Paul Gistain ;
Mon-Nzongo, Daniel L. ;
Song-Manguelle, Joseph ;
Flesch, Rodolfo C. C. ;
Jin, Tao .
IET POWER ELECTRONICS, 2019, 12 (06) :1434-1444
[3]  
Li Y., 2019, ELECT MACHINES CONTR, V46, P12
[4]  
Liu M., ELECT MACHINES CONTR, P20
[5]   Predictive Torque Control of Induction Machines Based on State-Space Models [J].
Miranda, Hernan ;
Cortes, Patricio ;
Yuz, Juan I. ;
Rodriguez, Jose .
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2009, 56 (06) :1916-1924
[6]   Multiobjective Fuzzy-Decision-Making Predictive Torque Control for an Induction Motor Drive [J].
Rojas, Christian A. ;
Rodriguez, Jose R. ;
Kouro, Samir ;
Villarroel, Felipe .
IEEE TRANSACTIONS ON POWER ELECTRONICS, 2017, 32 (08) :6245-6260
[7]   A New Predictive Direct Torque Control Method for Improving Both Steady-State and Transient-State Operations of the PMSM [J].
Vafaie, Mohammad Hossein ;
Dehkordi, Behzad Mirzaeian ;
Moallem, Payman ;
Kiyoumarsi, Arash .
IEEE TRANSACTIONS ON POWER ELECTRONICS, 2016, 31 (05) :3738-3753
[8]   Model Predictive Control for Power Converters and Drives: Advances and Trends [J].
Vazquez, Sergio ;
Rodriguez, Jose ;
Rivera, Marco ;
Franquelo, Leopoldo G. ;
Norambuena, Margarita .
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2017, 64 (02) :935-947
[9]  
Xia C., P CSEE, V36, P3045
[10]  
Zhang X., 2019, ELECT ENG, P19