Multiple-Vector Model Predictive Power Control for Grid-Tied Wind Turbine System With Enhanced Steady-State Control Performance

被引:168
作者
Zhang, Zhenbin [1 ,2 ]
Fang, Hui [3 ]
Gao, Feng [4 ]
Rodriguez, Jose [5 ]
Kennel, Ralph [2 ]
机构
[1] Huazhong Univ Sci & Technol, Sch Elect & Elect Engn, Wuhan 430074, Peoples R China
[2] Tech Univ Munich, Inst Elect Drive Syst & Power Elect, D-80333 Munich, Germany
[3] State Grid Chongqing Elect Power Res Inst, Chongqing 401121, Peoples R China
[4] Shandong Univ, Minist Educ, Key Lab Power Syst Intelligent Dispatch & Control, Jinan 250061, Peoples R China
[5] Univ Andres Bello, Fac Engn, Santiago 8370146, Chile
关键词
Direct model predictive control (DMPC); field programmable gate array (FPGA) digital control; gridtied active front end (AFE); performance-enhanced direct power control (DPC); time-optimal control; wind turbine systems with permanent-magnet synchronous generator (PMSG); CONVERTER;
D O I
10.1109/TIE.2017.2682000
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Direct model predictive control (DMPC) is a promising alternative for power electronics and electric drives. It takes the switching nonlinearity of the power converters and system constraints into consideration, without using an extra modulator. However, its oneswitching- vector-per-control-interval character leads to big ripples of the control variables. Therefore, with a similar sampling frequency, its steady-state performance is not satisfying. In this paper, we propose a multiple-vector direct model predictive power control (MV-DMPPC) concept for the grid-side power converter control of a back-to-back converter permanent-magnet synchronous generator wind turbine system, using a fully field programmable gate array based solution. The proposed control scheme is compared with the classical DMPPC and two recently reported DMPPC scheme with duty cycle optimizations. Both simulation and experimental tests validate that the control performances are evidently improved with the proposed MV-DMPPC solution.
引用
收藏
页码:6287 / 6298
页数:12
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