A Computationally Efficient Quasi-Centralized DMPC for Back-to-Back Converter PMSG Wind Turbine Systems Without DC-Link Tracking Errors
A Computationally Efficient Quasi-Centralized DMPC for Back-to-Back Converter PMSG Wind Turbine Systems Without DC-Link Tracking Errors
复制标题
用于无直流链路跟踪误差的背靠背变流器 PMSG 风力涡轮机系统的计算高效的准集中式 DMPC
DOI:
10.1109/tie.2016.2573768
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发表时间:
2016-05
影响因子:
7.7
通讯作者:
R. Kennel
中科院分区:
文献类型:
--
作者:
J.-Z. Zhang;汪凤翔;T. Sun;J. Rodriguez;R. Kennel
Quasi-centralized direct model predictive control (QC-DMPC) scheme may serve as an effective alternative for back-to-back power converter in permanent magnet synchronous generator (PMSG) wind turbine systems. However, model errors and imperfect power efficiency lead to evident dc-link voltage tracking offset. This paper proposes a revised quasi-centralized direct model predictive control (RQC-DMPC) scheme for back-to-back converter PMSG wind turbine systems, within which, the dc-link voltage is directly controlled by a grid side predictive controller with a flexibly designed cost function using a revised dynamic reference generation concept. The dc-link voltage steady status tracking errors are eliminated. To reduce the computational efforts of the classical scheme, a computational efficient concept is incorporated into the proposed method. The proposed scheme is implemented on an entirely field programmable gate array-based platform. The effectiveness of the proposed method is verified through experimental data. The dc-link control performance comparison with classical proportional-integration controller-based methods and the QC-DMPC scheme under different scenarios are also experimentally investigated. The results emphasize the improvement of the proposed RQC-DMPC scheme.
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DOI:
10.1109/jestpe.2013.2294920
发表时间:
2014-03
影响因子:
5.5
作者:
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DOI:
10.1109/ipemc.2016.7512507
发表时间:
2016-05
期刊:
2016 IEEE 8th International Power Electronics and Motion Control Conference (IPEMC-ECCE Asia)
影响因子:
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DOI:
--
发表时间:
2016-05
期刊:
--
影响因子:
--
作者:
Zhenbin Zhang;R. Kennel
通讯作者:
Zhenbin Zhang;R. Kennel
影响因子:
7.7
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Zhenbin Zhang;He Xu;M. Xue;Zhe Chen;Tongjing Sun;R. Kennel;C. Hackl
影响因子:
7.7
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