Finite Control Set Model Predictive Control for an LCL-Filtered Grid-Tied Inverter with Full Status Estimations under Unbalanced Grid Voltage
Finite Control Set Model Predictive Control for an LCL-Filtered Grid-Tied Inverter with Full Status Estimations under Unbalanced Grid Voltage
复制标题
不平衡电网电压下具有完整状态估计的 LCL 滤波并网逆变器的有限控制集模型预测控制
DOI：
10.3390/en12142691
复制
发表时间：
2019-07
期刊：
影响因子：
3.2
通讯作者：
Frede Blaabjerg
中科院分区：
文献类型：
--
作者：
Xiaotao Chen;Weimin Wu;Ning Gao;Jiahao Liu;Henry Shu-Hung Chung;Frede Blaabjerg
针对电感-电容-电感（LCL）滤波并网逆变器，提出了一种新的有限控制集模型预测控制（FCS-MPC）策略，无论三相电网电压是否平衡，该策略都能获得正弦并网电流。与传统的FCS-MPC方法相比，本文提出的FCS-MPC算法增加了四种成分，将电网电压观测器（GVO）和Luenberger观测器组合在一起实现全状态估计（包括电网电压、电容器电压、逆变器侧电流和电网注入电流），同时利用序列提取器和参考发生器消除有功或无功的双频纹波。或电网电压不平衡引起的电网注入电流的负序分量（NSC）。建立了仿真模型和实验平台，验证了所提出的FCS-MPC策略的有效性，并在电网电压平衡和不平衡条件下进行了全状态估计。
This paper proposes a novel finite control set model predictive control (FCS-MPC) strategy with merely grid-injected current sensors for an inductance-capacitance-inductance (LCL)-filtered grid-tied inverter, which can obtain a sinusoidal grid-injected current whether three-phase grid voltages are balanced or not. Compared with the conventional FCS-MPC method, four compositions are added in the proposed FCS-MPC algorithm, where the grid voltage observer (GVO) and Luenberger observer are combined together to achieve full status estimations (including grid voltage, capacitor voltage, inverter-side current, and grid-injected current), while the sequence extractor and the reference generator are applied to eliminate the double frequency ripples of the active or reactive power, or the negative sequence component (NSC) of the grid-injected current caused by the unbalanced grid voltage. Simulation model and experimental platform are established to verify the effectiveness of the proposed FCS-MPC strategy, with full status estimations under both balanced and unbalanced grid voltage conditions.
登录
查看更多内容
影响因子：
6.7
作者：
Hui Zhang;X. Zhu;Jingcong Shi;L. Tan;Chuanjin Zhang;Kun Hu
通讯作者：
Hui Zhang;X. Zhu;Jingcong Shi;L. Tan;Chuanjin Zhang;Kun Hu
影响因子：
7.7
作者：
J. Suul;A. Luna;P. Rodríguez;T. Undeland
通讯作者：
J. Suul;A. Luna;P. Rodríguez;T. Undeland
影响因子：
4.4
作者：
Jan R. Svensson;M. Bongiorno;Ambra Sannino
通讯作者：
Jan R. Svensson;M. Bongiorno;Ambra Sannino
影响因子：
12.3
作者：
Xia, Changliang;Liu, Tao;Song, Zhanfeng
通讯作者：
Song, Zhanfeng
影响因子：
7.7
作者：
P. Falkowski;A. Sikorski
通讯作者：
P. Falkowski;A. Sikorski