Finite Control Set Model Predictive Control Method with Fast Optimization Based on T-Type Three-Level Grid-Connected Inverter
Xin Yechun, Wang Yanxu, Li Guoqing, Wang Chaobin, Wang Wei
Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin 132012 China
Abstract：Rolling optimization of Finite Control Set model predictive control (Finite Control Set-MPC, FCS-MPC) needs to traverse all the switch states in the three-level converter control system, which will cause the problems of increased processor calculation and long processing time. For this reason, this paper proposes a FCS-MPC method with optimized calculation amount of T-type three-level grid-connected inverter. By constructing a single objective cost function based on voltage prediction, the design of weighting factor is avoided and the steps of single optimization are reduced. For improving the efficiency of optimization, the redundant small vector is selected according to the DC bus potential distribution to balance the neutral-point potential and reduce the number of predictions per control cycle to 3 times. The finite control set is divided according to the minimum weighted error square of the included vectors in the prediction process, and the vector angle is used to compensate the system delay, thereby improving the prediction accuracy and the grid-connected current quality. A power hardware-in-the-loop simulation system based on RT-Lab and a physical device are established to verify the proposed control strategy. The results show that the proposed theoretical analysis is correct and the control strategy is effective.
辛业春, 王延旭, 李国庆, 王朝斌, 王尉. T型三电平并网逆变器有限集模型预测控制快速寻优方法[J]. 电工技术学报, 2021, 36(8): 1681-1692.
Xin Yechun, Wang Yanxu, Li Guoqing, Wang Chaobin, Wang Wei. Finite Control Set Model Predictive Control Method with Fast Optimization Based on T-Type Three-Level Grid-Connected Inverter. Transactions of China Electrotechnical Society, 2021, 36(8): 1681-1692.
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2021, Vol. 36 
