摘要
近年来,模型预测控制被广泛应用于两电平电压源逆变器以实现并网控制。然而,常规有限控制集模型预测控制(FCS-MPC)存在参数失配时预测精度下降的问题。为此,提出一种适用于LC滤波型逆变器并网电压鲁棒预测控制的无参数FCS-MPC方法。首先分析了参数变化对常规LC滤波型逆变器并网电压预测控制的影响。然后基于超局部建模理论建立了LC滤波型并网逆变器的二阶超局部模型,并研究了2个集总扰动的计算方法。该计算方法省去了网侧电流传感器,节约了成本,并实现了无参数预测控制。实验结果表明,与常规FCS-MPC方法相比,在参数不匹配的情况下,所提无参数FCS-MPC方法仍能达到较好的模型预测控制性能,验证了所提方法的有效性。
In recent years,model predictive control has been widely used in two-level voltage source inverters to achieve grid-connected control.However,the conventional FCS-MPC(Finite Control Set-Model Predictive Control)has the problem of decreased prediction accuracy when the parameters are mismatched.For this reason,a parameter-free FCS-MPC method for robust predictive control of grid-connected voltage for LC filter type inverter is proposed.Firstly,the influence of parameter change on the grid-connected voltage predictive control of conventional LC filter type inverters is analyzed.Then based on the ultra-local model theory,the second-order ultra-local model of the LC filter type grid-connected inverter is established,and the calculation method of two lumped disturbances is studied.The calculation method eliminates the grid-side current sensor and saves the cost,and realizes parameter-free predictive control.Finally,the experimental results show that,compared with the conventional FCS-MPC method,the proposed parameter-free FCS-MPC method has better model predictive control characteristics.In the case of parameter mismatch,parameter-free FCS-MPC can still achieve better model predictive control performance,which verifies the effectiveness of the proposed method.
作者
郭磊磊
李伟韬
李琰琰
窦智峰
金楠
GUO Leilei;LI Weitao;LI Yanyan;DOU Zhifeng;JIN Nan(College of Electric and Information Engineering,Zhengzhou University of Light Industry,Zhengzhou 450002,China)
出处
《电力自动化设备》
EI
CSCD
北大核心
2022年第6期90-95,共6页
Electric Power Automation Equipment
基金
国家自然科学基金资助项目(51707176,U2004166)
河南省青年人才托举工程项目(2019HYTP021)
河南省重点研发与推广专项(科技攻关)项目(202102210103,202102210304,212102210021)。
关键词
并网逆变器
超局部建模
集总扰动
无参数
模型预测控制
grid-connected inverter
ultra-local model
lumped disturbance
parameter-free
model predictive control