A Model-Free Current Prediction Control with Runge-Kutta Algorithm for Grid-Connected Inverter

Guanglu Yang, Han Xiao, Yifeng Sun, Zhifeng Dou, Wenhui Wang, Zhiguo Wang
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Abstract

The conventional model predictive control (MPC) strategy produce current errors when the parameters of the inverter mismatch, which increases the Total Harmonic Distortion of grid current. To solve this problem, an improved method based on Runge-Kutta algorithm (RKA) is proposed. On the basis of the mathematical model of grid- connected inverter, current error of parameters mismatch of conventional MPC is analyzed. Moreover, Lagrange interpolation is performed to achieve the predictive current instead of the slope parameters of RKA, because the uncertain model with detailed parameters affects the prediction accuracy. Finally, simulation results verify the accuracy and effectiveness of the improved RKA approach.
基于龙格-库塔算法的并网逆变器无模型电流预测控制
传统的模型预测控制(MPC)策略在逆变器参数失配时产生电流误差,增大电网电流的总谐波畸变。为了解决这一问题,提出了一种基于龙格-库塔算法(RKA)的改进方法。在建立并网逆变器数学模型的基础上,分析了传统MPC参数失配引起的电流误差。此外,由于带有详细参数的不确定模型会影响预测精度,因此采用拉格朗日插值来实现预测电流,而不是RKA的斜率参数。最后,仿真结果验证了改进RKA方法的准确性和有效性。
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