Abstract
Model predictive control (MPC) methods are widely used in the power electronic control field, including finite control set model predictive control (FCS-MPC) and continuous control set model predictive control (CCS-MPC). The degree of parameter uncertainty influence on the two methods is the key to evaluate the feasibility of the two methods in power electronic application. This paper proposes a research method to analyze FCS-MPC and CCS-MPC’s influence on the current prediction error of three-phase active power filter (APF) under parameter uncertainty. It compares the performance of the two model predictive control methods under parameters uncertainty. In each sampling period of the prediction algorithm, different prediction error conditions will be produced when FCS-MPC cycles the candidate vectors. Different pulse width modulation (PWM) results will be produced when CCS-MPC solves the quadratic programming (QP) problem. This paper presents the simulation results and discusses the influence of inaccurate modeling of load resistance and inductance parameters on the control performance of the two MPC algorithms, the influence of reference value and state value on prediction error is also compared. The prediction error caused by resistance mismatch is lower than that caused by inductance mismatch, more errors are caused by underestimating inductance values than by overestimating inductance values. The CCS-MPC has a better control effect and dynamic performance in parameter mismatch, and the influence of parameter mismatch is relatively tiny.
Highlights
In industry, daily life, and new energy power generation (Zhang et al, 2021), many electronic devices are connected to the grid
Simulation results verify the performance of CCSMPC and FCS-Model predictive control (MPC) on parameter mismatch (PM), the MATLAB/Simulink model is established, and their results are compared
This paper focuses on the two popular MPC methods of threephase active power filter (APF), finite control set model predictive control (FCS-MPC), and continuous control set model predictive control (CCS-MPC), and analyzes their inductance and resistance values when they are not matched
Summary
Daily life, and new energy power generation (Zhang et al, 2021), many electronic devices are connected to the grid. These will cause harmonic pollution, consume reactive power and reduce the power quality of the power grid (Singh et al, 1999). The principle of FCS-MPC is necessary to: 1) Establish the mathematical model of the converter (based on Kirchhoff’s law), 2) The discrete prediction model is obtained, 3) The cost function is established; 4) FCS-MPC traverse seven vectors; 5) The switch state combination is obtained under the minimum cost function, applied to the converter
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