Abstract
This paper proposes a model predictive voltage control (MPVC) strategy with duty cycle control for grid-connected three-phase inverters with output LCL filter. The model of the system is used to predict the capacitor filter voltage according to the future output current for each possible switching state at each sampling period. Then the cost function for each prediction is determined and the switching state is selected. In the proposed method, two voltage vectors are applied during one sampling interval to achieve better steady-state performance. Finally, the optimal duration of the nonzero voltage vector is defined based on the duty cycle optimization, which is vital to the control system. The proposed strategy offers a better reference tracking error with less THD in linear and nonlinear load situations. The effectiveness of the proposed method has been verified by MATLAB/Simulink and experimental results exhibit a better steady-state performance with less sampling frequency.
Highlights
Renewable energy generation systems, such as photovoltaic and wind turbine connected to power grid are drawing more and more attention in recent years
This paper presents a new proposed method (MPVC with duty cycle optimization) to control the grid-connected Journal of Control Science and Engineering
As the proposed model predictive voltage control scheme is shown in Figure 3, this method uses the discrete-time model for the three-leg inverter and LCL filter to predict the capacitor filter voltage based on prediction of the output current and selects a switching state based on the minimization of cost function for each sampling time [20, 21]
Summary
Renewable energy generation systems, such as photovoltaic and wind turbine connected to power grid are drawing more and more attention in recent years. The conventional finite control set model predictive voltage control (FCS-MPVC) employs one voltage vector during one sampling period and optimization of duty cycle is not involved in the control method; it needs a high sampling frequency to achieve a better performance. Due to these reasons, research on new strategies to obtain better steady-state performance in lower sampling frequency is important [10,11,12,13].
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