Abstract

Variable switching frequency in the finite control set model predictive control (FCS-MPC) method causes a negative impact on the converter efficiency and the design of the output filters. Several studies have addressed the problem, but they are either complicated or require heavy computation. This study proposes a new model predictive control (MPC) method with constant switching frequency, which is simple to implement and needs only a small computation time. The proposed MPC method is based on the gradient descent (GD) method to find the optimal voltage vector. Since the cost function of the MPC method is represented in the strongly convex function, the optimal voltage vector could be found quickly by using the GD method, which reduces the computation time of the MPC method. The design of the proposed MPC method based on GD (GD-MPC) is shown in this study. The feasibility of the proposed GD-MPC is evaluated in the real-time simulation using OPAL-RT technologies. The performance of the proposed method in the case of single inverter operation or parallel inverter operation is shown. A comparison study on the proposed GD-MPC and the MPC with the concept of the virtual state vector (VSV-MPC) is presented to demonstrate the effectiveness of the proposed predictive control. Real-time simulation results show that the proposed GD-MPC method performs better with a low total harmonic distortion (THD) value of output current and short computation time, compared to the VSV-MPC method.

Highlights

  • Finite control set model predictive control (FCS-MPC) has been widely used for controlling the power converters in the microgrid (MG) system

  • With the number of virtual state vectors equal to 962 in the case of the VSV-MPC method, the output voltage and current are sinusoidal with low total harmonic distortion (THD) values

  • MGproposed system, which is based gradient descent to find voltage distributed generations in the system, which is based on gradient descent (GD)

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Summary

Introduction

Finite control set model predictive control (FCS-MPC) has been widely used for controlling the power converters in the microgrid (MG) system. Based on the discrete-time model of the converter system with the finite number of switching states, the FCS-MPC technique predicts all of the future behaviors of the controlled variables. Since the switching frequency of the converter is dependent on the switching period, the predictive control based on the regulation of the switching period could provide constant switching frequency [22,23] Another interesting method to improve the FCS-MPC with constant switching frequency is to use the virtual state vectors (VSVs) [11,12,13,14,15,16,17,18,19,20,21,22,23,24]. The large number of calculations and more powerful control platform are the limitations of this technique To overcome these problems, many studies have proposed methods to decrease the computational cost of the MPC [24,25,26]. Predictive Voltage Control with Constant Switching Frequency Based on Virtual Vector

Predictive
Gradient
Proposed
Results
Effect of Learning Rate
It can that be seen proposedatMPC descent is stably operated when
Effect
Effect of Uncertain Parameter
Nonlinear Load Condition
A Comparison Study
Computation Time
Parallel Operation of Inverters in Microgrid
11. Tested
Conclusions
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