Total Harmonic Distortion Oriented Finite Control Set Model Predictive Control for Single-Phase Inverters

Li, Po; Li, Ruiyu; Feng, Haifeng

doi:10.3390/en11123467

Cited by 7 publications

(4 citation statements)

References 32 publications

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“…Moreover, a new preselection strategy is proposed to reduce the computational numerations from 37 to 6 voltage vectors, reduce the torque and flux ripples and ultimately achieve robust characteristics against parameter variations. In [18], a total harmonic distortion (THD)-oriented FCS-MPC controller is designed for single-phase inverters. To achieve this goal, a modified cost function is constructed using a linear combination, with weight factors of the current fundamental tracking error and an instantaneous THD value.…”

Section: Introductionmentioning

confidence: 99%

Step-by-Step Development and Implementation of FS-MPC for a FPGA-Based PMSM Drive System

et al. 2021

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In this paper, finite-set model-predictive control (FS-MPC) is inducted for a motor drive system. The dynamic response and multiple constraint handling nature of FS-MPC are the major factors that stand out among the controller family. However, for real-time implementation, the computational burden of FS-MPC is a primary concern. Due to the parallel processing nature and discrete nature of the hardware platform, the field-programmable gate array (FPGA) can be an alternative solution for the real-time implementation of the controller algorithm. The FPGA is capable of handling the computational requirements for FS-MPC implementation; however, the system development involves multiple steps that lead to a time-consuming debugging process. Moreover, specific hardware coding skill makes it more complex, corresponding to an increase in system complexity, which leads to a tedious task for the system development. This paper presents a FPGA-based implementation of the predictive current control of a permanent magnet synchronous motor (PMSM). FS-MPC of the PMSM drive system is designed and implemented using the digital model integration approach provided by the Xilinx system generator (XSG) and VIVADO platform. The step change in the load disturbance as well as the reference speed is considered for the analysis of the controller for the motor drive system. Moreover, the steady state error and harmonic distortion in the motor current is considered for an in-depth analysis of the system performance corresponding to different sampling frequencies.

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Section: Introductionmentioning

confidence: 99%

Step-by-Step Development and Implementation of FS-MPC for a FPGA-Based PMSM Drive System

et al. 2021

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“…Therefore, Model Predictive Control (MPC) becomes more and more suitable for PCS. MPC mainly includes two classifications: continuous control set MPC and Finite Control Set MPC (FCS‐MPC) . By comparing with these two mainstream methods, FCS‐MPC takes the discrete property of PCS into account naturally, where the system output is chosen from only a finite number of available switching states by evaluating them in terms of cost function and control constraints.…”

Section: Introductionmentioning

confidence: 99%

Finite Control Set Model Predictive Control with Model Parameter Correction for Power Conversion System in Battery Energy Storage Applications

Gao

Chen

et al. 2020

IEEJ Transactions Elec Engng

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Adding a Battery Energy Storage System (BESS) in the vicinity of renewable energy sources is a feasible solution to overcome the inherent negative impact caused by their random power fluctuating problems. Power conversion system performs as an interface between the battery packs and grid. The control method of power conversion system is essential to BESS. Finite Control Set Model Predictive Control (FCS‐MPC) is favorable to be chosen as the core control method for grid‐injected current regulation in such a system due to its outstanding benefactions, including fast dynamics, multiobjective optimization and simple implementations. Hence, this paper presents a FCS‐MPC with active damping feature for an Inductor(L) Capacitor(C) Inductor(L) (LCL)‐filter‐based power conversion system first. However, it is also well known that the practical effect of model predictive control significantly relies on the accuracy of mathematical model embedded within the controller. Parametric mismatch of the model tends to generate a prediction error, leading to a deterioration of the power quality of power conversion system or even instability issues furthermore. Therefore, this paper proposes an additional model correction strategy based on a comprehensive on‐line analysis on the difference between predictive and real value of respective voltage or current signals during the ever‐lasting several grid cycles to eliminate the influence of parametric mismatch. Simulation of an LCL‐filter‐based power conversion system is carried out to verify the validity of the theoretical analysis and control method in this paper. Finally, experimental results obtained from a down‐scaled prototype are provided to confirm the feasibility of the overall control strategy. © 2020 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

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“…The control action that minimizes the cost function is given directly for the power converter without a modulator [1][2][3]. The cost function of the FCS-MPC method is flexible; it is easy to include system constraints, nonlinearities, and multi-variable control objectives [4][5][6]. With the intuitive concept, the FCS-MPC technique has been used in various fields of applications [1,[7][8][9].…”

Section: Introductionmentioning

confidence: 99%

MPC with Constant Switching Frequency for Inverter-Based Distributed Generations in Microgrid Using Gradient Descent

2019

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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.

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Total Harmonic Distortion Oriented Finite Control Set Model Predictive Control for Single-Phase Inverters

Cited by 7 publications

References 32 publications

Step-by-Step Development and Implementation of FS-MPC for a FPGA-Based PMSM Drive System

Step-by-Step Development and Implementation of FS-MPC for a FPGA-Based PMSM Drive System

Finite Control Set Model Predictive Control with Model Parameter Correction for Power Conversion System in Battery Energy Storage Applications

MPC with Constant Switching Frequency for Inverter-Based Distributed Generations in Microgrid Using Gradient Descent

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