An Improved Model Predictive Control Strategy to Reduce Common-Mode Voltage for Two-Level Voltage Source Inverters Considering Dead-Time Effects

Guo, Leilei; Jin, Nan; Gan, Chun; Xu, Lie; Wang, Qunjing

doi:10.1109/tie.2018.2856194

Cited by 66 publications

(66 citation statements)

References 33 publications

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“…is calculated by using Equations 7and (8). The reference voltage V ref and DC-link voltage V dc generate the reference current signal by PI control.…”

Section: Establish Permanent Magnet Synchronous Generator Current Modelmentioning

confidence: 99%

“…where id(k), iq(k), ud(k) and uq(k) are the dq components of output current and voltage at tk instant, id(k + 1) and iq(k + 1) are the dq components of the predictive current value at tk+1 instant. Predicted current id(k + 1), iq(k + 1) is calculated by using Equations 7and (8). The reference voltage Vref and DC-link voltage Vdc generate the reference current signal by PI control.…”

Section: Establish Permanent Magnet Synchronous Generator Current Modelmentioning

confidence: 99%

“…Distributed generation (DG) is dominated by solar energy and wind power [4][5][6]. Wind energy has received extensive attention in terms of economy and system stability [7,8]. However, wind energy is fed to the grid through a power electronics system, which usually cannot provide the necessary inertia and damping for the grid [9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

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Model Predictive Virtual Synchronous Control of Permanent Magnet Synchronous Generator-Based Wind Power System

et al. 2020

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As much wind power is integrated into the power grid through power electronic equipment, the use of wind power is increased rapidly. Wind power system makes the power grid lack inertia and damping, thereby reducing power grid stability; in severe cases, it may even be disconnected. virtual synchronous generator (VSG) has been put forward to enhance the anti-disturbance performance of power grid. However, conventional VSG adopts an outer power loop and inner-current loop control. The inner-current loop control needs a pulse width modulation (PWM) module and proportion integration (PI) parameter settings. In order to reduce the parameter settings and simplify control structures, in this study, model predictive control (MPC) is used instead of inner-current loop control. At the same time—for the overall stability and control flexibility of the back-to-back system—we further propose to use outer-voltage loop control (OVLC) and MPC to stabilize direct current (DC) voltage on the machine-side and to employ model predictive virtual synchronous controls to provide inertia and damping for the power grid. The proposed control method was simulated in Matlab/Simulink (MathWorks, Massachusetts, MA, 2016) and verified by experimental results.

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“…is calculated by using Equations 7and (8). The reference voltage V ref and DC-link voltage V dc generate the reference current signal by PI control.…”

Section: Establish Permanent Magnet Synchronous Generator Current Modelmentioning

confidence: 99%

Section: Establish Permanent Magnet Synchronous Generator Current Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Model Predictive Virtual Synchronous Control of Permanent Magnet Synchronous Generator-Based Wind Power System

et al. 2020

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“…Model predictive control (MPC) has become one of the well-established modern control methods in power electronics, particularly for three-phase inverters with LC filter according to [1], [23], [27]- [29]. The key characteristic of MPC is to explicitly use the model of the system to predict the future behavior of the variables to be controlled, considering a certain time horizon.…”

Section: Introductionmentioning

confidence: 99%

“…Afterwards, MPC selects the optimal control action (i.e., optimal switching signals) based on the minimization of a pre-defined cost function, which represents the desired behavior of the system [30]- [32]. With the aim of getting lower THD and improving steady and dynamic performance, many methods have been proposed in the literature [29], [33]. For instance, the deployment of longer prediction horizons is presented in [34].…”

Section: Introductionmentioning

confidence: 99%

A Neural-Network-Based Model Predictive Control of Three-Phase Inverter With an Output $LC$ Filter

et al. 2019

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Model predictive control (MPC) has become one of the well-established modern control methods for three-phase inverters with an output LC filter, where a high-quality voltage with low total harmonic distortion (THD) is needed. Although it is an intuitive controller, easy to understand and implement, it has the significant disadvantage of requiring a large number of online calculations for solving the optimization problem. On the other hand, the application of model-free approaches such as those based on artificial neural networks approaches is currently growing rapidly in the area of power electronics and drives. This paper presents a new control scheme for a two-level converter based on combining MPC and feed-forward ANN, with the aim of getting lower THD and improving the steady and dynamic performance of the system for different types of loads. First, MPC is used, as an expert, in the training phase to generate data required for training the proposed neural network. Then, once the neural network is fine-tuned, it can be successfully used online for voltage tracking purpose, without the need of using MPC. The proposed ANN-based control strategy is validated through simulation, using MATLAB/Simulink tools, taking into account different loads conditions. Moreover, the performance of the ANN-based controller is evaluated, on several samples of linear and non-linear loads under various operating conditions, and compared to that of MPC, demonstrating the excellent steady-state and dynamic performance of the proposed ANNbased control strategy.Index Terms-Three-phase inverter, model predictive control, artificial neural network, UPS systems.

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Sensorless real-time implementation-based FS-MPCC and deadbeat predictive control with delay and dead-time compensation of PMSM using MRAS and T‐S fuzzy speed controller

Benakcha

Abdessemed

et al. 2023

Electr Eng

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An Improved Model Predictive Control Strategy to Reduce Common-Mode Voltage for Two-Level Voltage Source Inverters Considering Dead-Time Effects

Cited by 66 publications

References 33 publications

Model Predictive Virtual Synchronous Control of Permanent Magnet Synchronous Generator-Based Wind Power System

Model Predictive Virtual Synchronous Control of Permanent Magnet Synchronous Generator-Based Wind Power System

A Neural-Network-Based Model Predictive Control of Three-Phase Inverter With an Output $LC$ Filter

Sensorless real-time implementation-based FS-MPCC and deadbeat predictive control with delay and dead-time compensation of PMSM using MRAS and T‐S fuzzy speed controller

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