Reduction of Prediction Errors for the Matrix Converter with an Improved Model Predictive Control

In this paper, an improved model predictive control (MPC) is proposed for the matrix converter (MC). First, the conventional MPC which adopts the separately discretized prediction models is discussed. It shows that the conventional MPC ignores the input–output interaction in every sampling period. Consequently, additional prediction errors arise, resulting in more current harmonics. Second, the principle of the improved MPC is presented. With the interaction considered, the integral state-space equation of the whole MC system is constructed and discretized to obtain the precise model. The eigenvalue analysis shows that the proposed prediction model has the same eigenvalues with the continuous model, and thus is more accurate than the conventional one to describe the MC’s behavior in every sampling period. Finally, experimental results under various working conditions prove that the proposed approach can always increase the control accuracy and reduce the harmonic distortions, which in turn requires smaller filter components.

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An Improved Model Predictive Control Method to Drive an Induction Motor Fed by Three-Level Diode-Clamped Indirect Matrix Converter

International Journal of Engineering and Technology Innovation ◽

10.46604/ijeti.2020.5870 ◽

2020 ◽

Vol 10 (4) ◽

pp. 265-279

Author(s):

Arman Farhadi ◽

Amir Akbari ◽

Ali Zakerian ◽

Mohammad Tavakoli Bina

Keyword(s):

Model Predictive Control ◽

Induction Motor ◽

Predictive Control ◽

Control Method ◽

Torque Ripple ◽

Matrix Converter ◽

Switching Frequency ◽

Motor Current ◽

Supply Current ◽

Improved Model

In this paper, an improved model predictive control method is proposed to drive an induction motor fed by a three-level matrix converter. The main objective of this paper is to present a novel method to increase the switching frequency at a constant sampling time. Also, it is analytically discussed that increasing the switching frequency not only can decrease the motor current ripples, but it can also significantly reduce its torque ripples. Finally, this study demonstrates that reducing the motor current ripple will improve the quality of the supply current. To be the accurate model and validate the motor drive system, a co-simulation method, which is a combination of FLUX and MATLAB software packages, is employed to find the simulation results. The findings indicate that the proposed method diminishes the THD of the supply current up to 26% approximately. Furthermore, increasing the switching frequency results in the torque ripple reduction by up to 10% almost.

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An Improved Model Predictive Control Method for Induction Motor Drives Fed by Indirect Matrix Converter

2018 IEEE International Power Electronics and Application Conference and Exposition (PEAC) ◽

10.1109/peac.2018.8590603 ◽

2018 ◽

Cited By ~ 1

Author(s):

Yang Mei ◽

Liang Wang ◽

Weichao Huang

Keyword(s):

Model Predictive Control ◽

Induction Motor ◽

Predictive Control ◽

Control Method ◽

Matrix Converter ◽

Motor Drives ◽

Induction Motor Drives ◽

Improved Model

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Sequential Model Predictive Control of Three-Phase Direct Matrix Converter

Energies ◽

10.3390/en12020214 ◽

2019 ◽

Vol 12 (2) ◽

pp. 214 ◽

Cited By ~ 5

Author(s):

Jianwei Zhang ◽

Margarita Norambuena ◽

Li Li ◽

David Dorrell ◽

Jose Rodriguez

Keyword(s):

Model Predictive Control ◽

Predictive Control ◽

Control Strategy ◽

Weighting Factor ◽

Matrix Converter ◽

Cost Functions ◽

Computational Burden ◽

Three Phase ◽

The Matrix ◽

Factor Design

The matrix converter (MC) is a promising converter that performs the direct AC-to-AC conversion. Model predictive control (MPC) is a simple and powerful tool for power electronic converters, including the MC. However, weighting factor design and heavy computational burden impose significant challenges for this control strategy. This paper investigates the generalized sequential MPC (SMPC) for a three-phase direct MC. In this control strategy, each control objective has an individual cost function and these cost functions are evaluated sequentially based on priority. The complex weighting factor design process is not required. Compared with the standard MPC, the computation burden is reduced because only the pre-selected switch states are evaluated in the second and subsequent sequential cost functions. In addition, the prediction model computation for the following cost functions is also reduced. Specifying the priority for control objectives can be achieved. A comparative study with traditional MPC is carried out both in simulation and an experiment. Comparable control performance to the traditional MPC is achieved. This controller is suitable for the MC because of the reduced computational burden. Simulation and experimental results verify the effectiveness of the proposed strategy.

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Model predictive control with feedback correction for optimal energy dispatch of a networked microgrid

Transactions of the Institute of Measurement and Control ◽

10.1177/0142331217701807 ◽

2017 ◽

Vol 41 (6) ◽

pp. 1540-1552 ◽

Cited By ~ 1

Author(s):

Xuping Zhou ◽

Huaicheng Yan ◽

Hao Zhang ◽

Chen Peng

Keyword(s):

Model Predictive Control ◽

Prediction Model ◽

Predictive Control ◽

Gas Turbines ◽

Prediction Models ◽

Power Line ◽

Power Line Communication ◽

Prediction Errors ◽

Optimal Energy ◽

Feedback Correction

The novel model predictive control with feedback correction designed in this paper aims to optimize the energy dispatch and minimize the operation costs of a microgrid, which contributes to the improvement of pollution emissions and economic growth. The microgrid communication is based on power line communication, thus more accurate prediction models of photovoltaic and wind power generations of a networked microgrid can be designed from weather forecast information transmitted by power line communication. The prediction model for micro gas turbines and the loads of a microgrid are also proposed for optimization of the model predictive control. The rolling optimization model is updated by the latest forecast information to get minimization costs and optimal energy dispatch. The feedback correction designs predictions of generation and loads prediction errors to give an adjustment of the prediction model. Then the energy optimization dispatch will be updated by the adjusted prediction, so the most optimal dispatch will be obtained. Finally, the data of a microgrid in the Zhejiang province is applied in simulation and the minimization costs are compared with ideal costs to verify the performance and effectiveness of the proposed model predictive control strategy.

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