Model Predictive Controllers With Capacitor Voltage Balancing for a Single-Phase Five-Level SiC/Si Based ANPC Inverter

Employing both high bandwidth (HBW) controller and wide bandgap (WBG) devices in the structure of converters improve the system size, performance, and efficiency. In this paper, HBW model predictive controllers (MPCs) are proposed, with both fixed and unfixed switching frequencies, to control a single-phase five-level hybrid active neutral-point-clamped (ANPC) inverter. A hybrid modulation technique is considered in this paper, in which some of the switches are modulating with high frequency. Therefore, Silicon-Carbide (SiC) MOSFETs are employed in the converter structure to increase the switching frequency and consequently reduce the filter size and increase converter power density. To have the functionality of multilevel output voltage, some restrictions are defined in the adopted MPC with unfixed switching frequency. In the MPC with the constant switching frequency, predefined switching sequences are employed for all sectors. Moreover, to control the neutral point (NP) voltage, the applied times of both small voltage vectors are sets through a cost function. Finally, the simulation and experimental results prove the ability of both proposed methods to control the voltages of the load and NP.This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/This work was supported by the APETT project, funded by Innovation Fund Denmark.fi=vertaisarvioitu|en=peerReviewed

Model-Free Predictive Control of Motor Drives and Power Converters:A Review

Predictive control has emerged as a promising control method in a variety of technological fields. Model predictive control, as one of the subdivisions of this control method, has found a growing number of applications in power electronics and motor drives. In practical implementations, model predictive control faces performance degradation of the controlled plant due to its dependency on a model. There are considerable numbers of review papers that are devoted to the different points of view of predictive control. However, the existing literature lacks a review study that addresses the solutions for parameter dependency of the model predictive control method. Recently, model-free predictive control has been used in drives and power electronics as a solution for dealing with the model-dependency of the model predictive control method. There are many papers that have used such methods. In this paper, a classification is proposed for the different implementation types of model-free predictive control or similar methods that address model parameter uncertainties. Additionally, a comparison between the methods is also presented

Model-free neural network-based predictive control for robust operation of power converters

An accurate definition of a system model significantly affects the performance of model-based control strategies, for example, model predictive control (MPC). In this paper, a model-free predictive control strategy is presented to mitigate all ramifications of the model’s uncertainties and parameter mismatch between the plant and controller for the control of power electronic converters in applications such as microgrids. A specific recurrent neural network structure called state-space neural network (ssNN) is proposed as a model-free current predictive control for a three-phase power converter. In this approach, NN weights are updated through particle swarm optimization (PSO) for faster convergence. After the training process, the proposed ssNN-PSO combined with the predictive controller using a performance criterion overcomes parameter variations in the physical system. A comparison has been carried out between the conventional MPC and the proposed model-free predictive control in different scenarios. The simulation results of the proposed control scheme exhibit more robustness compared to the conventional finite-control-set MPC