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A voltage sensorless finite control set-model predictive control for three-phase voltage source PWM rectifiers

机译:三相电压源PWM整流器的无电压传感器有限控制集模型预测控制

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摘要

In this paper, a grid voltage sensorless model predictive control is proposed and verified by simulation and experimental tests for a PWM rectifier. The presented method is simple and cost effective due to no need of modulator and voltage sensors. The developed sliding mode voltage observer (SMVO) can theoretically track the grid voltage accurately without phase lag and magnitude error. Based on the proposed SMVO, the finite control set-model predictive control (FCS-MPC) is incorporated for power regulation. The active power and reactive power are calculated and predicted using the measured current and the estimated grid voltage from the SMVO. With the predicated power for one-step delay compensation, the best voltage vector minimizing the tracking error is selected by FCS-MPC. The whole algorithm is implemented in stationary frame without using Park's transformation. Both the simulation and experimental results validate the effectiveness of the proposed method.
机译:本文提出了一种电网电压无传感器模型预测控制,并通过PWM整流器的仿真和实验测试进行了验证。由于不需要调制器和电压传感器,因此所提出的方法简单且具有成本效益。理论上,开发的滑模电压观测器(SMVO)可以准确地跟踪电网电压,而不会出现相位滞后和幅度误差。基于提出的SMVO,将有限控制集模型预测控制(FCS-MPC)纳入功率调节。使用从SMVO测得的电流和估算的电网电压来计算和预测有功功率和无功功率。利用用于一阶延迟补偿的预测功率,FCS-MPC选择了最小化跟踪误差的最佳电压矢量。整个算法在不使用Park变换的情况下在固定框架中实现。仿真和实验结果均验证了该方法的有效性。

著录项

  • 来源
    《Chinese Journal of Electrical Engineering》 |2016年第2期|52-59|共8页
  • 作者单位

    School of Electrical, Mechanical, and Mechatronic Systems, University of Technology, Sydney, NSW 2007, Australia;

    Inverter Technologies Engineering Research Center of Beijing, North China University of Technology, Beijing, 100144, China;

    School of Electrical, Mechanical, and Mechatronic Systems, University of Technology, Sydney, NSW 2007, Australia;

    School of Electrical, Mechanical, and Mechatronic Systems, University of Technology, Sydney, NSW 2007, Australia;

    Inverter Technologies Engineering Research Center of Beijing, North China University of Technology, Beijing, 100144, China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Silicon;

    机译:硅;

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