• DocumentCode
    3739071
  • Title

    Novel ripple reduced Direct Model Predictive Control of three-level NPC active front end with reduced computational effort

  • Author

    Zhenbin Zhang; Hui Fang;Ralph Kennel

  • Author_Institution
    Institute of Electrical Drive Systems and Power Electronics, Technische Universit?t M?nchen, Arcisstr. 21, D-80333, Munich, Germany
  • fYear
    2015
  • Firstpage
    32
  • Lastpage
    37
  • Abstract
    Three-level neutral-point clamped (NPC) power converter seems promising for high power grid-tied renewable applications. Direct Model Predictive Control (DMPC) is an attractive control method, in particular for multi-level converters. However, relatively big ripples of the control variables and heavy computational efforts are regarded as two of the shortcomings for DMPC schemes due to its cost enumeration and one-vector-per-control-interval characters. This work proposes a computational efficient ripple-reduced DMPC scheme for three level NPC Active-Front-End (AFE). By combining a deadbeat concept the targeted switching vectors are allocated efficiently and the respective actuating times of the vectors are on-line optimally calculated. Compared to the classical DMPC scheme, computational efforts are reduced efficiently and much smaller ripples of the control variables are achieved. Simulation results emphasize the effectiveness of the proposed scheme.
  • Keywords
    "Switches","Artificial neural networks","Voltage control","Cost function","Reactive power","Predictive control"
  • Publisher
    ieee
  • Conference_Titel
    Predictive Control of Electrical Drives and Power Electronics (PRECEDE), 2015 IEEE International Symposium on
  • Type

    conf

  • DOI
    10.1109/PRECEDE.2015.7395579
  • Filename
    7395579