IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v11y2018i2p370-d130240.html
   My bibliography  Save this article

A Duty Cycle Space Vector Modulation Strategy for a Three-to-Five Phase Direct Matrix Converter

Author

Listed:
  • Rutian Wang

    (School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China)

  • Xue Wang

    (School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China)

  • Chuang Liu

    (School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China)

  • Xiwen Gao

    (Jilin Electric Power Survey Design Limited Company, Jilin 132000, China)

Abstract

The duty cycle space vector (DCSV) modulation strategy is of universal significance, and the method can be utilized for different modulation approaches. In this paper, the vectors of input voltages and currents are equivalently represented by a complex two-dimensional space vector, and the vectors of output voltages and currents are equivalently represented by two two-dimensional space vectors. Then, input–output relationships in both the d1-q1 space and the d3-q3 space are obtained. Because the desired output voltages are only mapped onto a reference voltage space vector in the d1-q1 space, the reference in the d3-q3 space is regarded as zero, in order to reduce harmonics of output voltages to the greatest extent. Then, the duty cycle space vector modulation strategy of the three-to-five phase direct matrix converter (DMC) is deduced. Considering the influence of the zero vector on system performance, the duty cycles are decomposed and recomposed to obtain the space vector pulse width modulation (SVPWM) strategy based on the duty cycle space vector. Finally, the accuracy and feasibility of the theory are verified through experiments.

Suggested Citation

  • Rutian Wang & Xue Wang & Chuang Liu & Xiwen Gao, 2018. "A Duty Cycle Space Vector Modulation Strategy for a Three-to-Five Phase Direct Matrix Converter," Energies, MDPI, vol. 11(2), pages 1-13, February.
  • Handle: RePEc:gam:jeners:v:11:y:2018:i:2:p:370-:d:130240
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/11/2/370/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/11/2/370/
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Shuang Feng & Chaofan Wei & Jiaxing Lei, 2019. "Reduction of Prediction Errors for the Matrix Converter with an Improved Model Predictive Control," Energies, MDPI, vol. 12(15), pages 1-20, August.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:11:y:2018:i:2:p:370-:d:130240. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.