IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i14p4358-d597304.html
   My bibliography  Save this article

Proportional Usage of Low-Level Actions in Model Predictive Control for Six-Phase Electric Drives

Author

Listed:
  • Angel Gonzalez-Prieto

    (Electrical Engineering Department, University of Málaga, 29071 Málaga, Spain)

  • Ignacio Gonzalez-Prieto

    (Electrical Engineering Department, University of Málaga, 29071 Málaga, Spain)

  • Mario J. Duran

    (Electrical Engineering Department, University of Málaga, 29071 Málaga, Spain)

  • Juan Carrillo-Rios

    (Electrical Engineering Department, University of Málaga, 29071 Málaga, Spain)

  • Juan J. Aciego

    (Electrical Engineering Department, University of Málaga, 29071 Málaga, Spain)

  • Pedro Salas-Biedma

    (Electrical Engineering Department, University of Málaga, 29071 Málaga, Spain)

Abstract

Finite Control-Set Model Predictive Control (FCS-MPC) appears as an interesting alternative to regulate multiphase electric drives, thanks to inherent advantages such as its capability to include new restrictions and fast-transient response. Nevertheless, in industrial applications, FCS-MPC is typically discarded to control multiphase motors because the absence of a modulation stage produces a high harmonic content. In this regard, multi-vectorial approaches are an innovative solution to improve the electric drive performance taking advantage of the implicit modulator flexibility of Model Predictive Control (MPC) strategies. This work proposes the definition of a new multi-vectorial set of control actions formed by a couple of adjacent large voltage vectors and a null voltage vector with an adaptative application ratio. The combination of two large voltage vectors provides minimum x-y current injection whereas the application of a null voltage vector reduces the active voltage production. Moreover, the optimum selection of the null voltage vector for each couple of large voltage vectors permits reducing the switching frequency. On the other hand, the active application time for this couple is estimated through an analytic function based on the operating point. This procedure avoids the use of an iterative process to define the duty cycles, hence significatively decreasing the computational burden.

Suggested Citation

  • Angel Gonzalez-Prieto & Ignacio Gonzalez-Prieto & Mario J. Duran & Juan Carrillo-Rios & Juan J. Aciego & Pedro Salas-Biedma, 2021. "Proportional Usage of Low-Level Actions in Model Predictive Control for Six-Phase Electric Drives," Energies, MDPI, vol. 14(14), pages 1-15, July.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:14:p:4358-:d:597304
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/14/4358/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/14/4358/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Pedro Gonçalves & Sérgio Cruz & André Mendes, 2019. "Finite Control Set Model Predictive Control of Six-Phase Asymmetrical Machines—An Overview," Energies, MDPI, vol. 12(24), pages 1-42, December.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Marwa Ben Slimene & Mohamed Arbi Khlifi, 2022. "Investigation on the Effects of Magnetic Saturation in Six-Phase Induction Machines with and without Cross Saturation of the Main Flux Path," Energies, MDPI, vol. 15(24), pages 1-18, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Marwa Ben Slimene & Mohamed Arbi Khlifi, 2022. "Investigation on the Effects of Magnetic Saturation in Six-Phase Induction Machines with and without Cross Saturation of the Main Flux Path," Energies, MDPI, vol. 15(24), pages 1-18, December.
    2. Karol Wróbel & Piotr Serkies & Krzysztof Szabat, 2020. "Model Predictive Base Direct Speed Control of Induction Motor Drive—Continuous and Finite Set Approaches," Energies, MDPI, vol. 13(5), pages 1-15, March.
    3. Jaime A. Rohten & David N. Dewar & Pericle Zanchetta & Andrea Formentini & Javier A. Muñoz & Carlos R. Baier & José J. Silva, 2021. "Multivariable Deadbeat Control of Power Electronics Converters with Fast Dynamic Response and Fixed Switching Frequency," Energies, MDPI, vol. 14(2), pages 1-16, January.
    4. Carlos Romero & Larizza Delorme & Osvaldo Gonzalez & Magno Ayala & Jorge Rodas & Raul Gregor, 2021. "Algorithm for Implementation of Optimal Vector Combinations in Model Predictive Current Control of Six-Phase Induction Machines," Energies, MDPI, vol. 14(13), pages 1-15, June.
    5. Thyago Estrabis & Gabriel Gentil & Raymundo Cordero, 2021. "Development of a Resolver-to-Digital Converter Based on Second-Order Difference Generalized Predictive Control," Energies, MDPI, vol. 14(2), pages 1-22, January.
    6. Claudio Rossi & Yasser Gritli & Alessio Pilati & Gabriele Rizzoli & Angelo Tani & Domenico Casadei, 2020. "High Resistance Fault-Detection and Fault-Tolerance for Asymmetrical Six-Phase Surface-Mounted AC Permanent Magnet Synchronous Motor Drives," Energies, MDPI, vol. 13(12), pages 1-18, June.
    7. Sergio Toledo & Edgar Maqueda & Marco Rivera & Raúl Gregor & Pat Wheeler & Carlos Romero, 2020. "Improved Predictive Control in Multi-Modular Matrix Converter for Six-Phase Generation Systems," Energies, MDPI, vol. 13(10), pages 1-13, May.
    8. Hongtai Ma & Li Li & Yingpeng Fan & Youguang Guo & Zhihui Jin & Jian Luo, 2022. "A Discrete Current Controller for High Power-Density Synchronous Machines," Energies, MDPI, vol. 15(17), pages 1-23, September.
    9. Jing Tang & Yongheng Yang & Jie Chen & Ruichang Qiu & Zhigang Liu, 2019. "Characteristics Analysis and Measurement of Inverter-Fed Induction Motors for Stator and Rotor Fault Detection," Energies, MDPI, vol. 13(1), pages 1-17, December.

    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:14:y:2021:i:14:p:4358-:d:597304. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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.