IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i11p2519-d1400387.html
   My bibliography  Save this article

Model Predictive Control of a Modular Multilevel Converter with Reduced Computational Burden

Author

Listed:
  • Hussein Kadhum

    (Electrical and Electronic Engineering Department, University of Nottingham, Nottingham NG7 2RD, UK)

  • Alan J. Watson

    (Electrical and Electronic Engineering Department, University of Nottingham, Nottingham NG7 2RD, UK)

  • Marco Rivera

    (Electrical and Electronic Engineering Department, University of Nottingham, Nottingham NG7 2RD, UK
    Laboratory of Energy Conversion and Power Electronics, Direction of Research, Universidad de Talca, Talca 3341717, Chile)

  • Pericle Zanchetta

    (Electrical and Electronic Engineering Department, University of Nottingham, Nottingham NG7 2RD, UK)

  • Patrick Wheeler

    (Electrical and Electronic Engineering Department, University of Nottingham, Nottingham NG7 2RD, UK
    Centro de Transformación Energética, Universidad Andrés Bello, Santiago 7550196, Chile)

Abstract

Recent advances in high-power applications employing voltage source converters have been primarily fuelled by the emergence of the modular multilevel converter (MMC) and its derivatives. Model predictive control (MPC) has emerged as an effective way of controlling these converters because of its high response. However, the practical implementation of MPC encounters hurdles, particularly in MMCs featuring many sub-modules per arm. This research introduces an approach termed folding model predictive control (FMPC), coupled with a pre-processing sorting algorithm, tailored for modular multilevel converters. The objective is to alleviate a significant part of the computational burden associated with the control of these converters. The FMPC framework combines multiple control objectives, encompassing AC current, DC current, circulating current, arm energy, and leg energy, within a unified cost function. Both simulation studies and real-time hardware-in-the-loop (HIL) testing are conducted to verify the efficacy of the proposed FMPC. The findings underscore the FMPC’s ability to deliver fast response and robust performance under both steady-state and dynamic operating conditions. Moreover, the FMPC adeptly mitigates circulating currents, reduces total harmonic distortion (THD%), and upholds capacitor voltage stability within acceptable thresholds, even in the presence of harmonic distortions in the AC grid. The practical applicability of MMCs, notwithstanding the presence of a large number of sub-modules (SMs) per arm, is facilitated by the significant reduction in switching states and computational overhead achieved through the FMPC approach.

Suggested Citation

  • Hussein Kadhum & Alan J. Watson & Marco Rivera & Pericle Zanchetta & Patrick Wheeler, 2024. "Model Predictive Control of a Modular Multilevel Converter with Reduced Computational Burden," Energies, MDPI, vol. 17(11), pages 1-25, May.
  • Handle: RePEc:gam:jeners:v:17:y:2024:i:11:p:2519-:d:1400387
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/11/2519/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/17/11/2519/
    Download Restriction: no
    ---><---

    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:17:y:2024:i:11:p:2519-:d:1400387. 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.