IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i13p7038-d580316.html
   My bibliography  Save this article

Adaptive Predictive Control with Neuro-Fuzzy Parameter Estimation for Microgrid Grid-Forming Converters

Author

Listed:
  • Oluleke Babayomi

    (School of Electrical Engineering, Shandong University, Jinan 250061, China)

  • Zhenbin Zhang

    (School of Electrical Engineering, Shandong University, Jinan 250061, China)

  • Yu Li

    (School of Electrical Engineering, Shandong University, Jinan 250061, China)

  • Ralph Kennel

    (Institute for Electrical Drive Systems and Power Electronics, Technische Universitaet Muenchen, 80333 Munich, Germany)

Abstract

Model predictive control (MPC) is a flexible and multivariable control technique with better dynamic performance than linear control. However, MPC is sensitive to parametric mismatches that reduce its control capabilities. In this paper, we present a new method of improving the robustness of MPC to filter parameter variations/mismatches by easily implementable parameter estimation. Furthermore, we extend the proposed technique for wider operating conditions by novel neuro-fuzzy estimation. The results, which are demonstrated by both simulations and real-time hardware-in-the-loop tests, show a steady-state parameter estimation accuracy of 95%, and at least 20% improvement in total harmonic distortion (THD) than conventional non-adaptive MPC under parameter mismatches up to 50% of the nominal values.

Suggested Citation

  • Oluleke Babayomi & Zhenbin Zhang & Yu Li & Ralph Kennel, 2021. "Adaptive Predictive Control with Neuro-Fuzzy Parameter Estimation for Microgrid Grid-Forming Converters," Sustainability, MDPI, vol. 13(13), pages 1-17, June.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:13:p:7038-:d:580316
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/13/7038/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/13/13/7038/
    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:jsusta:v:13:y:2021:i:13:p:7038-:d:580316. 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.