IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i5p2146-d1077335.html
   My bibliography  Save this article

A Novel Deep Reinforcement Learning-Based Current Control Method for Direct Matrix Converters

Author

Listed:
  • Yao Li

    (College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China)

  • Lin Qiu

    (College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China)

  • Xing Liu

    (College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China)

  • Jien Ma

    (College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China)

  • Jian Zhang

    (College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China)

  • Youtong Fang

    (College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China)

Abstract

This paper presents the first approach to a current control problem for the direct matrix converter (DMC), which makes use of the deep reinforcement learning algorithm. The main objective of this paper is to solve the real-time capability issues of traditional control schemes (e.g., finite-set model predictive control) while maintaining feasible control performance. Firstly, a deep Q-network (DQN) algorithm is utilized to train an agent, which learns the optimal control policy through interaction with the DMC system without any plant-specific knowledge. Next, the trained agent is used to make computationally efficient online control decisions since the optimization process has been carried out in the training phase in advance. The novelty of this paper lies in presenting the first proof of concept by means of controlling the load phase currents of the DMC via the DQN algorithm to deal with the excessive computational burden. Finally, simulation and experimental results are given to demonstrate the effectiveness and feasibility of the proposed methodology for DMCs.

Suggested Citation

  • Yao Li & Lin Qiu & Xing Liu & Jien Ma & Jian Zhang & Youtong Fang, 2023. "A Novel Deep Reinforcement Learning-Based Current Control Method for Direct Matrix Converters," Energies, MDPI, vol. 16(5), pages 1-13, February.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:5:p:2146-:d:1077335
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/5/2146/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/5/2146/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Sergio Toledo & David Caballero & Edgar Maqueda & Juan J. Cáceres & Marco Rivera & Raúl Gregor & Patrick Wheeler, 2022. "Predictive Control Applied to Matrix Converters: A Systematic Literature Review," Energies, MDPI, vol. 15(20), pages 1-30, October.
    2. Paola Maidana & Christian Medina & Jorge Rodas & Edgar Maqueda & Raúl Gregor & Pat Wheeler, 2022. "Sliding-Mode Current Control with Exponential Reaching Law for a Three-Phase Induction Machine Fed by a Direct Matrix Converter," Energies, MDPI, vol. 15(22), pages 1-17, November.
    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. Weihua Ou & Jianping Gou & Shaoning Zeng & Lan Du, 2023. "Preface to the Special Issue “Advancement of Mathematical Methods in Feature Representation Learning for Artificial Intelligence, Data Mining and Robotics”—Special Issue Book," Mathematics, MDPI, vol. 11(4), pages 1-4, February.

    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. Leonardo Comparatore & Magno Ayala & Yassine Kali & Jorge Rodas & Julio Pacher & Alfredo Renault & Raúl Gregor, 2023. "Discrete-Time Sliding Mode Current Control for a Seven-Level Cascade H-Bridge Converter," Energies, MDPI, vol. 16(5), pages 1-19, March.
    2. Raúl Gregor & Sergio Toledo & Edgar Maqueda & Julio Pacher, 2023. "Part I—Advancements in Power Converter Technologies: A Focus on SiC-MOSFET-Based Voltage Source Converters," Energies, MDPI, vol. 16(16), pages 1-17, 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:16:y:2023:i:5:p:2146-:d:1077335. 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.