IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i3p746-d318183.html
   My bibliography  Save this article

A Genetic-Algorithm-Based DC Current Minimization Scheme for Transformless Grid-Connected Photovoltaic Inverters

Author

Listed:
  • Lei Song

    (School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China)

  • Lijun Huang

    (Guangzhou Haige Communications Group Incorporated Company, Guangzhou 510700, China)

  • Bo Long

    (School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China)

  • Fusheng Li

    (School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China)

Abstract

Transformerless grid-connected inverters are of great industrial value in photovoltaic power generation. However, the direct current (DC) induced into the inverter’s output degrades the power quality of the grid. Recently, a back-propagation neural work proportional–integral–derivative (BP-PID) scheme has proven helpful in solving this problem. However, this scheme can be improved by reducing the suppressing time and overshoot. A genetic algorithm (GA)-based DC current minimization scheme, namely the genetic-algorithm-based BP-PID (GA-BP-PID) scheme, was established in this study. In this scheme, GA was used off-line to optimize the initial weights within the BP neural network. Subsequently, the optimal weight was applied to the online DC current suppression process. Compared with the BP-PID scheme, the proposed scheme can reduce the suppressing time by 59% and restrain the overshoot. A prototype of the proposed scheme was implemented and tested on experimental hardware as a proof of concept. The results of the scheme were verified using a three-phase inverter experiment. The novel GA-PB-PID scheme proposed in this study was proven efficient in reducing the suppressing time and overshoot.

Suggested Citation

  • Lei Song & Lijun Huang & Bo Long & Fusheng Li, 2020. "A Genetic-Algorithm-Based DC Current Minimization Scheme for Transformless Grid-Connected Photovoltaic Inverters," Energies, MDPI, vol. 13(3), pages 1-18, February.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:3:p:746-:d:318183
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/3/746/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/3/746/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Long Bo & Lijun Huang & Yufei Dai & Youliang Lu & Kil To Chong, 2018. "Mitigation of DC Components Using Adaptive BP-PID Control in Transformless Three-Phase Grid-Connected Inverters," Energies, MDPI, vol. 11(8), pages 1-22, August.
    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. Mukul Chankaya & Ikhlaq Hussain & Aijaz Ahmad & Irfan Khan & S.M. Muyeen, 2021. "Nyström Minimum Kernel Risk-Sensitive Loss Based Seamless Control of Grid-Tied PV-Hybrid Energy Storage System," Energies, MDPI, vol. 14(5), pages 1-22, March.
    2. Xiongchao Lin & Wenshuai Xi & Jinze Dai & Caihong Wang & Yonggang Wang, 2020. "Prediction of Slag Characteristics Based on Artificial Neural Network for Molten Gasification of Hazardous Wastes," Energies, MDPI, vol. 13(19), pages 1-18, October.
    3. Marcel Nicola & Claudiu-Ionel Nicola & Dan Selișteanu, 2022. "Improvement of the Control of a Grid Connected Photovoltaic System Based on Synergetic and Sliding Mode Controllers Using a Reinforcement Learning Deep Deterministic Policy Gradient Agent," Energies, MDPI, vol. 15(7), pages 1-32, March.
    4. Marcel Nicola & Claudiu-Ionel Nicola, 2021. "Fractional-Order Control of Grid-Connected Photovoltaic System Based on Synergetic and Sliding Mode Controllers," Energies, MDPI, vol. 14(2), pages 1-25, January.

    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. Saleh Masoud Abdallah Altbawi & Ahmad Safawi Bin Mokhtar & Saifulnizam Bin Abdul Khalid & Nusrat Husain & Ashraf Yahya & Syed Aqeel Haider & Rayan Hamza Alsisi & Lubna Moin, 2023. "Optimal Control of a Single-Stage Modular PV-Grid-Driven System Using a Gradient Optimization Algorithm," Energies, MDPI, vol. 16(3), pages 1-23, February.

    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:13:y:2020:i:3:p:746-:d:318183. 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.