IDEAS home Printed from https://ideas.repec.org/p/hal/journl/hal-04831665.html
   My bibliography  Save this paper

Novel Direct Power Control Based on Grid Voltage Modulated Strategy Using Artificial Intelligence

Author

Listed:
  • Mujammal Ahmed Hasan Mujammal
  • Abdelhafidh Moualdia
  • Mohammed Abdulelah Albasheri
  • Patrice Wira
  • Abderrezzak Cherifi

    (END-ICAP - Handicap neuromusculaire : Physiopathologie, Biothérapie et Pharmacologies appliquées - UVSQ - Université de Versailles Saint-Quentin-en-Yvelines - INSERM - Institut National de la Santé et de la Recherche Médicale)

Abstract

This paper introduces a novel control algorithm leveraging artificial intelligence to address the key defects of Direct Power Control (DPC) via Grid Voltage Modulation (GVM) strategy enhanced by Neural Network Control (NNC) for a three-phase inverter in a photovoltaic generation system. Conventional DPC-GVM techniques face major constraints due to the need for accurate tuning of gains (KP and Ki) in three PI controls, which affects the system s robustness, reliability, and stability. Besides, these conventional techniques suffer from incomplete decoupling between active and reactive powers, direct interdependence between the DC-link voltage and active power reference, elevated harmonic distortion, and suboptimal transient response. This paper introduces a progressive NNC-based algorithm, termed GVM-NNC (Grid Voltage Modulated-Neural Network Control), to overcome these issues. The proposed strategy effectively decouples the active and reactive power control, mitigates the dependence between DC-link voltage and active power reference, diminishes harmonic distortion, and improves transient response. The innovations and contributions of the GVM-NNC strategy include decoupling of active and reactive power for improved control precision, robust adaptability to sudden changes and external disturbances guaranteeing excellent dynamic response, enhanced stability and reliability by eliminating the need for precise tuning of PI controller gains, reduced harmonic distortion for cleaner power output, where the proposed algorithm reduces the THD until 0.98% compared to the THD of conventional DPC-GVM 1.20%. Numerical simulations executed in MATLAB Simulink demonstrate that the GVM-NNC method achieves superior comportment in each steady-state and transient state compared to the conventional DPC-GVM strategy.

Suggested Citation

  • Mujammal Ahmed Hasan Mujammal & Abdelhafidh Moualdia & Mohammed Abdulelah Albasheri & Patrice Wira & Abderrezzak Cherifi, 2024. "Novel Direct Power Control Based on Grid Voltage Modulated Strategy Using Artificial Intelligence," Post-Print hal-04831665, HAL.
    • Handle: RePEc:hal:journl:hal-04831665
    DOI: 10.1007/s40866-024-00225-1
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    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:hal:journl:hal-04831665. 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: CCSD (email available below). General contact details of provider: https://hal.archives-ouvertes.fr/ .

    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.