IDEAS home Printed from https://ideas.repec.org/a/pkp/rocere/v9y2022i3p151-168id3144.html
   My bibliography  Save this article

Design and Simulation of Grid-Connected Photovoltaic System’s Performance Analysis with Optimal Control of Maximum Power Point Tracking Based on Artificial Intelligence

Author

Listed:
  • Faouzi Didi
  • Moustafa Sahnoune Chaouche

Abstract

The research presented in this paper is part of a more significant effort to improve the dynamic and static performance of power generation systems using solar panels under specific climatic conditions. The solar panel can produce the greatest power only at the specified voltage and electric current levels. Environmental variables, such as random atmospheric oscillations, have a significant effect on the performance of a solar system connected to the network. Irradiation and ambient temperature are the two inputs to a PV (photovoltaic) system. Because solar radiation varies in nature, the PV system efficiency is low. To improve the efficiency of a solar PV system, several maximum power point tracking (MPPT) approaches are used. The purpose of this paper is to improve the performance of DC/DC chopper controllers and PV inverters in the face of abrupt climate change. To that end, the primary goal of this paper is to compare the following maximum power point search (MPPT) algorithms: the incremental conductance (IC) algorithm, the fuzzy logic (FL) algorithm, VSI controller, and the particle swarm optimization (PSO) strategy. These algorithms were evaluated in terms of efficiency, stability, and speed. A 100 kW PV system is design using MATLAB software 2021a version.

Suggested Citation

  • Faouzi Didi & Moustafa Sahnoune Chaouche, 2022. "Design and Simulation of Grid-Connected Photovoltaic System’s Performance Analysis with Optimal Control of Maximum Power Point Tracking Based on Artificial Intelligence," Review of Computer Engineering Research, Conscientia Beam, vol. 9(3), pages 151-168.
    • Handle: RePEc:pkp:rocere:v:9:y:2022:i:3:p:151-168:id:3144
    as

    Download full text from publisher

    File URL: https://archive.conscientiabeam.com/index.php/76/article/view/3144/6935
    Download Restriction: no
    File URL: https://archive.conscientiabeam.com/index.php/76/article/view/3144/7060
    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:pkp:rocere:v:9:y:2022:i:3:p:151-168:id:3144. 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: Dim Michael (email available below). General contact details of provider: https://archive.conscientiabeam.com/index.php/76/ .

    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.