IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i18p4775-d412957.html
   My bibliography  Save this article

An Intelligent Automatic Adaptive Maximum Power Point Tracker for Photovoltaic Module Arrays

Author

Listed:
  • Kuei-Hsiang Chao

    (Department of Electrical Engineering, National Chin-Yi University of Technology, Taichung 41170, Taiwan)

  • Yu-Ju Lai

    (Department of Electrical Engineering, National Chin-Yi University of Technology, Taichung 41170, Taiwan)

Abstract

In this study, a maximum power point tracker was developed for photovoltaic module arrays by using a teacher-learning-based optimization (TLBO) algorithm to control the photovoltaic system. When a photovoltaic module array is shaded, a conventional maximum power point tracker may obtain the local maximum power point rather than the global maximum power point. The tracker developed in this study was aimed at solving this problem. To prove the viability of the proposed method, a SANYO HIP 2717 photovoltaic module with diverse connection patterns and shading ratios was used. Thus, single-peak, double-peak, triple-peak, and multi-peak power–voltage characteristic curves of the photovoltaic module array were obtained. A simulation of maximum power point tracking (MPPT) was then performed with MATLAB software. With regard to practical testing, a boost converter was used as the hardware structure of the maximum power point tracker and a TMS320F2808 digital signal processor was selected to execute the rules for MPPT. The results of the practical tests verified that the proposed improved TLBO algorithm had a superior accuracy to existing TLBO algorithms. In addition, the proposed improved TLBO algorithm can shorten the tracking time to 1/2 or 1/4, so it can improve the efficiency of power generation by two to three percentage.

Suggested Citation

  • Kuei-Hsiang Chao & Yu-Ju Lai, 2020. "An Intelligent Automatic Adaptive Maximum Power Point Tracker for Photovoltaic Module Arrays," Energies, MDPI, vol. 13(18), pages 1-24, September.
  • Handle: RePEc:gam:jeners:v:13:y:2020:i:18:p:4775-:d:412957
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/18/4775/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/13/18/4775/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kuei-Hsiang Chao & Meng-Cheng Wu, 2016. "Global Maximum Power Point Tracking (MPPT) of a Photovoltaic Module Array Constructed through Improved Teaching-Learning-Based Optimization," Energies, MDPI, vol. 9(12), pages 1-18, November.
    Full references (including those not matched with items on IDEAS)

    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. Rezk, Hegazy & AL-Oran, Mazen & Gomaa, Mohamed R. & Tolba, Mohamed A. & Fathy, Ahmed & Abdelkareem, Mohammad Ali & Olabi, A.G. & El-Sayed, Abou Hashema M., 2019. "A novel statistical performance evaluation of most modern optimization-based global MPPT techniques for partially shaded PV system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 115(C).
    2. Diego R. Espinoza Trejo & Ernesto Bárcenas & José E. Hernández Díez & Guillermo Bossio & Gerardo Espinosa Pérez, 2018. "Open- and Short-Circuit Fault Identification for a Boost dc/dc Converter in PV MPPT Systems," Energies, MDPI, vol. 11(3), pages 1-15, March.
    3. Ahmed Al Mansur & Md. Ruhul Amin & Kazi Khairul Islam, 2019. "Performance Comparison of Mismatch Power Loss Minimization Techniques in Series-Parallel PV Array Configurations," Energies, MDPI, vol. 12(5), pages 1-21, March.
    4. Dalia Yousri & Thanikanti Sudhakar Babu & Dalia Allam & Vigna. K. Ramachandaramurthy & Eman Beshr & Magdy. B. Eteiba, 2019. "Fractional Chaos Maps with Flower Pollination Algorithm for Partial Shading Mitigation of Photovoltaic Systems," Energies, MDPI, vol. 12(18), pages 1-27, September.

    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:18:p:4775-:d:412957. 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.