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A novel algorithm for maximum power point tracking using computer vision (CVMPPT)

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  • Morteza Ahmadi
  • Masoud Abrari
  • Majid Ghanaatshoar
  • Ali Khalafi

Abstract

The behavior of an illuminated solar module can be characterized by its power-voltage curve. Tracking the peak of this curve is essential to harvest the maximum power by the module. The position of the peak varies with temperature and irradiance and needs to be traced. Under partial shading conditions, the number of peaks increases and makes it more difficult to find the global maximum power point (MPP). Various methods are used for maximum power point tracking (MPPT) that are based on iterations. These methods are time-consuming and fail to work satisfactorily under rapidly changing environmental conditions. In this paper, a novel algorithm is proposed that for the first time, utilizes computer vision to find the global maximum power point. This algorithm, which is implemented in Matlab/Simulink, is free of voltage iterations and gives the real-time data for the maximum power point. The proposed algorithm increases the speed and the reliability of the MPP tracking via replacing analogue electronics calculations by digital means. The validity of the algorithm is experimentally verified.

Suggested Citation

  • Morteza Ahmadi & Masoud Abrari & Majid Ghanaatshoar & Ali Khalafi, 2024. "A novel algorithm for maximum power point tracking using computer vision (CVMPPT)," PLOS ONE, Public Library of Science, vol. 19(4), pages 1-16, April.
    • Handle: RePEc:plo:pone00:0301363
    DOI: 10.1371/journal.pone.0301363
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    References listed on IDEAS

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    1. Sampath Kumar Vankadara & Shamik Chatterjee & Praveen Kumar Balachandran & Lucian Mihet-Popa, 2022. "Marine Predator Algorithm (MPA)-Based MPPT Technique for Solar PV Systems under Partial Shading Conditions," Energies, MDPI, vol. 15(17), pages 1-16, August.
    2. Kuei-Hsiang Chao & Muhammad Nursyam Rizal, 2021. "A Hybrid MPPT Controller Based on the Genetic Algorithm and Ant Colony Optimization for Photovoltaic Systems under Partially Shaded Conditions," Energies, MDPI, vol. 14(10), pages 1-17, May.
    3. Jordehi, A. Rezaee, 2016. "Maximum power point tracking in photovoltaic (PV) systems: A review of different approaches," Renewable and Sustainable Energy Reviews, Elsevier, vol. 65(C), pages 1127-1138.
    4. Calcabrini, Andres & Muttillo, Mirco & Weegink, Raoul & Manganiello, Patrizio & Zeman, Miro & Isabella, Olindo, 2021. "A fully reconfigurable series-parallel photovoltaic module for higher energy yields in urban environments," Renewable Energy, Elsevier, vol. 179(C), pages 1-11.
    5. Srinivasan Alwar & Devakirubakaran Samithas & Meenakshi Sundaram Boominathan & Praveen Kumar Balachandran & Lucian Mihet-Popa, 2022. "Performance Analysis of Thermal Image Processing-Based Photovoltaic Fault Detection and PV Array Reconfiguration—A Detailed Experimentation," Energies, MDPI, vol. 15(22), pages 1-21, November.
    6. Humada, Ali M. & Hojabri, Mojgan & Mekhilef, Saad & Hamada, Hussein M., 2016. "Solar cell parameters extraction based on single and double-diode models: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 494-509.
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