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

Morteza Ahmadi, Masoud Abrari, Majid Ghanaatshoar and Ali Khalafi

PLOS ONE, 2024, vol. 19, issue 4, 1-16

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.

Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0301363

DOI: 10.1371/journal.pone.0301363

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