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A Neural Network-Based Rapid Maximum Power Point Tracking Method for Photovoltaic Systems in Partial Shading Conditions

  • DIRECT CONVERSION OF SOLAR ENERGY INTO ELECTRICAL ENERGY
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Abstract

The maximum power point tracking (MPPT) controller holds an important role in increasing the efficiency of the photovoltaic (PV) system. However, conventional MPPT techniques may fail to locate the global maximum power point (GMPP) under partial shading conditions (PSC). Hence, to optimize the efficiency of PV systems, we introduce a new MPPT technique for PSC. The proposed method employs an artificial neural network (ANN) to predict the area of the GMPP, and the classic perturb and observe (P&O) algorithm to locate the exact position of the GMPP. We validated the effectiveness of the technique using computer simulations performed with the MATLAB/Simulink program, the results of which verified that it can track the GMPP faster than other methods.

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ACKNOWLEDGMENTS

This study is supported by the Hitachi Global Foundation as the scholarship provider for the first author. The authors express their sincere appreciation to this organization.

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Authors and Affiliations

  1. Department of Marine Engineering, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia

    Adi Kurniawan

  2. Department of Transportation and Environmental Systems, Hiroshima University, Hiroshima, Japan

    Adi Kurniawan &  Eiji Shintaku

Authors
  1. Adi Kurniawan
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  2. Eiji Shintaku
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Correspondence to Adi Kurniawan.

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Adi Kurniawan, Eiji Shintaku A Neural Network-Based Rapid Maximum Power Point Tracking Method for Photovoltaic Systems in Partial Shading Conditions. Appl. Sol. Energy 56, 157–167 (2020). https://doi.org/10.3103/S0003701X20030068

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  • DOI: https://doi.org/10.3103/S0003701X20030068

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