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A voltage scanning-based MPPT method for PV power systems under complex partial shading conditions

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  • Celikel, Resat
  • Yilmaz, Musa
  • Gundogdu, Ahmet

Abstract

Maximum Power Point Tracking (MPPT) algorithms have developed to minimise the effect of the atmospheric changes. Partial shading conditions (PSCs) are an important cause of MPP changes in photovoltaic systems. In this study, the maximum power point of a PV system was found within a short period in a complex PSC by using voltage scanning. This proposed method can be used in simple and low-level microprocessors. An ever-increasing reference voltage in the proposed method was used to adjust the output voltage of the panels. MP points forming during the voltage increase were recorded and the largest value was used. Thus, a simple algorithm was formed by using the sample-hold blocks. This method was under six PS scenarios on a PV system (2 kW) consisting of eight panels that were formed in a MATLAB/Simulink environment. We then compared voltage scanning method with Cuckoo Search (CS) algorithm (an optimisation method) and presented the findings in detail. The proposed algorithm reached maximum power of 99.84% in the third scenario, and 99.9% in the other scenarios. The global MPP capture times in all six scenarios were between 0.078 and 0.262 s. This value is quite good compared to the CS algorithm.

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  • Celikel, Resat & Yilmaz, Musa & Gundogdu, Ahmet, 2022. "A voltage scanning-based MPPT method for PV power systems under complex partial shading conditions," Renewable Energy, Elsevier, vol. 184(C), pages 361-373.
    • Handle: RePEc:eee:renene:v:184:y:2022:i:c:p:361-373
    DOI: 10.1016/j.renene.2021.11.098
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    References listed on IDEAS

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    1. Larbes, C. & Aït Cheikh, S.M. & Obeidi, T. & Zerguerras, A., 2009. "Genetic algorithms optimized fuzzy logic control for the maximum power point tracking in photovoltaic system," Renewable Energy, Elsevier, vol. 34(10), pages 2093-2100.
    2. Punitha, K. & Devaraj, D. & Sakthivel, S., 2013. "Artificial neural network based modified incremental conductance algorithm for maximum power point tracking in photovoltaic system under partial shading conditions," Energy, Elsevier, vol. 62(C), pages 330-340.
    3. Rizzo, Santi Agatino & Scelba, Giacomo, 2015. "ANN based MPPT method for rapidly variable shading conditions," Applied Energy, Elsevier, vol. 145(C), pages 124-132.
    4. Belhachat, Faiza & Larbes, Cherif, 2017. "Global maximum power point tracking based on ANFIS approach for PV array configurations under partial shading conditions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 875-889.
    5. Hashemzadeh, Seyed Majid, 2019. "A new model-based technique for fast and accurate tracking of global maximum power point in photovoltaic arrays under partial shading conditions," Renewable Energy, Elsevier, vol. 139(C), pages 1061-1076.
    6. Pal, Rudra Sankar & Mukherjee, V., 2020. "Metaheuristic based comparative MPPT methods for photovoltaic technology under partial shading condition," Energy, Elsevier, vol. 212(C).
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