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Extraction of the PV modules parameters with MPP estimation using the modified flower algorithm

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  • Benkercha, Rabah
  • Moulahoum, Samir
  • Taghezouit, Bilal

Abstract

Modeling of photovoltaic (PV) module remains a serious issue for a lot of applications such as monitoring system or fault detection system. Therefore, several equivalents models of the PV cell have been proposed, the famous proposed models are called the single diode model (SDM) and double diode model (DDM). Each model possesses unknown parameters values which must be defined. In the present paper, two electrical models equivalent to PV cell are proposed, these models have an unknowns parameters which must be identified. The modified flower algorithm (MFA) is an optimization algorithm inspired from the nature, this algorithm is used to extract the optimal parameters values for both models. The proposed algorithm mimics the pathways of pollen transfer to help produce plants in nature, in other words, there are a lot of ways that the pollen can be travel to reproduce the plants these ways can be developed to a powerful optimization algorithm. In order to assess the proposed algorithm, several experimental data are used, these data are acquired in outdoors conditions and contains various I-V curves, these I-V curves are taken from three kind of PV cell technologies which namely Monocrystalline, Polycrystalline and Amorphous. In addition, the simulation results are compared with experimental data for both models. Moreover, the identified SDM parameters are applied to predict the current, the voltage and the power at the maximum power point (MPP) which was then compared to the MPP obtained from real data of grid connected photovoltaic system (GCPVS).

Suggested Citation

  • Benkercha, Rabah & Moulahoum, Samir & Taghezouit, Bilal, 2019. "Extraction of the PV modules parameters with MPP estimation using the modified flower algorithm," Renewable Energy, Elsevier, vol. 143(C), pages 1698-1709.
    • Handle: RePEc:eee:renene:v:143:y:2019:i:c:p:1698-1709
    DOI: 10.1016/j.renene.2019.05.107
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    References listed on IDEAS

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    6. Mohamed Abdel-Basset & Reda Mohamed & Ripon K. Chakrabortty & Michael J. Ryan & Attia El-Fergany, 2021. "An Improved Artificial Jellyfish Search Optimizer for Parameter Identification of Photovoltaic Models," Energies, MDPI, vol. 14(7), pages 1-33, March.
    7. Samuel R. Fahim & Hany M. Hasanien & Rania A. Turky & Shady H. E. Abdel Aleem & Martin Ćalasan, 2022. "A Comprehensive Review of Photovoltaic Modules Models and Algorithms Used in Parameter Extraction," Energies, MDPI, vol. 15(23), pages 1-56, November.
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