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Comparison of two PV array models for the simulation of PV systems using five different algorithms for the parameters identification

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  • Kichou, Sofiane
  • Silvestre, Santiago
  • Guglielminotti, Letizia
  • Mora-López, Llanos
  • Muñoz-Cerón, Emilio

Abstract

Simulation is of primal importance in the prediction of the produced power and automatic fault detection in PV grid-connected systems (PVGCS). The accuracy of simulation results depends on the models used for main components of the PV system, especially for the PV module. The present paper compares two PV array models, the five-parameter model (5PM) and the Sandia Array Performance Model (SAPM). Five different algorithms are used for estimating the unknown parameters of both PV models in order to see how they affect the accuracy of simulations in reproducing the outdoor behavior of three PVGCS. The arrays of the PVGCS are of three different PV module technologies: Crystalline silicon (c-Si), amorphous silicon (a-Si:H) and micromorph silicon (a-Si:H/μc-Si:H).

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  • Kichou, Sofiane & Silvestre, Santiago & Guglielminotti, Letizia & Mora-López, Llanos & Muñoz-Cerón, Emilio, 2016. "Comparison of two PV array models for the simulation of PV systems using five different algorithms for the parameters identification," Renewable Energy, Elsevier, vol. 99(C), pages 270-279.
  • Handle: RePEc:eee:renene:v:99:y:2016:i:c:p:270-279
    DOI: 10.1016/j.renene.2016.07.002
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    1. Ciulla, Giuseppina & Lo Brano, Valerio & Di Dio, Vincenzo & Cipriani, Giovanni, 2014. "A comparison of different one-diode models for the representation of I–V characteristic of a PV cell," Renewable and Sustainable Energy Reviews, Elsevier, vol. 32(C), pages 684-696.
    2. de Blas, M.A & Torres, J.L & Prieto, E & Garcı́a, A, 2002. "Selecting a suitable model for characterizing photovoltaic devices," Renewable Energy, Elsevier, vol. 25(3), pages 371-380.
    3. Celik, Ali Naci & Acikgoz, NasIr, 2007. "Modelling and experimental verification of the operating current of mono-crystalline photovoltaic modules using four- and five-parameter models," Applied Energy, Elsevier, vol. 84(1), pages 1-15, January.
    4. Lineykin, Simon & Averbukh, Moshe & Kuperman, Alon, 2014. "An improved approach to extract the single-diode equivalent circuit parameters of a photovoltaic cell/panel," Renewable and Sustainable Energy Reviews, Elsevier, vol. 30(C), pages 282-289.
    5. Silvestre, Santiago & Kichou, Sofiane & Chouder, Aissa & Nofuentes, Gustavo & Karatepe, Engin, 2015. "Analysis of current and voltage indicators in grid connected PV (photovoltaic) systems working in faulty and partial shading conditions," Energy, Elsevier, vol. 86(C), pages 42-50.
    6. Oliva, Diego & Cuevas, Erik & Pajares, Gonzalo, 2014. "Parameter identification of solar cells using artificial bee colony optimization," Energy, Elsevier, vol. 72(C), pages 93-102.
    7. AlHajri, M.F. & El-Naggar, K.M. & AlRashidi, M.R. & Al-Othman, A.K., 2012. "Optimal extraction of solar cell parameters using pattern search," Renewable Energy, Elsevier, vol. 44(C), pages 238-245.
    8. Peng, Jinqing & Lu, Lin & Yang, Hongxing & Ma, Tao, 2015. "Validation of the Sandia model with indoor and outdoor measurements for semi-transparent amorphous silicon PV modules," Renewable Energy, Elsevier, vol. 80(C), pages 316-323.
    9. Kichou, Sofiane & Silvestre, Santiago & Nofuentes, Gustavo & Torres-Ramírez, Miguel & Chouder, Aissa & Guasch, Daniel, 2016. "Characterization of degradation and evaluation of model parameters of amorphous silicon photovoltaic modules under outdoor long term exposure," Energy, Elsevier, vol. 96(C), pages 231-241.
    10. Chine, W. & Mellit, A. & Lughi, V. & Malek, A. & Sulligoi, G. & Massi Pavan, A., 2016. "A novel fault diagnosis technique for photovoltaic systems based on artificial neural networks," Renewable Energy, Elsevier, vol. 90(C), pages 501-512.
    11. Muhsen, Dhiaa Halboot & Ghazali, Abu Bakar & Khatib, Tamer & Abed, Issa Ahmed, 2016. "A comparative study of evolutionary algorithms and adapting control parameters for estimating the parameters of a single-diode photovoltaic module's model," Renewable Energy, Elsevier, vol. 96(PA), pages 377-389.
    12. Alberto Dolara & Francesco Grimaccia & Sonia Leva & Marco Mussetta & Emanuele Ogliari, 2015. "A Physical Hybrid Artificial Neural Network for Short Term Forecasting of PV Plant Power Output," Energies, MDPI, vol. 8(2), pages 1-16, February.
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    7. van Vuuren, Dirk Johan & Marnewick, Annlizé & Pretorius, Jan Harm C., 2019. "A proposed simulation-based theoretical preconstruction process: The case of solar photovoltaic technology in South African shopping centres," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
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