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Comparison of four methods for parameter estimation of mono- and multi-junction photovoltaic devices using experimental data

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  • Peñaranda Chenche, Luz Elena
  • Hernandez Mendoza, Oscar Saul
  • Bandarra Filho, Enio Pedone

Abstract

The present work analyses four methods used to estimate the physical properties of photovoltaic devices for a single diode model. Two of the most efficient photovoltaic technologies—mono- and multi-junction devices—are used under different temperature and solar radiation conditions for comparing the applicability of each method. Three of the four parameter estimation methods are analytic and the remaining one uses an algorithm for the optimization of non-linear problems, i.e., the generalized reduced gradient. The different methods are summarized and a comparative analysis is performed using experimental data obtained from the literature, highlighting the advantages and disadvantages of each method. Criteria such as the mean absolute percentage error, the coefficient of determination, the absolute error in current calculated at the maximum power point, and computational cost are used. Accordingly, it is concluded that, for all the methods considered in this study, the best accuracy is obtained from simulations using the method proposed by Blas et al. [34] applied to mono-junction modules, and the method proposed by Xiao et al. [39] to multi-junction devices.

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  • Peñaranda Chenche, Luz Elena & Hernandez Mendoza, Oscar Saul & Bandarra Filho, Enio Pedone, 2018. "Comparison of four methods for parameter estimation of mono- and multi-junction photovoltaic devices using experimental data," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2823-2838.
  • Handle: RePEc:eee:rensus:v:81:y:2018:i:p2:p:2823-2838
    DOI: 10.1016/j.rser.2017.06.089
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    1. Ma, Tao & Yang, Hongxing & Lu, Lin, 2014. "Solar photovoltaic system modeling and performance prediction," Renewable and Sustainable Energy Reviews, Elsevier, vol. 36(C), pages 304-315.
    2. Dehghanzadeh, Ahmad & Farahani, Gholamreza & Maboodi, Mohsen, 2017. "A novel approximate explicit double-diode model of solar cells for use in simulation studies," Renewable Energy, Elsevier, vol. 103(C), pages 468-477.
    3. Bana, Sangram & Saini, R.P., 2017. "Identification of unknown parameters of a single diode photovoltaic model using particle swarm optimization with binary constraints," Renewable Energy, Elsevier, vol. 101(C), pages 1299-1310.
    4. 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.
    5. Lim, Li Hong Idris & Ye, Zhen & Ye, Jiaying & Yang, Dazhi & Du, Hui, 2015. "A linear method to extract diode model parameters of solar panels from a single I–V curve," Renewable Energy, Elsevier, vol. 76(C), pages 135-142.
    6. Jiang, Lian Lian & Maskell, Douglas L. & Patra, Jagdish C., 2013. "Parameter estimation of solar cells and modules using an improved adaptive differential evolution algorithm," Applied Energy, Elsevier, vol. 112(C), pages 185-193.
    7. 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.
    8. Ghani, F. & Rosengarten, G. & Duke, M. & Carson, J.K., 2014. "The numerical calculation of single-diode solar-cell modelling parameters," Renewable Energy, Elsevier, vol. 72(C), pages 105-112.
    9. Bastidas-Rodriguez, J.D. & Petrone, G. & Ramos-Paja, C.A. & Spagnuolo, G., 2017. "A genetic algorithm for identifying the single diode model parameters of a photovoltaic panel," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 131(C), pages 38-54.
    10. Almonacid, Florencia & Rodrigo, Pedro & Fernández, Eduardo F., 2016. "Determination of the current–voltage characteristics of concentrator systems by using different adapted conventional techniques," Energy, Elsevier, vol. 101(C), pages 146-160.
    11. 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.
    12. Senturk, A. & Eke, R., 2017. "A new method to simulate photovoltaic performance of crystalline silicon photovoltaic modules based on datasheet values," Renewable Energy, Elsevier, vol. 103(C), pages 58-69.
    13. Lo Brano, Valerio & Ciulla, Giuseppina, 2013. "An efficient analytical approach for obtaining a five parameters model of photovoltaic modules using only reference data," Applied Energy, Elsevier, vol. 111(C), pages 894-903.
    14. Orioli, Aldo & Di Gangi, Alessandra, 2013. "A procedure to calculate the five-parameter model of crystalline silicon photovoltaic modules on the basis of the tabular performance data," Applied Energy, Elsevier, vol. 102(C), pages 1160-1177.
    15. Ishaque, Kashif & Salam, Zainal & Mekhilef, Saad & Shamsudin, Amir, 2012. "Parameter extraction of solar photovoltaic modules using penalty-based differential evolution," Applied Energy, Elsevier, vol. 99(C), pages 297-308.
    16. Askarzadeh, Alireza & Rezazadeh, Alireza, 2013. "Artificial bee swarm optimization algorithm for parameters identification of solar cell models," Applied Energy, Elsevier, vol. 102(C), pages 943-949.
    17. 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.
    18. Boutana, N. & Mellit, A. & Lughi, V. & Massi Pavan, A., 2017. "Assessment of implicit and explicit models for different photovoltaic modules technologies," Energy, Elsevier, vol. 122(C), pages 128-143.
    19. 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.
    20. Chen, Xu & Yu, Kunjie & Du, Wenli & Zhao, Wenxiang & Liu, Guohai, 2016. "Parameters identification of solar cell models using generalized oppositional teaching learning based optimization," Energy, Elsevier, vol. 99(C), pages 170-180.
    21. 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.
    22. Rodrigo, P. & Fernández, E.F. & Almonacid, F. & Pérez-Higueras, P.J., 2013. "Models for the electrical characterization of high concentration photovoltaic cells and modules: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 26(C), pages 752-760.
    23. Humada, Ali M. & Hojabri, Mojgan & Mekhilef, Saad & Hamada, Hussein M., 2016. "Solar cell parameters extraction based on single and double-diode models: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 494-509.
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    2. Qais, Mohammed H. & Hasanien, Hany M. & Alghuwainem, Saad, 2020. "Parameters extraction of three-diode photovoltaic model using computation and Harris Hawks optimization," Energy, Elsevier, vol. 195(C).
    3. Papul Changmai & Sunil Deka & Shashank Kumar & Thanikanti Sudhakar Babu & Belqasem Aljafari & Benedetto Nastasi, 2022. "A Critical Review on the Estimation Techniques of the Solar PV Cell’s Unknown Parameters," Energies, MDPI, vol. 15(19), pages 1-20, September.
    4. Yousri, Dalia & Thanikanti, Sudhakar Babu & Allam, Dalia & Ramachandaramurthy, Vigna K. & Eteiba, M.B., 2020. "Fractional chaotic ensemble particle swarm optimizer for identifying the single, double, and three diode photovoltaic models’ parameters," Energy, Elsevier, vol. 195(C).
    5. Efstratios Batzelis, 2019. "Non-Iterative Methods for the Extraction of the Single-Diode Model Parameters of Photovoltaic Modules: A Review and Comparative Assessment," Energies, MDPI, vol. 12(3), pages 1-26, January.

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