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A single procedure for helping PV designers to select silicon PV modules and evaluate the loss resistances

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  • Carrero, C.
  • Amador, J.
  • Arnaltes, S.

Abstract

When a photovoltaic system is to be sized, different PV modules are considered. The optimisation of such systems is always the goal, but the choice of the PV module with best performance should also be considered. Nevertheless, selecting a module from catalogue data has certain inconveniences. First, because those data allow only comparisons with absolute magnitudes, the conclusions about which module is the most appropriate is not easy. Second, data provided in catalogues are not sufficient to know the module behaviour under conditions different from standard. This paper deals with the normalisation of the modules data by considering a base that allows for obtaining a “per unit” representation. For modelling and studying the modules under non-standard conditions it is necessary to know series and shunt resistances, but that is not easy. Then, by simulations, it is possible to show the influence of these resistances in the module behaviour.

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  • Carrero, C. & Amador, J. & Arnaltes, S., 2007. "A single procedure for helping PV designers to select silicon PV modules and evaluate the loss resistances," Renewable Energy, Elsevier, vol. 32(15), pages 2579-2589.
    • Handle: RePEc:eee:renene:v:32:y:2007:i:15:p:2579-2589
    DOI: 10.1016/j.renene.2007.01.001
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    5. Carrero, C. & Rodríguez, J. & Ramírez, D. & Platero, C., 2010. "Simple estimation of PV modules loss resistances for low error modelling," Renewable Energy, Elsevier, vol. 35(5), pages 1103-1108.
    6. 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.
    7. Dong, Xiao-Jian & Shen, Jia-Ni & He, Guo-Xin & Ma, Zi-Feng & He, Yi-Jun, 2021. "A general radial basis function neural network assisted hybrid modeling method for photovoltaic cell operating temperature prediction," Energy, Elsevier, vol. 234(C).
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    9. Ustaoglu, Abid & Ozbey, Umut & Torlaklı, Hande, 2020. "Numerical investigation of concentrating photovoltaic/thermal (CPV/T) system using compound hyperbolic –trumpet, V-trough and compound parabolic concentrators," Renewable Energy, Elsevier, vol. 152(C), pages 1192-1208.
    10. Chin, Vun Jack & Salam, Zainal & Ishaque, Kashif, 2015. "Cell modelling and model parameters estimation techniques for photovoltaic simulator application: A review," Applied Energy, Elsevier, vol. 154(C), pages 500-519.
    11. Ayang, Albert & Wamkeue, René & Ouhrouche, Mohand & Djongyang, Noël & Essiane Salomé, Ndjakomo & Pombe, Joseph Kessel & Ekemb, Gabriel, 2019. "Maximum likelihood parameters estimation of single-diode model of photovoltaic generator," Renewable Energy, Elsevier, vol. 130(C), pages 111-121.
    12. Saheli, Mahdieh Arabzadeh & Lari, Kamran & Salehi, Gholamreza & Azad, Masoud Torabi, 2022. "Techno-economic assessment of a hybrid on grid PV-wave system: A case study in Caspian Sea," Renewable Energy, Elsevier, vol. 186(C), pages 596-608.
    13. Wang, Meng & Peng, Jinqing & Luo, Yimo & Shen, Zhicheng & Yang, Hongxing, 2021. "Comparison of different simplistic prediction models for forecasting PV power output: Assessment with experimental measurements," Energy, Elsevier, vol. 224(C).
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