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Experimental analysis and dynamic modeling of a photovoltaic module with porous fins

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  • Selimefendigil, Fatih
  • Bayrak, Fatih
  • Oztop, Hakan F.

Abstract

In this study, experimental analysis and performance predictions of solar photovoltaic (PV) module equipped with porous fins were performed. The experimental setup was tested in Technology Faculty of Firat University, Elazig of Turkey which is located at 36° and 42° North latitudes. The PV module was oriented facing south and tilted to an angle of 36° with respect to the horizontal in order to maximize the solar radiation incident on the glass cover. Experimental analysis was conducted for configurations where PV module is equipped with porous metal foams. A multi-input multi-output dynamic system based on artificial neural networks was obtained for the PV configuration with and without fin by using the measured data (ambient temperature, PV panels back surface temperatures, current, voltage, radiation and wind velocity) from the experimental test rig. It was observed that adding porous fins to the PV module results in performance enhancements. The developed mathematical model based on dynamic neural networks can be used for further development and performance predictions of these systems.

Suggested Citation

  • Selimefendigil, Fatih & Bayrak, Fatih & Oztop, Hakan F., 2018. "Experimental analysis and dynamic modeling of a photovoltaic module with porous fins," Renewable Energy, Elsevier, vol. 125(C), pages 193-205.
  • Handle: RePEc:eee:renene:v:125:y:2018:i:c:p:193-205
    DOI: 10.1016/j.renene.2018.02.002
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    1. Voyant, Cyril & Notton, Gilles & Kalogirou, Soteris & Nivet, Marie-Laure & Paoli, Christophe & Motte, Fabrice & Fouilloy, Alexis, 2017. "Machine learning methods for solar radiation forecasting: A review," Renewable Energy, Elsevier, vol. 105(C), pages 569-582.
    2. Huang, Chao & Bensoussan, Alain & Edesess, Michael & Tsui, Kwok L., 2016. "Improvement in artificial neural network-based estimation of grid connected photovoltaic power output," Renewable Energy, Elsevier, vol. 97(C), pages 838-848.
    3. Magnus Nørgaard & Ole Ravn & Niels Kjølstad Poulsen, 2002. "NNSYSID-Toolbox for System Identification with Neural Networks," Mathematical and Computer Modelling of Dynamical Systems, Taylor & Francis Journals, vol. 8(1), pages 1-20, March.
    4. Mellit, A. & Benghanem, M. & Kalogirou, S.A., 2007. "Modeling and simulation of a stand-alone photovoltaic system using an adaptive artificial neural network: Proposition for a new sizing procedure," Renewable Energy, Elsevier, vol. 32(2), pages 285-313.
    5. Mellit, A. & Benghanem, M. & Kalogirou, S.A., 2006. "An adaptive wavelet-network model for forecasting daily total solar-radiation," Applied Energy, Elsevier, vol. 83(7), pages 705-722, July.
    6. Mellit, Adel & Kalogirou, Soteris A., 2011. "ANFIS-based modelling for photovoltaic power supply system: A case study," Renewable Energy, Elsevier, vol. 36(1), pages 250-258.
    7. Jordehi, A. Rezaee, 2016. "Parameter estimation of solar photovoltaic (PV) cells: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 61(C), pages 354-371.
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    2. Selimefendigil, Fatih & Öztop, Hakan F., 2020. "Identification of pulsating flow effects with CNT nanoparticles on the performance enhancements of thermoelectric generator (TEG) module in renewable energy applications," Renewable Energy, Elsevier, vol. 162(C), pages 1076-1086.
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    5. Bestas, Sukru & Aktas, Ilter Sahin & Bayrak, Fatih, 2024. "A bibliometric and performance evaluation of nano-PCM-integrated photovoltaic panels: Energy, exergy, environmental and sustainability perspectives," Renewable Energy, Elsevier, vol. 226(C).
    6. Gürbüz, Emine Yağız & Şahinkesen, İstemihan & Tuncer, Azim Doğuş & Keçebaş, Ali, 2023. "Design and experimental analysis of a parallel-flow photovoltaic-thermal air collector with finned latent heat thermal energy storage unit," Renewable Energy, Elsevier, vol. 217(C).
    7. Ahmad Al Aboushi & Eman Abdelhafez & Mohammad Hamdan, 2022. "Finned PV Natural Cooling Using Water-Based TiO 2 Nanofluid," Sustainability, MDPI, vol. 14(20), pages 1-16, October.
    8. Ahmad Manasrah & Mohammad Masoud & Yousef Jaradat & Piero Bevilacqua, 2022. "Investigation of a Real-Time Dynamic Model for a PV Cooling System," Energies, MDPI, vol. 15(5), pages 1-15, March.

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