IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v125y2018icp193-205.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148118301381
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.renene.2018.02.002?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Al-Amri, Fahad & Saeed, Farooq & Mujeebu, Muhammad Abdul, 2022. "Novel dual-function racking structure for passive cooling of solar PV panels –thermal performance analysis," Renewable Energy, Elsevier, vol. 198(C), pages 100-113.
    2. 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.
    3. Fatih Selimefendigil & Damla Okulu & Hakan F. Öztop, 2023. "Photovoltaic Thermal Management by Combined Utilization of Thermoelectric Generator and Power-Law-Nanofluid-Assisted Cooling Channel," Sustainability, MDPI, vol. 15(6), pages 1-29, March.
    4. 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.
    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. Choi, Hwiung & Choi, Kwanghwan, 2022. "Parametric study of a novel air-based photovoltaic-thermal collector with a transverse triangular-shaped block," Renewable Energy, Elsevier, vol. 201(P1), pages 96-110.
    8. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jha, Sunil Kr. & Bilalovic, Jasmin & Jha, Anju & Patel, Nilesh & Zhang, Han, 2017. "Renewable energy: Present research and future scope of Artificial Intelligence," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 297-317.
    2. Zendehboudi, Sohrab & Rezaei, Nima & Lohi, Ali, 2018. "Applications of hybrid models in chemical, petroleum, and energy systems: A systematic review," Applied Energy, Elsevier, vol. 228(C), pages 2539-2566.
    3. A. Bassam & O. May Tzuc & M. Escalante Soberanis & L. J. Ricalde & B. Cruz, 2017. "Temperature Estimation for Photovoltaic Array Using an Adaptive Neuro Fuzzy Inference System," Sustainability, MDPI, vol. 9(8), pages 1-16, August.
    4. Ping-Huan Kuo & Chiou-Jye Huang, 2018. "A Green Energy Application in Energy Management Systems by an Artificial Intelligence-Based Solar Radiation Forecasting Model," Energies, MDPI, vol. 11(4), pages 1-15, April.
    5. Marzouq, Manal & El Fadili, Hakim & Zenkouar, Khalid & Lakhliai, Zakia & Amouzg, Mohammed, 2020. "Short term solar irradiance forecasting via a novel evolutionary multi-model framework and performance assessment for sites with no solar irradiance data," Renewable Energy, Elsevier, vol. 157(C), pages 214-231.
    6. Jesús Ferrero Bermejo & Juan Francisco Gómez Fernández & Rafael Pino & Adolfo Crespo Márquez & Antonio Jesús Guillén López, 2019. "Review and Comparison of Intelligent Optimization Modelling Techniques for Energy Forecasting and Condition-Based Maintenance in PV Plants," Energies, MDPI, vol. 12(21), pages 1-18, October.
    7. Dimri, Neha & Tiwari, Arvind & Tiwari, G.N., 2019. "Comparative study of photovoltaic thermal (PVT) integrated thermoelectric cooler (TEC) fluid collectors," Renewable Energy, Elsevier, vol. 134(C), pages 343-356.
    8. Himri, Y. & Malik, Arif S. & Boudghene Stambouli, A. & Himri, S. & Draoui, B., 2009. "Review and use of the Algerian renewable energy for sustainable development," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(6-7), pages 1584-1591, August.
    9. Ceylin Şirin & Fatih Selimefendigil & Hakan Fehmi Öztop, 2023. "Performance Analysis and Identification of an Indirect Photovoltaic Thermal Dryer with Aluminum Oxide Nano-Embedded Thermal Energy Storage Modification," Sustainability, MDPI, vol. 15(3), pages 1-27, January.
    10. Wang, Gang & Zhao, Ke & Shi, Jiangtao & Chen, Wei & Zhang, Haiyang & Yang, Xinsheng & Zhao, Yong, 2017. "An iterative approach for modeling photovoltaic modules without implicit equations," Applied Energy, Elsevier, vol. 202(C), pages 189-198.
    11. Wang, Gang & Zhao, Ke & Qiu, Tian & Yang, Xinsheng & Zhang, Yong & Zhao, Yong, 2016. "The error analysis of the reverse saturation current of the diode in the modeling of photovoltaic modules," Energy, Elsevier, vol. 115(P1), pages 478-485.
    12. Hwang, Jun Kwon & Yun, Geun Young & Lee, Sukho & Seo, Hyeongjoon & Santamouris, Mat, 2020. "Using deep learning approaches with variable selection process to predict the energy performance of a heating and cooling system," Renewable Energy, Elsevier, vol. 149(C), pages 1227-1245.
    13. Apfel, Dorothee & Haag, Steffen & Herbes, Carsten, 2021. "Research agendas on renewable energies in the Global South: A systematic literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 148(C).
    14. Voyant, Cyril & Motte, Fabrice & Notton, Gilles & Fouilloy, Alexis & Nivet, Marie-Laure & Duchaud, Jean-Laurent, 2018. "Prediction intervals for global solar irradiation forecasting using regression trees methods," Renewable Energy, Elsevier, vol. 126(C), pages 332-340.
    15. Trigo-González, Mauricio & Batlles, F.J. & Alonso-Montesinos, Joaquín & Ferrada, Pablo & del Sagrado, J. & Martínez-Durbán, M. & Cortés, Marcelo & Portillo, Carlos & Marzo, Aitor, 2019. "Hourly PV production estimation by means of an exportable multiple linear regression model," Renewable Energy, Elsevier, vol. 135(C), pages 303-312.
    16. Pedro, Hugo T.C. & Lim, Edwin & Coimbra, Carlos F.M., 2018. "A database infrastructure to implement real-time solar and wind power generation intra-hour forecasts," Renewable Energy, Elsevier, vol. 123(C), pages 513-525.
    17. Boland, John, 2015. "Spatial-temporal forecasting of solar radiation," Renewable Energy, Elsevier, vol. 75(C), pages 607-616.
    18. Agga, Ali & Abbou, Ahmed & Labbadi, Moussa & El Houm, Yassine, 2021. "Short-term self consumption PV plant power production forecasts based on hybrid CNN-LSTM, ConvLSTM models," Renewable Energy, Elsevier, vol. 177(C), pages 101-112.
    19. Mousavi, Navid & Kothapalli, Ganesh & Habibi, Daryoush & Das, Choton K. & Baniasadi, Ali, 2020. "A novel photovoltaic-pumped hydro storage microgrid applicable to rural areas," Applied Energy, Elsevier, vol. 262(C).
    20. Wang, Zhenyu & Zhang, Yunpeng & Li, Guorong & Zhang, Jinlong & Zhou, Hai & Wu, Ji, 2024. "A novel solar irradiance forecasting method based on multi-physical process of atmosphere optics and LSTM-BP model," Renewable Energy, Elsevier, vol. 226(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:renene:v:125:y:2018:i:c:p:193-205. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/renewable-energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.