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A Hysteresis Model for Fixed and Sun Tracking Solar PV Power Generation Systems

Author

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  • Ümmühan Başaran Filik

    (Department of Electrical and Electronics Engineering, Anadolu University, TR-26555 Eskişehir, Turkey)

  • Tansu Filik

    (Department of Electrical and Electronics Engineering, Anadolu University, TR-26555 Eskişehir, Turkey)

  • Ömer Nezih Gerek

    (Department of Electrical and Electronics Engineering, Anadolu University, TR-26555 Eskişehir, Turkey)

Abstract

In this study, a new solar photovoltaic (PV) panel output power model is proposed. The model is constructed as a function of ambient temperature and solar radiations for two types (fixed panel and sun tracking panel) of PV systems. The proposed models are tested and verified on the Renewable Energy Research Home (RERH) system that was installed at the Anadolu University campus in Eskişehir, Turkey. The model is deliberately constructed for the winter season, where cloudliness, rain and snow constitute more challenging conditions for modeling. The developed model outcomes are compared to the outputs of state of the art methods that use global solar radiation and temperature data. A total of eight algebraic models are constructed for the purpose of depicting the solar radiation-to-electric power behavior. It is observed that even the least successful one of these eight variants are performing better than the most accurate method in the literature. It is argued that mathematical incorporation of the proposed novel hysteresis functions to the solar radiation-to-power conversion curves results in a richer class of functions and causes a significant accuracy improvement on the mathematical power generation model, even for the most challenging season of winter.

Suggested Citation

  • Ümmühan Başaran Filik & Tansu Filik & Ömer Nezih Gerek, 2018. "A Hysteresis Model for Fixed and Sun Tracking Solar PV Power Generation Systems," Energies, MDPI, vol. 11(3), pages 1-15, March.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:3:p:603-:d:135432
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    References listed on IDEAS

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    1. Emanuele Ogliari & Francesco Grimaccia & Sonia Leva & Marco Mussetta, 2013. "Hybrid Predictive Models for Accurate Forecasting in PV Systems," Energies, MDPI, vol. 6(4), pages 1-12, April.
    2. Verma, Deepak & Nema, Savita & Shandilya, A.M. & Dash, Soubhagya K., 2016. "Maximum power point tracking (MPPT) techniques: Recapitulation in solar photovoltaic systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 54(C), pages 1018-1034.
    3. Claudio Monteiro & Tiago Santos & L. Alfredo Fernandez-Jimenez & Ignacio J. Ramirez-Rosado & M. Sonia Terreros-Olarte, 2013. "Short-Term Power Forecasting Model for Photovoltaic Plants Based on Historical Similarity," Energies, MDPI, vol. 6(5), pages 1-20, May.
    4. Fernandez-Jimenez, L. Alfredo & Muñoz-Jimenez, Andrés & Falces, Alberto & Mendoza-Villena, Montserrat & Garcia-Garrido, Eduardo & Lara-Santillan, Pedro M. & Zorzano-Alba, Enrique & Zorzano-Santamaria,, 2012. "Short-term power forecasting system for photovoltaic plants," Renewable Energy, Elsevier, vol. 44(C), pages 311-317.
    5. Honglu Zhu & Xu Li & Qiao Sun & Ling Nie & Jianxi Yao & Gang Zhao, 2015. "A Power Prediction Method for Photovoltaic Power Plant Based on Wavelet Decomposition and Artificial Neural Networks," Energies, MDPI, vol. 9(1), pages 1-15, December.
    6. 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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    Cited by:

    1. João Gomes, 2019. "Assessment of the Impact of Stagnation Temperatures in Receiver Prototypes of C-PVT Collectors," Energies, MDPI, vol. 12(15), pages 1-20, August.

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