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A simple behavioural model for solar module electric characteristics based on the first order system step response for MPPT study and comparison

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  • Amrouche, Badia
  • Guessoum, Abderrezak
  • Belhamel, Maiouf

Abstract

This paper proposes a simple behavioural model for photovoltaic modules. This model can be used to characterise current–voltage and power–voltage outputs of photovoltaic modules as a function of solar module temperature and solar radiation intensity. Such a model cannot only serve as a tool to study the I–V curve and its maximum power point characteristics but also to design photovoltaic power systems and power converters used for PV applications. It can also be used for performance rating. This model has first been developed to study the maximum power point characteristics by exploring the existing similarity between the photovoltaic module I–V characteristic and the step response of a first-order system. It has the advantage to use only parameters that are available on the data sheet. To construct the proposed model, measured I–V curves at different working conditions (solar radiation intensity and ambient temperature) were used, then other I–V characteristics corresponding to different working conditions have been used to validate it. The obtained results show a high degree of correspondence between the real outdoor measured I–V characteristics and those given by the developed model.

Suggested Citation

  • Amrouche, Badia & Guessoum, Abderrezak & Belhamel, Maiouf, 2012. "A simple behavioural model for solar module electric characteristics based on the first order system step response for MPPT study and comparison," Applied Energy, Elsevier, vol. 91(1), pages 395-404.
    • Handle: RePEc:eee:appene:v:91:y:2012:i:1:p:395-404
    DOI: 10.1016/j.apenergy.2011.09.036
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    1. Celik, Ali Naci & Acikgoz, NasIr, 2007. "Modelling and experimental verification of the operating current of mono-crystalline photovoltaic modules using four- and five-parameter models," Applied Energy, Elsevier, vol. 84(1), pages 1-15, January.
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    Cited by:

    1. Chellaswamy, C. & Ramesh, R., 2016. "Parameter extraction of solar cell models based on adaptive differential evolution algorithm," Renewable Energy, Elsevier, vol. 97(C), pages 823-837.
    2. Chi-Jeng Bai & Wei-Cheng Wang & Po-Wei Chen & Wen-Tong Chong, 2014. "System Integration of the Horizontal-Axis Wind Turbine: The Design of Turbine Blades with an Axial-Flux Permanent Magnet Generator," Energies, MDPI, vol. 7(11), pages 1-21, November.
    3. Sánchez Reinoso, Carlos R. & Milone, Diego H. & Buitrago, Román H., 2013. "Simulation of photovoltaic centrals with dynamic shading," Applied Energy, Elsevier, vol. 103(C), pages 278-289.
    4. 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.
    5. Fathabadi, Hassan, 2015. "Lambert W function-based technique for tracking the maximum power point of PV modules connected in various configurations," Renewable Energy, Elsevier, vol. 74(C), pages 214-226.
    6. Salam, Zainal & Ahmed, Jubaer & Merugu, Benny S., 2013. "The application of soft computing methods for MPPT of PV system: A technological and status review," Applied Energy, Elsevier, vol. 107(C), pages 135-148.
    7. Fan, Yi & Wang, Pengjun & Heidari, Ali Asghar & Chen, Huiling & HamzaTurabieh, & Mafarja, Majdi, 2022. "Random reselection particle swarm optimization for optimal design of solar photovoltaic modules," Energy, Elsevier, vol. 239(PA).
    8. Evaldo C. Gouvêa & Pedro M. Sobrinho & Teófilo M. Souza, 2017. "Spectral Response of Polycrystalline Silicon Photovoltaic Cells under Real-Use Conditions," Energies, MDPI, vol. 10(8), pages 1-13, August.
    9. 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.
    10. Kara Mostefa Khelil, Chérifa & Amrouche, Badia & Benyoucef, Abou soufiane & Kara, Kamel & Chouder, Aissa, 2020. "New Intelligent Fault Diagnosis (IFD) approach for grid-connected photovoltaic systems," Energy, Elsevier, vol. 211(C).
    11. Gao, Xian-Zhong & Hou, Zhong-Xi & Guo, Zheng & Chen, Xiao-Qian, 2015. "Reviews of methods to extract and store energy for solar-powered aircraft," Renewable and Sustainable Energy Reviews, Elsevier, vol. 44(C), pages 96-108.
    12. Ciprian Cristea & Maria Cristea & Dan Doru Micu & Andrei Ceclan & Radu-Adrian Tîrnovan & Florica Mioara Șerban, 2022. "Tridimensional Sustainability and Feasibility Assessment of Grid-Connected Solar Photovoltaic Systems Applied for the Technical University of Cluj-Napoca," Sustainability, MDPI, vol. 14(17), pages 1-23, August.
    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. Amrouche, Badia & Le Pivert, Xavier, 2014. "Artificial neural network based daily local forecasting for global solar radiation," Applied Energy, Elsevier, vol. 130(C), pages 333-341.

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