access icon free Artificial neural network-based modelling of compensated multi-crystalline solar-grade silicon under wide temperature variations

In recent years, multi-crystalline solar grade silicon (mc-SoG-Si), instead of expensive electronic-grade Si, is being considered in photovoltaic industry for production of solar modules. These materials usually contain a comparable amount of acceptors (e.g. boron) and donors (e.g. phosphorus) and are therefore called compensated mc-SoG-Si. The electrical parameters, e.g. majority carrier mobility (μ), majority carrier density (p) and resistivity (ρ), of compensated mc-SoG-Si which affect performance of the solar cells vary non-linearly with temperature due to several complex mechanisms. In this study, the authors propose artificial neural network (ANN)-based models to predict the three electrical parameters of mc-SoG-Si material. Using a limited amount of measurement data, the authors have shown that the ANN-based models can predict the three electrical parameters of a given sample over a wide temperature range of 70–400 K and a specific range of compensation ratio. The authors have shown with extensive simulated results that these models can predict the three parameters with a maximum error of ±10%.

Inspec keywords: compensation; neural nets; elemental semiconductors; power engineering computing; solar cells; silicon

Other keywords: donor; artificial neural network-based modelling; temperature 70 K to 400 K; boron; majority carrier mobility; majority carrier density; acceptor; compensated multicrystalline solar-grade silicon; solar module production; solar cell; ANN; mc-SoG-Si; photovoltaic industry; phosphorus; Si

Subjects: Power engineering computing; Neural computing techniques; Solar cells and arrays; Photoelectric conversion; solar cells and arrays

References

    1. 1)
    2. 2)
    3. 3)
    4. 4)
    5. 5)
    6. 6)
    7. 7)
    8. 8)
    9. 9)
    10. 10)
    11. 11)
      • 6. Schindler, F., Geilker, J., Kwapil, W., et al: ‘Conductivity mobility and Hall mobility in compensated multicrystalline silicon’. Proc. 25th European Solar Energy Conf., Valencia, Spain, 2010, pp. 23642368.
    12. 12)
    13. 13)
    14. 14)
      • 1. SEMI PV17-0611– Specification for Virgin Silicon Feedstock Materials for Photovoltaic Applications, 2012.
    15. 15)
    16. 16)
    17. 17)
    18. 18)
      • 23. Trapanese, M.: ‘Identification of parameters of the Jiles–Atherton model by neural networks’, J. Appl. Phys., 2011, 109, p. 07D355. Available at http://dx.doi.org/10.1063/1.3569735.
    19. 19)
      • 28. Elobaid, L.M., Abdelsalam, A.K., Zakzouk, E.E.: ‘Artificial neural network-based photovoltaic maximum power point tracking techniques: a survey’, IET Renew. Power Gener., 2015, 9, (8), pp. 10431063, doi: 10.1049/iet-rpg.2014.0359.
    20. 20)
      • 27. Tina, G.M., Ventura, C., Adinolfi, G., et al: ‘AC power short-term forecasting of a thin-film photovoltaic plant based on artificial neural network models’. Proc. WREC XIII, World Renewable Energy Congress, London, UK, 2014.
    21. 21)
    22. 22)
    23. 23)
      • 26. Graditi, G., Ferlito, S., Adinolfi, G., et al: ‘Performance estimation of a thin-film photovoltaic plant based on an artificial neural network model’. Proc. IREC 2014, Hammamet, March 2014, pp. 16, art. no. 6826954.
    24. 24)
      • 31. Patra, J.C., Modanese, C., Acciarri, M.: ‘Prediction of electronic parameters of compensated multi-crystalline solar-grade silicon using artificial neural networks’. Proc. Int. Joint Conf. on Neural Networks, Killarney, Ireland, July 2015.
    25. 25)
    26. 26)
    27. 27)
    28. 28)
    29. 29)
    30. 30)
      • 13. Haykin, S.: ‘Neural networks’ (Prentice Hall, Upper Saddle River, NJ, 1999, 2nd edn.).
    31. 31)
    32. 32)
    33. 33)
    34. 34)
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