© The Institution of Engineering and Technology
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%.
References
-
-
1)
-
18. Restrepo, S.E., Giraldo, S.T., Thijsse, B.J.: ‘Using artificial neural networks to predict grain boundary energies’, Comput. Mater. Sci., 2014, 86, pp. 170–173 (doi: 10.1016/j.commatsci.2014.01.039).
-
2)
-
5. Rougieux, F.E., Macdonald, D., Cuevas, A., et al: ‘Electron and hole mobility reduction and Hall factor in phosphorus compensated p-type silicon’, J. Appl. Phys., 2010, 108, p. 013706 (doi: 10.1063/1.3456076).
-
3)
-
25. Tina, G.M., Gagliano, S., Graditi, G., et al: ‘Experimental validation of a probabilistic model for estimating the energy output from double axis tracking PV systems’, Appl. Energy, 2012, 97, pp. 990–998 (doi: 10.1016/j.apenergy.2012.01.054).
-
4)
-
24. Bonanno, F., Capizzi, G., Graditi, G., et al: ‘A radial basis function neural network based approach for the electrical characteristics estimation of a photovoltaic module’, Appl. Energy, 2012, 97, pp. 956–961 (doi: 10.1016/j.apenergy.2011.12.085).
-
5)
-
14. Chikh, A., Chandra, A.: ‘Adaptive neuro-fuzzy based solar cell model’, IET Renew. Power Gener., 2014, 8, (6), pp. 679–686 (doi: 10.1049/iet-rpg.2013.0183).
-
6)
-
32. Modanese, C., Acciarri, M., Binetti, S., et al: ‘Temperature-dependent Hall-effect measurements of p-type multicrystalline compensated solar grade silicon’, Prog. Photovolt. Res. Appl., 2013, 21, pp. 1469–1477 (doi: 10.1002/pip.2223).
-
7)
-
15. Patra, J.C.: ‘Neural network-based model for dual-junction solar cells’, Prog. Photovolt. Res. Appl., 2011, 19, pp. 33–44 (doi: 10.1002/pip.985).
-
8)
-
34. Patra, J.C., Pal, R.N., Chatterji, B.N., et al: ‘Identification of nonlinear dynamic systems using functional link artificial neural networks’, IEEE Trans. Syst. Man Cybern. B-Cybern., 1999, 29, pp. 254–262 (doi: 10.1109/3477.752797).
-
9)
-
26. Muyeen, S.M., Hasanien Hany, M., Tamura, J.: ‘Reduction of frequency fluctuation for wind farm connected power systems by an adaptive artificial neural network controlled energy capacitor system’, IET Renew. Power Gener., 2012, 6, (4), pp. 226–235 (doi: 10.1049/iet-rpg.2010.0126).
-
10)
-
3. Tanay, F., Dubois, S., Enjalbert, N., et al: ‘Low temperature-coefficient for solar cells processed from solar-grade silicon purified by metallurgical route’, Prog. Photovolt. Res. Appl., 2011, 19, pp. 966–972 (doi: 10.1002/pip.1104).
-
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. 2364–2368.
-
12)
-
9. Veirman, J., Dubois, S., Enjalbert, N., et al: ‘Electronic properties of highly-doped and compensated solar-grade silicon wafers and solar cells’, J. Appl. Phys., 2011, 109, p. 103711 (doi: 10.1063/1.3585800).
-
13)
-
19. Chen, W.C., Lee, A.H.I., Deng, W.J., et al: ‘The implementation of neural network for semiconductor PECVD process’, Expert Syst. Appl., 2007, 32, (4), pp. 1148–1153 (doi: 10.1016/j.eswa.2006.02.013).
-
14)
-
15)
-
21. Sáez, R.M., Sidrach-de-Cardona, M., Mora-López, L.: ‘Data mining and statistical techniques for characterizing the performance of thin-film photovoltaic modules’, Expert Syst. Appl., 2013, 40, (17), pp. 7141–7150 (doi: 10.1016/j.eswa.2013.06.059).
-
16)
-
16. Salam, Z., Ahmed, J., Merugu, B.S.: ‘The application of soft computing methods for MPPT of PV system: a technological and status review’, Appl. Energy, 2013, 107, pp. 135–148 (doi: 10.1016/j.apenergy.2013.02.008).
-
17)
-
20. Lee, S.J., Kim, B., Baik, S.W.: ‘Neural network modeling of inter-characteristics of silicon nitride film deposited by using a plasma-enhanced chemical vapor deposition’, Expert Syst. Appl., 2011, 38, (9), pp. 11437–11441 (doi: 10.1016/j.eswa.2011.03.016).
-
18)
-
23. Trapanese, M.: ‘Identification of parameters of the Jiles–Atherton model by neural networks’, J. Appl. Phys., 2011, 109, p. 07D355. .
-
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. 1043–1063, .
-
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)
-
8. Veirman, J., Dubois, S., Enjalbert, N., et al: ‘Hall mobility reduction in single-crystalline silicon gradually compensated by thermal donors activation’, Solid-State Electron., 2010, 54, pp. 671–674 (doi: 10.1016/j.sse.2010.02.002).
-
22)
-
56. Mellit, A., Kalogirou, S.A.: ‘Artificial intelligence techniques for photovoltaic applications: A review’, Progr. Energy Combus. Sci., 2008, 34, (5), pp. 574–632 (doi: 10.1016/j.pecs.2008.01.001).
-
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. 1–6, .
-
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)
-
22. Zhang, H., Zhao, J., Jia, Y., et al: ‘Exploration of artificial neural network to predict morphology of TiO2 nanotube’, Expert Syst. Appl., 2012, 39, (4), pp. 4094–4101 (doi: 10.1016/j.eswa.2011.09.081).
-
26)
-
4. Di-Sabatino, M., Binetti, S., Libal, J., et al: ‘Oxygen distribution on a multicrystalline silicon ingot grown from upgraded metallurgical silicon’, Sol. Energy Mater. Sol. Cells, 2011, 95, pp. 529–533 (doi: 10.1016/j.solmat.2010.09.013).
-
27)
-
12. Schindler, F., Forster, M., Broisch, J., et al: ‘Towards a unified low-field model for carrier mobilities in crystalline silicon’, Sol. Energy Mater. Sol. Cells, 2014, 131, pp. 92–99 (doi: 10.1016/j.solmat.2014.05.047).
-
28)
-
2. Libal, J., Novaglia, S., Acciarri, M., et al: ‘Effect of compensation and of metallic impurities on the electrical properties of Cz-grown solar grade silicon’, J. Appl. Phys., 2008, 104, p. 104507 (doi: 10.1063/1.3021300).
-
29)
-
10. Rougieux, F.E., Macdonald, D., Cuevas, A.: ‘Transport properties of p-type compensated silicon at room temperature’, Prog. Photovolt. Res. Appl., 2011, 19, pp. 787–793 (doi: 10.1002/pip.1036).
-
30)
-
13. Haykin, S.: ‘Neural networks’ (Prentice Hall, Upper Saddle River, NJ, 1999, 2nd edn.).
-
31)
-
123. Narendra, K.S., Parthasarathy, K.: ‘Identification and control for dynamic system using neural networks’, IEEE Trans. Neural Netw., 1990, 1, (1), pp. 4–27 (doi: 10.1109/72.80202).
-
32)
-
45. Sekhar, P.C., Mishra, S., Sharma, R.: ‘Data analytics based neuro-fuzzy controller for diesel-photovoltaic hybrid AC microgrid’, IET Gener. Transm. Distrib., 2015, 9, (2), pp. 193–207 (doi: 10.1049/iet-gtd.2014.0287).
-
33)
-
11. Lim, B., Wolf, M., Schmidt, J.: ‘Carrier mobilities in multicrystalline silicon wafers made from UMG-Si’, Phys. Status Solidi C, 2011, 3, pp. 835–838 (doi: 10.1002/pssc.201000144).
-
34)
-
7. Schindler, F., Schubert, M.C., Kimmerle, A., et al: ‘Modeling majority carrier mobility in compensated crystalline silicon for solar cells’, Sol. Energy Mater. Sol. Cells, 2012, 106, pp. 31–36 (doi: 10.1016/j.solmat.2012.06.018).
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