Multilayer perceptron applied to the estimation of the influence of the solar spectral distribution on thin-film photovoltaic modules

Applied Energy

Volumn 112, Issue , 2013, Pages 610-617

  Piliougine, Michel ^a   Elizondo, David ^b   Mora López, Llanos ^a   Sidrach de Cardona, Mariano ^a  

^a UNIVERSITY OF MÁLAGA   (Spain)

^b DE MONTFORT UNIVERSITY   (United Kingdom)

Author keywords

Average photon energy; Current voltage curve; Kohonen self organising map; Multilayer perceptron; Solar spectral distribution; Thin film photovoltaic module

Indexed keywords

CURVE FITTING; PHOTOVOLTAIC CELLS; SOLAR RADIATION; SUN;

CURRENT VOLTAGE CURVE; KOHONEN; MULTI LAYER PERCEPTRON; PHOTON ENERGY; PHOTOVOLTAIC MODULES; SPECTRAL DISTRIBUTION;

ENERGY CONVERSION;

ARTIFICIAL NEURAL NETWORK; BIOFILM; ESTIMATION METHOD; NUMERICAL MODEL; PHOTOVOLTAIC SYSTEM; SOLAR POWER; SPECTRAL ANALYSIS;

EID: 84884208096     PISSN: 03062619     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.apenergy.2013.05.053     Document Type: Article

References (40)

1. Solar Generation 6 - Solar photovoltaic electricity empowering the world. European Photovoltaic Industry Association (EPIA) and Greenpeace; 2011.
2. Herrmann W, Becker H, Wiesner W. Round robin test on translation procedures for measured PV generator characteristics. In: 14th European photovoltaic solar energy conference; Barcelona, Spain; 1997. p. 203-6.
3. Sandstrom JD. A method for predicting solar cell current voltage curve characteristics as a function of incident solar intensity and cell temperature. Technical Report 32-7742. Pasadena (USA): Jet Propulsion Laboratory, California Institute of Technology, National Aeronautics and Space Administration (NASA); 1967.
4. IEC 60891. Procedures for temperature and irradiance corrections to measure I-V characteristics of crystalline silicon photovoltaic devices. 2nd ed. Geneva, Switzerland: International Electrotechnical Commission; 2008. ISBN: 2-8318-1073-1.
5. Blaesser G., Rossi E. Extrapolation of outdoor measurements of PV array I-V characteristics to standard test conditions. Sol Cells 1988, 25(2):91-96. 10.1016/0379-6787(88)90014-2.
6. Anderson AJ. Photovoltaic translation equations. A new approach. Technical Report NREL/TP-411-20279. Golden, USA: National Renewable Energy Laboratory (NREL); 1996, DOI: 10.2172/177401.
7. Marion B. A method for modeling the current-voltage curve on a PV module for outdoor conditions. Prog Photovol Res Appl 2002, 10(3):205-214. 10.1002/pip.403.
8. Marion B., Rummel S., Anderberg A. Current-voltage curve translation by bilinear interpolation. Photovol Res Appl 2004, 12(8):593-607. 10.1002/pip.551.
9. Rosell J.I., Ibáñez M. Modeling power output in photovoltaic modules for outdoor operating conditions. Energy Convers Manage 2006, 47(15-16):2424-2430. 10.1016/j.enconman.2005.11.004.
10. Celik A.N., Acikgoz N. Modelling and experimental verification of the operating current of mono-crystalline photovoltaic modules using four- and five-parameter models. Appl Energy 2007, 84(1):1-15. 10.1016/j.apenergy.2006.04.007.
11. Sandrolini L., Artioli M., Reggiani U. Numerical method for the extraction of photovoltaic module double-diode model parameters through cluster analysis. Appl Energy 2010, 87(2):442-451. 10.1016/j.apenergy.2009.07.022.
12. Ishaque K., Salam Z., Taheri H. Simple, fast and accurate two-diode model for photovoltaic modules. Sol Energy Mater Sol Cells 2011, 95(2):586-594. 10.1016/j.solmat.2010.09.023.
13. Domínguez C., Antón I., Sala G. Multijunction solar cell model for translating I-V characteristics as a function of irradiance, spectrum, and cell temperature. Prog Photovol Res Appl 2010, 18(4):272-284. DOI: 1010.1002/pip.965.
14. Kalogirou S.A. Applications of artificial neural-networks for energy systems. Appl Energy 2000, 67(1-2):17-35. 10.1016/S0306-2619(00)00005-2.
15. Kalogirou S.A. Optimization of solar systems using artificial neural-networks and genetic algorithms. Appl Energy 2004, 77(4):383-405. 10.1016/S0306-2619(03)00153-3.
16. Zhang L., Bai Y.F. On-line neural network training for maximum power point tracking of PV power plant. Trans Inst Meas Control 2008, 30(1):77-96. 10.1177/0142331207076374.
17. Chao KH, Li CJ, Wang MH. A maximum power point tracking method based on extension neural network for PV systems. Advances in Neural Networks - ISNN 2009, Lect Notes Comput Sci 2009;5551:745-55 [Yu W, He H and Zhang N, editors. Berlin/Heidelberg, Germany: Springer, DOI: ]. doi:10.1007/978-3-642-01507-6_84.
18. Patra J.C. Neural network-based model for dual-junction solar cells. Prog Photovol Res Appl 2011, 19(1):33-44. 10.1002/pip.985.
19. Karatepe E., Boztepe M., Colak M. Neural network based solar cell model. Energy Convers Manage 2006, 47(9-10):1159-1178. 10.1016/j.enconman.2005.07.007.
20. Celik A.N. Artificial neural network modelling and experimental verification of the operating current of mono-crystalline photovoltaic modules. Sol Energy 2011, 85(10):2507-2517. 10.1016/j.solener.2011.07.009.
21. Di Piazza M.C., Pucci M., Ragusa A., Vitale G. Analytical versus neural real-time simulation of a photovoltaic generator based on a DC-DC converter. IEEE Trans Ind Appl 2010, 46(10):2501-2510. 10.1109/TIA.2010.2072975.
22. Dolan J.A., Lee R., Yeh Y.H., Yeh C., Nguyen D.Y., Ben-Menahem S., et al. Neural network estimation of photovoltaic I-V curves under partially shaded conditions. International Joint Conference on Neural Networks (IJCNN) 2011, 1358-1365. San Jose, CA (USA), DOI: 10.1109/IJCNN.2011.6033382.
23. Patra J.C. Chebyshev neural network-based model for dual-junction solar cells. IEEE Trans Energy Convers 2011, 26(1):132-139. DOI: 10.1109/TEC.2010.2079935.
24. Patra J.C., Maskell D.L. Modeling of multi-junction solar cells for estimation of EQE under influence of charged particles using artificial neural networks. Renew Energy 2012, 44:7-16. 10.1016/j.renene.2011.11.044.
25. Hontoria L, Aguilera J, Almonacid F, Nofuentes G, Zufiria P. Artificial neural networks applied in PV systems and solar radiation. In: Kalogirou S, editor, Artificial intelligence in energy and renewable energy systems, chapter 5, p. 163-200. Nova Science Publishers Inc., UK; 2006. ISBN 978-1-60021-261-1.
26. Almonacid F., Rus C., Hontoria L., Fuentes M., Nofuentes G. Characterisation of Si-crystalline PV modules by artificial neural networks. Renew Energy 2009, 34(4):941-949. 10.1016/j.renene.2008.06.010.
27. Almonacid F., Rus C., Hontoria L., Muñoz J.F. Characterisation of PV CIS module by artificial neural networks: a comparative study with other methods. Renew Energy 2010, 35(5):973-980. 10.1016/j.renene.2009.11.018.
28. Zárate LE, Neto LVBM, Soares DA, Bittencout FR. Artificial neural networks applied for representation of curves current-voltage of photovoltaic modules, In: IEEE international conference on industrial informatics. Daejeon, Korea; 2008. p. 1644-9, DOI. doi:10.1109/INDIN.2008.4618367.
29. Hadjab M., Berrah S., Abid H. Neural network for modeling solar panel. Int J Energy 2012, 6(1):9-16. ISSN: 1998-4316.
30. Bonanno F., Capizzi G., Graditi G., Napoli C., Tina G.M. A radial basis function neural network based approach for the electrical characteristics estimation of a photovoltaic module. Appl Energy 2012, 97:956-961. 10.1016/j.apenergy.2011.12.085.
31. Mekki H., Mellit A., Kalogirou S.A., Messai A., Furlan G. FPGA-based implementation of a real time photovoltaic module simulator. Prog Photovol Res Appl 2010, 18(2):115-127. 10.1002/pip.950.
32. Piliougine M, Elizondo D, Mora-López L, Sidrach-de-Cardona M. Photovoltaic module simulation by neural networks using solar spectral distribution. Prog Photovol Res Appl, in press, doi: [Springer]. doi:10.1002/pip.1073.
33. Piliougine M., Carretero J., Mora-López L., Sidrach-de-Cardona M. Experimental system for current-voltage curve measurement of photovoltaic modules under outdoor conditions. Prog Photovol Res Appl 2011, 19(5):591-602. 10.1002/pip.1073.
34. Minemoto T., Nagae S., Takakura H. Impact of spectral irradiance distribution and temperature on the outdoor performance of amorphous Si photovoltaic modules. Sol Energy Mat Sol Cells 2007, 91(10):919-923. 10.1016/j.solmat.2007.02.012.
35. Principe J.C., Euliano J.R., Lefebvre W.C. Neural and adaptive systems: fundamentals through simulations 2000, John Wiley & Sons, New York (USA), ISBN: 0-471-35167-2.
36. Kohonen T. The self-organizing map. Proc IEEE 1990, 78(9):1464-1480. 10.1109/5.58325.
37. Božnar MZ, Mlakar P. Use of neural networks in the field of air pollution modeling. In: Borrego C., Schayes G. editors, Proceedings of the air pollution modeling and its application XV. New York, Kluwer Academic Publishers, USA; 2002. p. 375-83. ISBN: 0-306-47813-7.
38. Beale MH, Hagan MT, Demuth HB. Matlab R2011b. Neural network toolbox user's guide. MA (USA): The Mathworks, Inc. Natick; 2011.
39. Levenberg K. A method for the solution of certain non-linear problems in least squares. Q Appl Math 1944, 2(2):164-168. ISSN: 0033-569X.
40. Marquardt D. An algorithm for least-squares estimation of nonlinear parameters. SIAM J Appl Math 1963, 11(2):431-441. ISSN: 0368-4245.