Neural network-based model for dual-junction solar cells

Progress in Photovoltaics: Research and Applications

Volumn 19, Issue 1, 2011, Pages 33-44

  Patra, Jagdish C ^a  

^a NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

Author keywords

multi layer perceptron; neural networks; solar cell modeling; tunnel junction

Indexed keywords

ACCURATE ESTIMATION; ARTIFICIAL NEURAL NETWORK; COMPLEX DEVICES; COMPLEX INTERACTION; CONVENTIONAL ANALYTICAL; DESIGN AND DEVELOPMENT; DEVICE SIMULATORS; DUAL-JUNCTION; EXTERNAL QUANTUM EFFICIENCY; GAINP/GAAS; IV CHARACTERISTICS; MODELING TECHNIQUE; MULTI LAYER PERCEPTRON; SILVACO; SOLAR CELL MODELING; TUNNELING EFFECTS; TUNNELING PHENOMENA;

ESTIMATION; NEURAL NETWORKS; SOLAR CELLS; TUNNEL JUNCTIONS;

COMPUTER SIMULATION;

EID: 78650709097     PISSN: 10627995     EISSN: 1099159X     Source Type: Journal    
DOI: 10.1002/pip.985     Document Type: Article

References (24)

1. Cuevas A, Tan J,. Analytical and computer modelling of suns-Voc silicon solar cell characteristics. Solar Energy Materials & Solar Cells 2009; 93: 958-960.
2. Liao C-C,. Genetic K-means algorithm based RBF network for photovoltaic MPP prediction. Energy, doi: 10.1016/j.energy.2009.10.021. (available on-line).
3. Hsu L, Walukiewicz W,. Modeling of InGaN/Si tandem solar cells. Journal of Applied Physics 2008; 104 (024507): 1-7.
4. Sato S-I, Miyamoto H, Imaizumi M, Shimazaki K, Morioka C, Kawano K, Ohshima T,. Degradation modeling of InGaP/GaAs/Ge triple-junction solar cells irradiated with various-energy protons. Solar Energy Materials & Solar Cells. 2009; 93: 768-773.
5. Cheknane A, Hilal HS, Charles JP, Benyoucef B, Campet G,. Modelling and simulation of InGaP solar cells under solar concentration: Series resistance measurement and prediction. Solid State Sciences 2006; 8: 556-559.
6. Hermle M, Letay G, Philipps SP, Bett AW,. Numerical simulation of tunnel diodes for multi-junction solar cells. Progress in Photovoltaics: Research and Applications 2008; 16: 409-418.
7. Burgelman M, Minnaert M, Grasso C,. Device modeling of nano-structured solar cells. In Nanostructural Materials for Solar Energy Conversion, T Soga, (ed.). Elsevier Science: Amsterdam, The Netherlands, 2006; 45-80.
8. SILVACO. available online
9. Synopsys, available online
10. Haykin S,. Neural Networks, (2nd edn). Prentice Hall: Upper Saddle River, NJ, 1999.
11. Patra JC, Pal RN, Chatterji BN, Panda G,. Identification of nonlinear dynamic systems using functional link artificial neural networks. IEEE Transactions on Systems, Man and Cybernetics, Part B- Cybernetics 1999; 29 (2): 254-262.
12. Patra JC, Meher PK, Chakraborty G,. Nonlinear channel equalization for wireless communication systems using Legendre neural networks. Signal Processing 2009; 89: 2251-2262.
13. Patra JC, Chakraborty G, Meher PK,. Neural-network-based robust linearization and compensation technique for sensors under nonlinear environmental influences. IEEE Transactions on Circuits and Systems-I 2008; 55 (5): 1316-1327.
14. Patra JC, Singh O,. Artificial neural network-based approach to design ARIs using QSAR for diabetes mellitus. Journal of Computational Chemistry 2009; 30 (15): 2494-2508.
15. Abdulhadi M, Al-Ibrahim AM, Virk GS,. Neuro-fuzzy-based solar cell model. IEEE Transactions on Energy Conversion 2004; 19 (3): 619-624.
16. Hiyama T, Kitabayashi K,. Neural network based estimation of maximum power generation from PV module using environmental information. IEEE Transactions on Energy Conversion 1997; 12 (3): 241-247.
17. Paul D, Mandal SN, Mukherjee D, Chaudhuri SRB,. Artificial neural network modeling for efficient photovoltaic system design. In Proceedings of IEEE International Conference on Advanced Computer Theory and Engineering Phuket, Thailand, 2008; pp. 50-56.
18. Varnham A, Al-Ibrahim AM, Virk GS, Azzi D,. Soft-computing model-based controllers for increased photovoltaic plant efficiencies. IEEE Transactions on Energy Conversion 2007; 22 (4): 873-880.
19. Almonacid F, Rus C, Hontoria L, Fuentes M, Nofuentes G,. Characterisation of Si-crystalline PV modules by artificial neural networks. Renewable Energy 2009; 34: 941-949.
20. Karatepe E, Boztepe M, Colak M,. Neural network based solar cell model. Energy Conversion and Management 2006; 47: 1159-1178.
21. Mellita A, Kalogirou SA,. Artificial intelligence techniques for photovoltaic applications: a review. Progress in Energy and Combustion Science 2008; 34: 574-632.
22. Baudrit M, Algora C,. Modeling of GaInP/GaAs dualjunction solar cells including tunnel junction. In Proceedings of 33rd IEEE Photovoltaic Specialists Conference St. Diego, USA, 2008.
23. Narendra KS, Parthasarathy K,. Identification and control of dynamical systems using neural networks. IEEE Transactions on Neural Networks 1990; 1 (1): 4-27.
24. Kane EO,. Theory of tunneling. Journal of Applied Physics 1961; 32: 83-91.