Skip to main content
SpringerLink
Log in

Real Time Techniques and Architectures for Maximizing the Power Produced by a Photovoltaic Array

  • Chapter
Neural Nets and Surroundings

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 19))

  • 1473 Accesses

Abstract

The inherent low conversion efficiency, from solar to electrical energy, of the photovoltaic cells makes the use of techniques and architectures aimed at maximizing the electrical power a photovoltaic array is able to produce at any weather condition mandatory. In order to understand what are the challenging problems cropping up in some modern applications, an overview of the main techniques for photovoltaic arrays modeling is given first. Afterwards, the control strategies for the maximum power point tracking used in commercial products dedicated to photovoltaic strings and modules are compared and their advantages and drawbacks are put into evidence, with a special emphasis on their efficiency. Some methods presented in literature and based on the use of artificial neural networks are compared with more classical ones. Finally, a brief overview of other applications of artificial neural networks to photovoltaic-related problems is also given.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Chapin, D., Fuller, C., Pearson, G.J.: A new silicon p-n junction photocell for converting solar radiation into electrical power. Applied Physics 25, 676–677 (1954)

    Google Scholar 

  2. Luque, A., Hegedus, S.: Handbook of photovoltaic science and engineering. John Wiley and Sons (2006) ISBN:978-0-471-49196-5

    Google Scholar 

  3. Eicker, U.: Solar Technologies for Buildings. Wiley (2003)

    Google Scholar 

  4. Weisstein, E.: Lambert w-function (2008)

    Google Scholar 

  5. Petrone, G., Spagnuolo, G., Vitelli, M.: Analytical model of mismatched photovoltaic fields by means of lambert w-function. Solar Energy Materials and Solar Cells, 1652–1657 (2007)

    Google Scholar 

  6. Petrone, G., Ramos-Paja, C.: Modeling of photovoltaic fields in mismatched conditions for energy yield evaluations. Electric Power Systems Research, 1003–1013 (2011)

    Google Scholar 

  7. Bruendlinger, R., Bletterie, B., Milde, M., Oldenkamp, H.: Maximum power point tracking performance under partially shaded pv array conditions. In: Proceedings of the Twenty-first European Photovoltaic Solar Energy Conference, Dresden, Germany, September 4-8, vol. 34(5), pp. 2157–2160 (2006)

    Google Scholar 

  8. Giaffreda, D., Omana, M., Rossi, D., Metra, C.: Model for thermal behavior of shaded photovoltaic cells under hot-spot condition. In: 2011 IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems (DFT), pp. 252–258 (October 2011)

    Google Scholar 

  9. Karatepe, E., Boztepe, M., Colak, M.: Neural network based solar cell model. Energy Conversion and Management 47(910), 1159–1178 (2006)

    Article  Google Scholar 

  10. Almonacid, F., Rus, C., Hontoria, L., Fuentes, M., Nofuentes, G.: Characterisation of si-crystalline pv modules by artificial neural networks. Renewable Energy 34(4), 941–949 (2009)

    Article  Google Scholar 

  11. Al-Amoudi, A., Zhang, L.: Application of radial basis function networks for solar-array modelling and maximum power-point prediction. IEE Proceedings-Generation, Transmission and Distribution 147(5), 310–316 (2000)

    Article  Google Scholar 

  12. Zhang, L., Bai, Y.F.: Genetic algorithm-trained radial basis function neural networks for modelling photovoltaic panels. Engineering Applications of Artificial Intelligence 18(7), 833–844 (2005)

    Article  MathSciNet  Google Scholar 

  13. Piliougine Rocha, M., Mora Lopez, L., Sidrach de Cardona, M., Elizondo, D.A.: Photovoltaic module simulation by neural networks using solar spectral distribution. Progress in Photovoltaics: Research and Applications (accepted for publication)

    Google Scholar 

  14. Salas, V., Olìas, E., Barrado, A., Làzaro, A.: Review of the maximum power point tracking algorithms for stand-alone photovoltaic systems. Solar Energy Materials & Solar Cells 90, 1555–1576 (2006)

    Article  Google Scholar 

  15. Femia, N., Petrone, G., Spagnuolo, G., Vitelli, M.: Optimization of perturb and observe maximum power point tracking method. IEEE Transactions on Power Electronics 20(4), 963–973 (2005)

    Article  Google Scholar 

  16. Femia, N., Petrone, G., Spagnuolo, G., Vitelli, M.: A technique for improving p&o mppt performances of double stage grid-connected photovoltaic systems. IEEE Transactions on Industrial Electronics 56(11), 4473–4482 (2009)

    Article  Google Scholar 

  17. Hussein, K., Muta, I., Hoshino, T., Osakada, M.: Maximum photovoltaic power tracking: an algorithm for rapidly changing atmospheric conditions. IEE Proceedings-Generation, Transmission and Distribution 142(1), 59–64 (1995)

    Article  Google Scholar 

  18. Spagnuolo, G., Petrone, G., Vitelli, M., Calvente, J., Ramos-Paja, C., Giral, R., Mamarelis, E., Bianconi, E.: A fast current-based mppt technique employing sliding mode control. IEEE Transactions on Industrial Electronics PP(99), 1 (2012)

    Google Scholar 

  19. Shmilovitz, D.: On the control of photovoltaic maximum power point tracker via output parameters. IEE Proceedings-Electric Power Applications 152(2), 239–248 (2005)

    Article  Google Scholar 

  20. Petrone, G., Spagnuolo, G., Vitelli, M.: A multivariable perturb-and-observe maximum power point tracking technique applied to a single-stage photovoltaic inverter. IEEE Transactions on Industrial Electronics 58(1), 76–84 (2011)

    Article  Google Scholar 

  21. Mei, Q., Shan, M., Liu, L., Guerrero, J.: A novel improved variable step-size incremental-resistance mppt method for pv systems. IEEE Transactions on Industrial Electronics 58(6), 2427–2434 (2011)

    Article  Google Scholar 

  22. Casadei, D., Grandi, G., Rossi, C.: Single-phase single-stage photovoltaic generation system based on a ripple correlation control maximum power point tracking. IEEE Transactions on Energy Conversion 21(2), 562–568 (2006)

    Article  Google Scholar 

  23. Kimball, J., Krein, P.: Discrete-time ripple correlation control for maximum power point tracking. IEEE Transactions on Power Electronics 23(5), 2353–2362 (2008)

    Article  Google Scholar 

  24. Brunton, S., Rowley, C., Kulkarni, S., Clarkson, C.: Maximum power point tracking for photovoltaic optimization using ripple-based extremum seeking control. IEEE Transactions on Power Electronics 25(10), 2531–2540 (2010)

    Article  Google Scholar 

  25. Femia, N., Lisi, G., Petrone, G., Spagnuolo, G., Vitelli, M.: Distributed maximum power point tracking of photovoltaic arrays: Novel approach and system analysis. IEEE Transactions on Industrial Electronics 55(7), 2610–2621 (2008)

    Article  Google Scholar 

  26. Petrone, G., Spagnuolo, G., Vitelli, M.: Distributed maximum power point tracking: challenges and commercial solutions. Automatika Journal for Control, Measurement, Electronics, Computing and Communications (accepted for publication)

    Google Scholar 

  27. Cirrincione, M., Pucci, M., Vitale, G.: Growing neural gas based mppt of variable pitch wind generators with induction machines. IEEE Transactions on Industry Applications PP(99), 1 (2012)

    Google Scholar 

  28. Ngan, M.S., Tan, C.W.: Multiple peaks tracking algorithm using particle swarm optimization incorporated with artificial neural network. World Academy of Science, Engineering and Technology 58, 379–385 (2011)

    Google Scholar 

  29. Salah, C.B., Ouali, M.: Comparison of fuzzy logic and neural network in maximum power point tracker for pv systems. Electric Power Systems Research 81(1), 43–50 (2011)

    Article  Google Scholar 

  30. Syafaruddin, Karatepe, E., Hiyama, T.: Artificial neural network-polar coordinated fuzzy controller based maximum power point tracking control under partially shaded conditions. IET Renewable Power Generation 3(2), 239–253 (2009)

    Google Scholar 

  31. Mohamed, S.A., Shareef, H.: Hopfield neural network optimized fuzzy logic controller for maximumpower point tracking in a photovoltaic system. International Journal of Photoenergy, 13 (2012)

    Google Scholar 

  32. Wai, R.J., Lin, C.Y.: Active low-frequency ripple control for clean-energy power-conditioning mechanism. IEEE Transactions on Industrial Electronics 57, 3780–3792 (2010)

    Article  Google Scholar 

  33. Radzi, M.A.M., Rahim, N.A.: Neural network and bandless hysteresis approach to control switched capacitor active power filter for reduction of harmonics. IEEE Transactions on Industrial Electronics 56, 1477–1484 (2009)

    Article  Google Scholar 

  34. Cirrincione, M., Pucci, M., Vitale, G., Miraoui, A.: Current harmonic compensation by a single-phase shunt active power filter controlled by adaptive neural filtering. IEEE Transactions on Industrial Electronics 56, 3128–3143 (2009)

    Article  Google Scholar 

  35. Mellit, A., Kalogirou, S.A.: Artificial intelligence techniques for photovoltaic applications: A review. Progress in Energy and Combustion Science 34(5), 574–632 (2008)

    Article  Google Scholar 

  36. del Rìo, P., Gual, M.: The promotion of green electricity in europe: present and future. European Environment 14(4), 219–234 (2004)

    Article  Google Scholar 

  37. Matallanas, E., Castillo-Cagigal, M., Gutirrez, A., Monasterio-Huelin, F., Caamao-Martinez, E., Masa, D., Jimnez-Leube, J.: Neural network controller for active demand-side management with pv energy in the residential sector. Applied Energy 91(1), 90–97 (2012)

    Article  Google Scholar 

  38. Lin, W.M., Hong, C.M., Chen, C.H.: Neural-network-based mppt control of a stand-alone hybrid power generation system. IEEE Transactions on Power Electronics 26(12), 3571–3581 (2011)

    Article  Google Scholar 

  39. Giraud, F., Salameh, Z.: Analysis of the effects of a passing cloud on a grid-interactive photovoltaic system with battery storage using neural networks. IEEE Transaction on Energy Conversion 14(4), 1572–1579 (1999)

    Article  Google Scholar 

  40. Hontoria, L., Aguilera, J., Zufiria, P.: A new approach for sizing stand alone photovoltaic systems based in neural networks. Solar Energy 78(2), 313–319 (2005); ISES Solar World Congress 2003

    Article  Google Scholar 

  41. Balzani, M., Reatti, A.: Neural network based model of a pv array for the optimum performance of pv system. In: Research in Microelectronics and Electronics, 2005 PhD, vol. 2, pp. 123–126 (July 2005)

    Google Scholar 

  42. Caputo, D., Grimaccia, F., Mussetta, M., Zich, R.: Photovoltaic plants predictive model by means of ann trained by a hybrid evolutionary algorithm. In: The 2010 International Joint Conference on Neural Networks (IJCNN), pp. 1–6 (July 2010)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Dipartimento di Ingegneria Elettronica ed Ingegneria Informatica (D.I.E.I.I.), University of Salerno, Via Ponte Don Melillo, 84084, Fisciano, Salerno, Italy

    Giovanni Petrone & Giovanni Spagnuolo

  2. Escuela Politècnica Superior (despacho 3050) Dept. de Tecnologìa Electrònica, Universidad de Màlaga, C/ Dr. Ortiz Ramos (campus Teatinos), 29071, Màlaga, Spain

    Francisco Jose Sànchez Pacheco

Authors
  1. Giovanni Petrone
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Francisco Jose Sànchez Pacheco
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Giovanni Spagnuolo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Giovanni Petrone .

Editor information

Editors and Affiliations

  1. , Dept of Computer Science, Milano University, Via Comelico 39, Milano, 20135, Italy

    Bruno Apolloni

  2. Dept of Computer Science, Milano University, Via Comelico 39, Milano, 20135, Italy

    Simone Bassis

  3. Dept. of Psychology, Second University of Naples, Via Vivaldi 43, Caserta, 81100, Salerno, Italy

    Anna Esposito

  4. , Dipartimento di Meccanica e Materiali, University Mediterranea of Reggio, Via Graziella - Feo di Vito, Reggio Calabria, 89124, Reggio Calabria, Italy

    Francesco Carlo Morabito

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Petrone, G., Sànchez Pacheco, F.J., Spagnuolo, G. (2013). Real Time Techniques and Architectures for Maximizing the Power Produced by a Photovoltaic Array. In: Apolloni, B., Bassis, S., Esposito, A., Morabito, F. (eds) Neural Nets and Surroundings. Smart Innovation, Systems and Technologies, vol 19. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35467-0_25

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-35467-0_25

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-35466-3

  • Online ISBN: 978-3-642-35467-0

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation