Skip to main content
SpringerLink
Log in
Book cover

Recent Metaheuristics Algorithms for Parameter Identification pp 51–95Cite as

Comparison of Solar Cells Parameters Estimation Using Several Optimization Algorithms

  • Chapter
  • First Online:

Part of the book series: Studies in Computational Intelligence ((SCI,volume 854))

Abstract

The increasing use of renewable energy due to the excessive use of fossil fuels causing high quantities pollution has caused the growth of research fields in this area. One of the most important is the use of solar cells because of their unlimited source of power. The performance of a solar cell directly depends on its design parameters, so that, the solar cells parameter estimation is a complex task due to its non-linearity and high multimodality. Optimization techniques are widely used to solve complex problems efficiently.

This is a preview of subscription content, 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   84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   109.99
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

Learn about institutional subscriptions

References

  1. S. Shafiee, E. Topal, When will fossil fuel reserves be diminished? Energy Policy 37(1), 181–189 (2009)

    Article  Google Scholar 

  2. Peer Review of Renewables 2017 Global Status Report—REN21. [Online]. Available: http://www.ren21.net/peer-review-renewables-2017-global-status-report/. Accessed 21 Mar 2017

  3. C. Town, in CIE42 Proceedings, 16–18 July 2012, Cape Town, South Africa © 2012 CIE & SAIIE. pp. 16–18, July 2012

    Google Scholar 

  4. V. Quaschning, R. Hanitsch, Numerical simulation of current-voltage characteristics of photovoltaic systems with shaded solar cells. Sol. Energy 56(6), 513–520 (1996)

    Article  Google Scholar 

  5. G. Farivar, B. Asaei, Photovoltaic module single diode model parameters extraction based on manufacturer datasheet parameters, in PECon2010—2010 IEEE International Conference on Power and Energy 2010, vol. 2, pp. 929–934 (2010)

    Google Scholar 

  6. T.S. Babu, J.P. Ram, K. Sangeetha, A. Laudani, N. Rajasekar, Parameter extraction of two diode solar PV model using Fireworks algorithm. Sol. Energy 140, 265–276 (2016)

    Article  Google Scholar 

  7. V. Khanna, B.K. Das, D. Bisht, P.K. Singh, A three diode model for industrial solar cells and estimation of solar cell parameters using PSO algorithm. Renew. Energy 78, 105–113 (2015)

    Article  Google Scholar 

  8. T. Easwarakhanthan, J. Bottin, I. Bouhouch, C. Boutrit, Nonlinear minimization algorithm for determining the solar cell parameters with microcomputers. Int. J. Sol. Energy 4, 1–12 (1986)

    Article  Google Scholar 

  9. A. Ortiz-Conde, F.J. García Sánchez, J. Muci, New method to extract the model parameters of solar cells from the explicit analytic solutions of their illuminated I-V characteristics. Sol. Energy Mater. Sol. Cells 90(3), 352–361 (2006)

    Article  Google Scholar 

  10. A. Jain, A. Kapoor, Exact analytical solutions of the parameters of real solar cells using Lambert W-function. Sol. Energy Mater. Sol. Cells 81(2), 269–277 (2004)

    Article  Google Scholar 

  11. H. Saleem, S. Karmalkar, An analytical method to extract the physical parameters of a solar cell from four points on the illuminated J-V curve. Electron Device Lett. IEEE 30(4), 349–352 (2009)

    Article  Google Scholar 

  12. J. Appelbaum, A. Peled, Parameters extraction of solar cells—A comparative examination of three methods. Sol. Energy Mater. Sol. Cells 122, 164–173 (2014)

    Article  Google Scholar 

  13. J.A. Jervase, H. Bourdoucen, A. Al-Lawati, Solar cell parameter extraction using genetic algorithms. Meas. Sci. Technol. 12(11), 1922–1925 (2001)

    Article  Google Scholar 

  14. N. Moldovan, R. Picos, E. Garcia-Moreno, Parameter extraction of a solar cell compact model using genetic algorithms, in Proceedings of the 2009 Spanish Conference on Electron Devices, CDE’09, 2009, pp. 379–382

    Google Scholar 

  15. M. Zagrouba, A. Sellami, M. Bouaı, Identification of PV solar cells and modules parameters using the genetic algorithms: Application to maximum power extraction. Sol. Energy 84(5), 860–866 (2010)

    Article  Google Scholar 

  16. M. Ye, X. Wang, Y. Xu, Parameter extraction of solar cells using particle swarm optimization. J. Appl. Phys. 105(9), 094502 (2009)

    Article  Google Scholar 

  17. A. Khare, S. Rangnekar, A review of particle swarm optimization and its applications in solar photovoltaic system. Appl. Soft Comput (2013)

    Google Scholar 

  18. H. Qin, J.W. Kimball, Parameter determination of photovoltaic cells from field testing data using particle swarm optimization, in 2011 IEEE Power and Energy Conference at Illinois, pp. 1–4 (2011)

    Google Scholar 

  19. E.E. Faculty, Implementation of artificial bee colony algorithm on maximum power point tracking for PV modules. Adv. Top. Electr. Eng. (ATEE). 1–4 (2013)

    Google Scholar 

  20. D. Oliva, E. Cuevas, G. Pajares, Parameter identification of solar cells using artificial bee colony optimization. Energy 72, 93–102 (2014)

    Article  Google Scholar 

  21. W. Gong, Z. Cai, Parameter extraction of solar cell models using repaired adaptive differential evolution. Sol. Energy 94, 209–220 (2013)

    Article  Google Scholar 

  22. K. Ishaque, Z. Salam, S. Mekhilef, A. Shamsudin, Parameter extraction of solar photovoltaic modules using penalty-based differential evolution. Appl. Energy 99, 297–308 (2012)

    Article  Google Scholar 

  23. K. Ishaque, Z. Salam, An improved modeling method to determine the model parameters of photovoltaic (PV) modules using differential evolution (DE). Sol. Energy 85, 2349–2359 (2011)

    Article  Google Scholar 

  24. A. Askarzadeh, A discrete chaotic harmony search-based simulated annealing algorithm for optimum design of PV/wind hybrid system. Sol. Energy (2013)

    Google Scholar 

  25. A. Askarzadeh, A. Rezazadeh, Parameter identification for solar cell models using harmony search-based algorithms. Sol. Energy (2012)

    Google Scholar 

  26. J. Ahmed, Z. Salam, A soft computing MPPT for PV system based on Cuckoo search algorithm, in International Conference on Power Engineering, Energy and Electrical Drives, pp. 558–562 (2013)

    Google Scholar 

  27. J. Ma, T.O. Ting, K.L. Man, N. Zhang, S.U. Guan, P.W.H. Wong, Parameter estimation of photovoltaic models via cuckoo search. J. Appl. Math. 2013, 1–8 (2013)

    MathSciNet  Google Scholar 

  28. A.M. Humada, M. Hojabri, S. Mekhilef, H.M. Hamada, Solar cell parameters extraction based on single and double-diode models: A review. Renew. Sustain. Energy Rev. 56, 494–509 (2016)

    Article  Google Scholar 

  29. R. Tamrakar, A. Gupta, A review: Extraction of solar cell modelling parameters. 3(1) (2015)

    Google Scholar 

  30. D.S.H. Chan, J.R. Phillips, J.C.H. Phang, A comparative study of extraction methods for solar cell model parameters. Scopus (1986)

    Google Scholar 

  31. D. Karaboga, An idea based on honey bee swarm for numerical optimization, in Tech. Rep. TR06, Erciyes Univ., no. TR06, p. 10 (2005)

    Google Scholar 

  32. R. Storn, K. Price, Differential evolution—A simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optim. 341–359 (1997)

    Google Scholar 

  33. Z.W. Geem, A new heuristic optimization algorithm: Harmony search. Simulation (2001)

    Google Scholar 

  34. E. Rashedi, H. Nezamabadi-pour, S. Saryazdi, GSA: A gravitational search algorithm. Inf. Sci. (Ny) 179(13), 2232–2248 (2009)

    Article  Google Scholar 

  35. X.S. Yang, S. Deb, Cuckoo search via Lévy flights, in 2009 World Congr. Nat. Biol. Inspired Comput. NABIC 2009—Proc., pp. 210–214 (2009)

    Google Scholar 

  36. P. Civicioglu, Transforming geocentric cartesian coordinates to geodetic coordinates by using differential search algorithm. Comput. Geosci. 46, 229–247 (2012)

    Article  Google Scholar 

  37. A. Askarzadeh, A novel metaheuristic method for solving constrained engineering optimization problems: Crow search algorithm. Comput. Struct. 169, 1–12 (2016)

    Article  Google Scholar 

  38. N. Hansen, A. Ostermeier, Adapting arbitrary normal mutation distributions in evolution strategies: the covariance matrix adaptation, in Proceedings of IEEE International Conference on Evolutionary Computation, pp. 312–317

    Google Scholar 

  39. N. Hansen, A. Ostermeier, Completely derandomized self-adaptation in evolution strategies. Evol. Comput. 9(2), 159–195 (2001)

    Article  Google Scholar 

  40. J. Kennedy, R. Eberhart, Particle swarm optimization, in Neural Networks, 1995. Proceedings., IEEE International. Conference, vol. 4, pp. 1942–1948 (1995)

    Google Scholar 

  41. P. Barthelemy, J. Bertolotti, D.S. Wiersma, A Lévy flight for light. Nature 453(7194), 495–498 (2008)

    Article  Google Scholar 

  42. A. Yona, T. Senjyu, T. Funabshi, H. Sekine, Application of neural network to 24-hours-ahead generating power forecasting for PV system. IEEJ Trans. Power Energy 128(1), 33–39 (2008)

    Article  Google Scholar 

  43. T. Hiyama, S. Kouzuma, T. Imakubo, Identification of optimal operating point of PV modules using neural network for real time maximum power tracking control. IEEE Trans. Energy Convers. 10(2), 360–367 (1995)

    Article  Google Scholar 

  44. E. Karatepe, M. Boztepe, M. Colak, Neural network based solar cell model. Energy Convers. Manage. 47(9–10), 1159–1178 (2006)

    Article  Google Scholar 

  45. K. Ishaque, Z. Salam, H. Taheri, Simple, fast and accurate two-diode model for photovoltaic modules. Sol. Energy Mater. Sol. Cells 95(2), 586–594 (2011)

    Article  Google Scholar 

  46. Y.-H. Ji, J.-G. Kim, S.-H. Park, J.-H. Kim, C.-Y. Won, C-language based PV array simulation technique considering effects of partial shading

    Google Scholar 

  47. H.-G. Beyer, Evolutionary algorithms in noisy environments: theoretical issues and guidelines for practice (1999)

    Google Scholar 

  48. L. Jun-hua, L. Ming, An analysis on convergence and convergence rate estimate of elitist genetic algorithms in noisy environments. Opt. Int. J. Light Electron Opt. 124(24), 6780–6785 (2013)

    Article  Google Scholar 

  49. T. Ma, H. Yang, L. Lu, Solar photovoltaic system modeling and performance prediction. Renew. Sustain. Energy Rev. 36, 304–315 (2014)

    Article  Google Scholar 

  50. K. Nishioka, N. Sakitani, Y. Uraoka, T. Fuyuki, Analysis of multicrystalline silicon solar cells by modified 3-diode equivalent circuit model taking leakage current through periphery into consideration. Sol. Energy Mater. Sol. Cells 91(13), 1222–1227 (2007)

    Article  Google Scholar 

  51. N.F.A. Hamid, N.A. Rahim, and J. Selvaraj, Solar cell parameters extraction using particle swarm optimization algorithm, in 2013 IEEE Conference on Clean Energy and Technology (CEAT), pp. 461–465 (2013)

    Google Scholar 

  52. F. Wilcoxon, Breakthroughs in Statistics: Methodology and Distribution, ed. by S. Kotz, N.L. Johnson (Springer, New York, NY, 1992), pp. 196–202

    Google Scholar 

  53. S. García, D. Molina, M. Lozano, F. Herrera, A study on the use of non-parametric tests for analyzing the evolutionary algorithms’ behaviour: A case study on the CEC’2005 Special Session on Real Parameter Optimization. J. Heuristics 15(6), 617–644 (2009)

    Article  Google Scholar 

  54. Y. Hochberg, A sharper Bonferroni procedure for multiple tests of significance. Biometrika 75(4), 800–802 (1988)

    Article  MathSciNet  Google Scholar 

  55. R.A. Armstrong, When to use the Bonferroni correction. Ophthalmic Physiol. Opt. 34(5), 502–508 (2014)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. CUCEI, Universidad de Guadalajara, Guadalajara, Mexico

    Erik Cuevas, Jorge Gálvez & Omar Avalos

Authors
  1. Erik Cuevas
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jorge Gálvez
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Omar Avalos
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Erik Cuevas .

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Cuevas, E., Gálvez, J., Avalos, O. (2020). Comparison of Solar Cells Parameters Estimation Using Several Optimization Algorithms. In: Recent Metaheuristics Algorithms for Parameter Identification. Studies in Computational Intelligence, vol 854. Springer, Cham. https://doi.org/10.1007/978-3-030-28917-1_4

Download citation

Publish with us

Policies and ethics

Search

Navigation