A Set of Smart Swarm-Based Optimization Algorithms Applied for Determining Solar Photovoltaic Cell’s Parameters

  • Selma Tchoketch KebirEmail author
  • Mohamed Salah Ait Cheikh
  • Mourad Haddadi
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 62)


This paper presents the study and the use of two smart swarm-based optimization methods for determining the electrical unknown parameters of solar photovoltaic cells. These two methods are the well-known Particle Swarm Optimization (PSO) and a recent smart swarm-based method named, Whale Optimization Algorithm (WOA). This last one is inspired by the hunting behaviour of humpback whales in nature. The best parameters determination values are essential for the accuracy of the solar photovoltaic characteristics. The non-linear parameters determination problem is formulated mathematically as a multi-parameters or as a multi-objective optimization problem. The two swarm-based optimization methods are first described, explaining every step, and then validated using solar photovoltaic manufacturers’ data sheets information. The performance of each approach is evaluated in terms of chosen criteria. The results show that the WOA method outperforms the PSO.


Optimization Swarm-based intelligence Nature-inspired PSO algorithm WOA algorithm Photovoltaic cells 


  1. 1.
    Tu-Ilmenau, F., Geletu, A.: Solving Optimization Problems Using the Matlab Optimization Toolbox—A Tutorial (2018)Google Scholar
  2. 2.
    Brownlee, J.: Clever Algorithms—Nature-Inspired Programming Recipes., Morrisville (2011)Google Scholar
  3. 3.
    Talukder, S.: Mathematical modelling and applications of particle swarm optimization. Independent thesis advanced level (degree of master (two years)) student thesis (2011)Google Scholar
  4. 4.
    Mirjalili, S., Lewis, A.: The whale optimization algorithm. Adv. Eng. Softw. 95, 51–67 (2016)CrossRefGoogle Scholar
  5. 5.
    Trivedi, I.N., Pradeep, J., Narottam, J., Arvind, K., Dilip, L.: Novel adaptive whale optimization algorithm for global optimization. Indian J. Sci. Technol. (2016). Scholar
  6. 6.
    El-Fergany, A.: Efficient tool to characterize photovoltaic generating systems using mine blast algorithm. Electr. Power Compon. Syst. 43, 890–901 (2015)CrossRefGoogle Scholar
  7. 7.
    Carrero, C., Ramirez, D., Rodriguez, J., Platero, C.: Accurate and fast convergence method for parameter estimation of PV generators based on three main points of the I/V curve. Renew. Energy 36, 2972–2977 (2011)CrossRefGoogle Scholar
  8. 8.
    Ghani, F., Rosengarten, G., Duke, M., Carson, J.K.: The numerical calculation of single-diode solar-cell modelling parameters. Renew. Energy 72, 105–112 (2014)CrossRefGoogle Scholar
  9. 9.
    Wang, X., Xu, Y., Ye, M.: Parameter extraction of solar cells using particle swarm optimization. J. Appl. Phys. 105, 094502 (2009)CrossRefGoogle Scholar
  10. 10.
    Soon, J.J., Low, K.S.: Photovoltaic model identification using particle swarm optimization with inverse barrier constraint. IEEE Trans. Power Electron. 27, 3975–3983 (2012)CrossRefGoogle Scholar
  11. 11.
    Askarzadeh, A., Rezazadeh, A.: Parameter identification for solar cell models using harmony search-based algorithms. Sol. Energy 86, 3241–3249 (2012)CrossRefGoogle Scholar
  12. 12.
    Zagrouba, M., Sellami, A., Bouaïcha, M., Ksouri, M.: Identification of PV solar cells and modules parameters using the genetic algorithms: application to maximum power extraction. Sol. Energy 84, 860–866 (2010)CrossRefGoogle Scholar
  13. 13.
    Ishaque, K., Salam, Z.: An improved modeling method to determine the model parameters of photovoltaic (PV) modules using differential evolution (DE). Sol. Energy 85, 2349–2359 (2011)CrossRefGoogle Scholar
  14. 14.
    Kebir, S.T., Cheikh, M.S.A., Haddadi, M.: A detailed step-by-step electrical parameters identification method for photovoltaic generators using a combination of two approaches. Adv. Sci. Technol. Eng. Syst. J. 3(4), 44–52 (2018)CrossRefGoogle Scholar
  15. 15.
    Hamid, N.F.A., Rahim, N.A., Selvaraj, J.: Solar cell parameters identification using hybrid Nelder–Mead and modified particle swarm optimization. J. Renew. Sustain. Energy 8, 015502 (2016). Scholar
  16. 16.
    Mughal, M.A., Ma, Q., Xiao, C.: Photovoltaic cell parameter estimation using hybrid particle swarm optimization and simulated annealing. Energies 10, 1213 (2017). Scholar
  17. 17.
    Izadian, A., Pourtaherian, A., Motahari, S.: Basic model and governing equation of solar cells used in power and control applications. IEEE Energy Convers. Congr. Expos. (ECCE) 2012, 1483–1488 (2012)CrossRefGoogle Scholar
  18. 18.
    Ishaque, K., Salam, Z., Taheri, H.: Simple, fast and accurate two-diode model for photovoltaic modules. Sol. Energy Mater. Sol. Cells 95, 586–594 (2011)CrossRefGoogle Scholar
  19. 19.
    El-Fergany, A.: Efficient tool to characterize photovoltaic generating systems using mine blast algorithm. Electr. Power Compon. Syst. 43, 890–901 (2015)CrossRefGoogle Scholar
  20. 20.
    Lo Brano, V., Ciulla, G.: An efficient analytical approach for obtaining a five parameters model of photovoltaic modules using only reference data. Appl. Energy 111, 894–903 (2013)CrossRefGoogle Scholar
  21. 21.
    Seyedmahmoudian, M., Mekhilef, S., Rahmani, R., Yusof, R., Renani, E.: Analytical modeling of partially shaded photovoltaic systems. Energies 6, 128 (2013)CrossRefGoogle Scholar
  22. 22.
    Bidram, A., Davoudi, A., Balog, R.S.: Control and circuit techniques to mitigate partial shading effects in photovoltaic arrays. IEEE J. Photovolt. 2, 532–546 (2012)CrossRefGoogle Scholar
  23. 23.
    Beni, G., Wang, J.: Swarm intelligence in cellular robotic systems. In: Dario, P., Sandini, G., Aebischer, P. (eds.) Robots and biological systems: towards a new bionics, pp. 703–712. Springer, Berlin (1993)CrossRefGoogle Scholar
  24. 24.
    Wiley, D., Ware, C., Bocconcelli, A., Cholewiak, D., Friedlaender, A., Thompson, M., et al.: Underwater components of humpback whale bubble-net feeding. Behaviour 148, 575–602 (2011)CrossRefGoogle Scholar
  25. 25.
    Van Overstraeten, R.: Crystalline silicon solar cells. Renew. Energy 5, 103–106 (1994)CrossRefGoogle Scholar
  26. 26.
  27. 27.
    Tian, H., Mancilla-David, F., Ellis, K., Muljadi, E., Jenkins, P.: Detailed performance model for photovoltaic systems. Preprint (2012)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Selma Tchoketch Kebir
    • 1
    Email author
  • Mohamed Salah Ait Cheikh
    • 1
  • Mourad Haddadi
    • 1
  1. 1.Electronic DepartmentEcole Nationale Polytechnique, ENPAlgiersAlgeria

Personalised recommendations