Skip to main content
SpringerLink
Log in

Artificial bee colony algorithm based on a new local search approach for parameter estimation of photovoltaic systems

  • Published:
Journal of Computational Electronics Aims and scope Submit manuscript

Abstract

In this study, an ABC-Local Search (ABC-Ls) method was proposed by including a new local search procedure into the standard artificial bee colony (ABC) algorithm to perform the parameter estimation of photovoltaic systems (PV). The aim of the proposed ABC-Ls method was to improve the exploration capability of the standard ABC with a new local search procedure in addition to the exploitation and exploration balance of the standard ABC algorithm. The proposed ABC-Ls method was first tested on 15 well-known benchmark functions in the literature. In the results of the Friedman Mean Rank test used for statistical analysis, ABC-Ls method successfully ranked first with a value of 1.300 in benchmark functions. After obtaining successful results on the benchmark tests, the proposed ABC-Ls method was applied to the single diode, double diode and Photowatt-PWP-201 PV modules of PV systems for parameter estimations. In addition, the proposed ABC-Ls method has been applied to the KC200GT PV module for parameter estimation under different temperature and irradiance conditions of the PV modules. The success of ABC-Ls method was compared with genetic algorithm (GA), particle swarm optimization (PSO) algorithm, ABC algorithm, tree seed algorithm (TSA), Jaya, Atom search optimization (ASO). The comparison results were presented in tables and graphics in detail. The RMSE values for the parameter estimation of single diode, double diode and Photowatt-PWP-201 PV module of the proposed ABC-LS method were found as 9.8602E−04, 9.8257E−04 and 2.4251E−03, respectively. In this context, the proposed ABC-LS method has been compared with the literature for parameter estimation of single diode, double diode and Photowatt-PWP-201 PV module and it has been found that it provides a parameter estimation similar or better than other studies. The proposed ABC-Ls method for parameter estimation of the KC200GT PV module under different conditions is shown in convergence graphs and box plots, where it achieves more successful, effective and stable results than GA, PSO, ABC, TSA, Jaya and ASO algorithms.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20

Similar content being viewed by others

References

  1. Yu, K., Liang, J. J., Qu, B. Y., Chen, X., Wang, H.: Parameters identification of photovoltaic models using an improved JAYA optimization algorithm. Energy Conversion Manage. 150, 742–753 (2017).

    Article  Google Scholar 

  2. Chin, V. J., Salam, Z., Ishaque, K.: Cell modelling and model parameters estimation techniques for photovoltaic simulator application: a review. Appl. Energy 154, 500–519 (2015).

    Article  Google Scholar 

  3. Nunes, H. G. G., Pombo, J. A. N., Mariano, S. J. P. S., Calado, M. R. A., Felippe de Souza, J. A. M.: A new high performance method for determining the parameters of PV cells and modules based on guaranteed convergence particle swarm optimization. Appl. Energy 211, 774–791 (2018).

    Article  Google Scholar 

  4. Premkumar, M., Babu, T. S., Umashankar, S., Sowmya, R.: A new metaphor-less algorithms for the photovoltaic cell parameter estimation. Optik 208, 164559 (2020).

  5. Yazdanifard, F., Ameri, M., Ebrahimnia-Bajestan, E.: Performance of nanofluid-based photovoltaic/thermal systems: A review. Renew. Sustain. Energy Rev. 76, 323–352 (2017).

  6. Gümüş, Z., Demirtaş, M.: Comparison of the algorithms used in maximum power point tracking in photovoltaic systems under partial shading conditions. J. Polytechnic, pp. 1–15 (2020).

  7. Colak, I., Demirtas, M., Kabalci, M.: Design, optimisation and application of a resonant DC link inverter for solar energy systems. COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering,, 33(5), 1761–1776 (2014).

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

    Article  Google Scholar 

  9. Ouennoughi, Z., Chegaar, M.: A simpler method for extracting solar cell parameters using the conductance method. Solid-State Electronics 43(11), 1985–1988 (1999).

    Article  Google Scholar 

  10. Chegaar, M., Ouennoughi, Z., Hoffmann, A.: A new method for evaluating illuminated solar cell parameters. Solid-State Electronics 45(2), 293–296, (2001).

    Article  Google Scholar 

  11. Chegaar, M., Ouennoughi, Z., Guechi, F.: Extracting dc parameters of solar cells under illumination. Vacuum 75(4), 367–372 (2004).

    Article  Google Scholar 

  12. Chan, D. S. H., Phillips, J. R., Phang, J. C. H.: A comparative study of extraction methods for solar cell model parameters. Solid-State Electron. 29(3), 329–337 (1986).

    Article  Google Scholar 

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

    Article  Google Scholar 

  14. AlHajri, M. F., El-Naggar, K. M, AlRashidi, M. R., Al-Othman, A. K.: Optimal extraction of solar cell parameters using pattern search. Renew. Energy 44, 238–245 (2012).

    Article  Google Scholar 

  15. Askarzadeh, A., Rezazadeh, A.: Artificial bee swarm optimization algorithm for parameters identification of solar cell models. Appl. Energy, 102, 943–949 (2013).

    Article  Google Scholar 

  16. Yuan, X., He, Y., Liu, L.: Parameter extraction of solar cell models using chaotic asexual reproduction optimization. Neural Comput. Appl. 26(5), 1227–1239 (2015).

    Article  Google Scholar 

  17. Xiong, G., Zhang, J., Shi, D., He, Y.: Parameter extraction of solar photovoltaic models using an improved whale optimization algorithm. Energy Convers. Manag. 174, 388–405 (2018).

    Article  Google Scholar 

  18. Yu, K., Qu, B., Yue, C., Ge, S., Chen, X., Liang, J.: A performance-guided JAYA algorithm for parameters identification of photovoltaic cell and module. Appl. Energy 237, 241–257 (2019).

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  21. Jordehi, A.R.: Gravitational search algorithm with linearly decreasing gravitational constant for parameter estimation of photovoltaic cells. In: IEEE Congress on Evolutionary Computation (CEC) 2017, 37–42 (2017)

  22. Oliva, D., Ewees, A.A., Aziz, M.A.E., Hassanien, A.E., Peréz-Cisneros, M.: A chaotic improved Artificial Bee Colony for parameter estimation of photovoltaic cells. Energies 10(7), 865 (2017)

    Article  Google Scholar 

  23. Zhang, Y., Jin, Z., Zhao, X., Yang, Q.: Backtracking search algorithm with Lévy flight for estimating parameters of photovoltaic models. Energy Conversion Manag. 208, 112615 (2020).

    Article  Google Scholar 

  24. Premkumar, M., Jangir, P., Ramakrishnan, C., Nalinipriya, G., Alhelou, H.H., Kumar, B.S.: Identification of solar photovoltaic model parameters using an improved gradient-based optimization algorithm with Chaotic drifts. IEEE Access 9, 62347–62379 (2021)

    Article  Google Scholar 

  25. Kumar, C., Raj, T. D., Premkumar, M., Raj, T. D.: A new stochastic slime mould optimization algorithm for the estimation of solar photovoltaic cell parameters. Optik, 223, 165277 (2020).

    Article  Google Scholar 

  26. Premkumar, M., Jangir, P., Sowmya, R., Elavarasan, R. M., Kumar, B. S.: Enhanced chaotic JAYA algorithm for parameter estimation of photovoltaic cell/modules. ISA Trans. 116, 139–166 (2021).

    Article  Google Scholar 

  27. Premkumar, M., Sowmya, R., Umashankar, S., Jangir, P.: Extraction of uncertain parameters of single-diode photovoltaic module using hybrid particle swarm optimization and grey wolf optimization algorithm. Mater. Today Proc., 46, 5315–5321 (2021).

    Article  Google Scholar 

  28. Premkumar, M., Kumar, C., Sowmya, R., Pradeep, J.: A novel salp swarm assisted hybrid maximum power point tracking algorithm for the solar photovoltaic power generation systems. Automatika, 62 (1), 1–20 (2021).

    Article  Google Scholar 

  29. Derick, M., Rani, C., Rajesh, M., Farrag, M. E., Wang, Y., Busawon, K.: An improved optimization technique for estimation of solar photovoltaic parameters. Solar Energy 157, 116–124 (2017).

    Article  Google Scholar 

  30. Premkumar, M., Sowmya, R., Jangir, P., Kumar, J.S.V.S.: A New and Reliable Objective Functions for Extracting the Unknown Parameters of Solar Photovoltaic Cell Using Political Optimizer Algorithm. In: International Conference on Data Analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI) 2020, 1–6 (2020)

  31. Kiran, M. S., Hakli, H., Gunduz, M., Uguz, H.: Artificial bee colony algorithm with variable search strategy for continuous optimization. Inf. Sci. 300, 140–157 (2015).

    Article  MathSciNet  Google Scholar 

  32. Hakli, H., Kiran, M. S.: An improved artificial bee colony algorithm for balancing local and global search behaviors in continuous optimization. Int. J. Mach. Learn. Cybern. 11(9), 2051–2076 (2020).

    Article  Google Scholar 

  33. Wang, H., Wu, Z., Rahnamayan, S., Sun, H., Liu, Y., Pan, J.-s.: Multi-strategy ensemble artificial bee colony algorithm. Inf. Sci. 279, 587–603 (2014).

    Article  MathSciNet  MATH  Google Scholar 

  34. Karaboga, D., Ozturk, C., Karaboga, N., Gorkemli, B.: Artificial bee colony programming for symbolic regression. Inf. Sci. 209, 1–15 (2012).

    Article  Google Scholar 

  35. Kıran, M., Gündüz, M.: A novel Artificial Bee Colony-based Algorithm for solving the numerical optimization problems. Int. J. Innovative Comput. Inform. Control: IJICIC, 8, 6107 (2012).

    Google Scholar 

  36. Kiran, M. S.: A binary artificial bee colony algorithm and its performance assessment. Expert Syst. Appl. 175, 114817 (2021).

    Article  Google Scholar 

  37. Kiran, M. S.: TSA: Tree-seed algorithm for continuous optimization. Expert Syst. Appl. 42(19), 6686–6698 (2015).

    Article  Google Scholar 

  38. Venkata Rao, R.: Jaya: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems. Int. J. Industrial Eng. Comput. 7, 19–34 (2016).

    Google Scholar 

  39. Zhao, W., Wang, L., Zhang, Z.: Atom search optimization and its application to solve a hydrogeologic parameter estimation problem. Knowledge-Based Syst. 163, 283–304 (2019).

    Article  Google Scholar 

  40. Karaboga, D., Akay, B.: A comparative study of Artificial Bee Colony algorithm. Appl. Math. Comput. 214(1), 108–132 (2009).

    MathSciNet  MATH  Google Scholar 

  41. Karaboğa, D.: An Idea Based On Honey Bee Swarm For Numerical Optimization. Erciyes University Engineering Faculty Computer Engineering Department, Kayseri Technical Report-TR06 (2005).

  42. Karaboga, D., Akay, B.: A survey: algorithms simulating bee swarm intelligence. Artif. Intell. Rev. 31 (1), 61 (2009).

    Article  Google Scholar 

  43. Karaboga, D., Basturk, B.: A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J. Global Optim. 39(3), 459–471 (2007).

    Article  MathSciNet  MATH  Google Scholar 

  44. Lin, Q., et al.: A novel artificial bee colony algorithm with local and global information interaction. Appl. Soft Comput. 62, 702–735 (2018).

    Article  Google Scholar 

  45. Lin-Yu, T., Chun, C.: Multiple trajectory search for Large Scale Global Optimization. In: IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence) 2008, 3052–3059 (2008)

  46. Ma, J.: Optimization approaches for parameter estimation and Maximum Power Point Tracking (MPPT) of photovoltaic systems. Doctor in Philosophy, University of Liverpool, 2014.

  47. Premkumar, M., Kumar, C., Sowmya, R.: Mathematical Modelling of Solar Photovoltaic Cell/Panel/Array based on the Physical Parameters from the Manufacturer’s Datasheet. Forecasting; I–V characteristics; Maximum power point; Partial shading; PV cell. vol. 9 (1), 16 (2020).

  48. Premkumar, M., Sowmya, R., Umashankar, S., Pradeep, J.: An effective solar Photovoltaic Module parameter estimation technique for single-diode model. IOP Conference Series: Materials Science and Engineering, vol. 937, 012014.

  49. Jordehi, A. R.: Parameter estimation of solar photovoltaic (PV) cells: a review. Renew. Sustain. Energy Rev. 61, 354–371 (2016).

    Article  Google Scholar 

  50. Bastidas-Rodriguez, J. D., Petrone, G., Ramos-Paja, C. A., Spagnuolo, G.: A genetic algorithm for identifying the single diode model parameters of a photovoltaic panel. Math. Comput. Simulation, 131 , 38–54 (2017).

    Article  MathSciNet  MATH  Google Scholar 

  51. Eberhart, Yuhui, S.: Particle swarm optimization: developments, applications and resources. in Proceedings of the 2001 Congress on Evolutionary Computation, 1, 81–86 (2001).

  52. Karaboga, D., Basturk, B.: On the performance of artificial bee colony (ABC) algorithm. Appl. Soft Comput. 8(1), 687–697 (2008).

    Article  Google Scholar 

  53. Friedman, M.: A comparison of alternative tests of significance for the problem of m rankings. Ann. Math. Stat. 11(1), 86–92 (1940)

    Article  MathSciNet  MATH  Google Scholar 

  54. Derrac, J., García, S., Molina, D., Herrera, F.: A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evolut. Comput. 1(1), 3–18 (2011).

    Article  Google Scholar 

  55. Rao, R.V., Pawar, R.B.: Quasi-oppositional-based Rao algorithms for multi-objective design optimization of selected heat sinks. J. Comput. Des. Eng. 7(6), 830–863 (2020)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Engineering, Osmaniye Korkut Ata University, Osmaniye, Turkey

    Mehmet Fatih Tefek

Authors
  1. Mehmet Fatih Tefek
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

MFT was responsible for methodology, writing—original draft, data curation, validation, writing—review & editing.

Corresponding author

Correspondence to Mehmet Fatih Tefek.

Ethics declarations

Conflict of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tefek, M.F. Artificial bee colony algorithm based on a new local search approach for parameter estimation of photovoltaic systems. J Comput Electron 20, 2530–2562 (2021). https://doi.org/10.1007/s10825-021-01796-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10825-021-01796-3

Keywords

Search

Navigation