Skip to main content

Advertisement

SpringerLink
Log in

Marine predators algorithm for parameters estimation of photovoltaic modules considering various weather conditions

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

The accurate models of photovoltaic systems are the core of solar energy studies that are describing the system performance and behavior under different operating conditions. The improving of photovoltaic models based on optimizing method is recently the main simulation tool to construct I-V and P–V characteristic curves with the aid of system parameters. These parameters are extracted by using powerful optimal techniques that are building-up from datasheet of manufacturers or experimental data. This paper presents a novel proposed photovoltaic model based on Marine Predators Algorithm to estimate the optimal model parameters of solar cells or modules. Also, it can extract parameters of single diode, double diode and three diode models. Moreover, the Route Mean Square Error value between each model computed parameters and measured results of photovoltaic components are considered as the objective function. R.T.C. France Solar Cell, Photowatt-PWP201 and thin-film photovoltaic modules have been implemented to extract parameters for all three previously mentioned models at different irradiance intensities or temperature degrees. The proposed algorithm results of such cells and modules for each diode model are compared with other research works and manufacturer's results. Moreover, the evaluation of proposed algorithm has been presented considering the complexity analysis and statistical tests. The compared results show that, the accuracy of algorithm results is the best and their I-V and P–V characteristic curves are highly coinciding with manufacturer's curves. Therefore, the results of the proposed algorithm are satisfied with high superiority and better reliability to optimize parameters under different operating conditions.

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.

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

Similar content being viewed by others

References

  1. Nunes H et al (2019) Collaborative swarm intelligence to estimate PV parameters. Energy Convers Manage 185:866–890

    Article  Google Scholar 

  2. Abbassi R et al (2019) An efficient slap swarm-inspired algorithm for parameters identification of photovoltaic cell models. Energy Convers Manage 179:362–372

    Article  Google Scholar 

  3. Prasanth R, Sudhakar B, Tomislav D, Rajasekar N (2017) A new hybrid bee pollinator flower pollination algorithm for solar PV parameter estimation. Energy Convers Manage 135:463–476

    Article  Google Scholar 

  4. Peijie L et al (2017) Parameters extraction of solar cell models using a modified simplified swarm optimization algorithm. Sol Energy 144:594–603

    Article  Google Scholar 

  5. Dalia A, Yousri D, Eteiba M (2016) Parameters extraction of the three diode model for the multi-crystalline solar cell/module using Moth-Flame Optimization Algorithm. Energy Conver Manag 123:535–548

    Article  Google Scholar 

  6. Shuhui X, Yong W (2017) “Parameter estimation of photovoltaic modules using a hybrid flower pollination algorithm. Energy Convers Manage 144:53–68

    Article  Google Scholar 

  7. Easwarakhanthan T, Bottin J, Bouhouch I, Boutrit C (1986) Nonlinear minimization algorithm for determining the solar cell parameters with microcomputers. Int J Sol Energy 4:1–12. https://doi.org/10.1080/01425918608909835

    Article  Google Scholar 

  8. Chen Xu et al (2018) Teaching–learning–based artificial bee colony for solar photovoltaic parameter estimation. Appl Energy 212:1578–1588

    Article  Google Scholar 

  9. Merchaoui M, Sakly A, Mimoun M (2018) Particle swarm optimization with adaptive mutation strategy for Photovoltaic solar cell/module parameter extraction. Energy Convers Manage 175:151–163

    Article  Google Scholar 

  10. Abd Elaziz M, Oliva D (2018) Parameter estimation of solar cells diode models by an improved opposition-based whale optimization algorithm. Energy Convers Manage 171:1843–1859

    Article  Google Scholar 

  11. Elbaset AA, Ali H, Sattar M-E (2014) Novel seven-parameter model for photovoltaic modules. Solar Energy Mat Sol Cells 130:442–455

    Article  Google Scholar 

  12. Rajasekar N, Kumar N, Venugopalan R (2013) Bacterial Foraging Algorithm based solar PV parameter estimation. Sol Energy 97:255–265

    Article  Google Scholar 

  13. Guojiang X et al (2018) Parameter extraction of solar photovoltaic models by means of a hybrid differential evolution with whale optimization algorithm. Sol Energy 176:742–761

    Article  Google Scholar 

  14. A. A. Elbaset, H. Ali, and Montaser Abd El Sattar, “New seven parameters model for amorphous silicon and thin film PV modules based on solar irradiance,” Solar Energy, vol. 138, pp. 26–35, 2016.

  15. V. Lo Brano, et al., “An improved five-parameter model for photovoltaic modules,” Solar Energy Materials & Solar Cells, vol. 94, pp. 1358–1370, 2010.

  16. Villalva M, Gazoli J, Filho E (2009) Comprehensive approach to modeling and simulation of photovoltaic arrays. IEEE Trans Power Electron 24(5):1198–1208

    Article  Google Scholar 

  17. Meiying Y, Xiaodong W, Yousheng X (2009) Parameter extraction of solar cells using particle swarm optimization. J Appl Phys 105(9):1–8. https://doi.org/10.1063/1.3122082

    Article  Google Scholar 

  18. El-Naggar K, AlRashidi M, AlHajri M, Al-Othman A (2012) Simulated annealing algorithm for photovoltaic parameters identification. Sol Energy 86:266–274. https://doi.org/10.1016/j.solener.2011.09.032

    Article  Google Scholar 

  19. Et-torabi K et al (2017) Parameters estimation of the single and double diode photovoltaic models using a Gauss-Seidel algorithm and analytical method: A comparative study. Energy Convers Manage 148:1041–1054

    Article  Google Scholar 

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

    Article  Google Scholar 

  21. Diab A et al (2020) Coyote optimization algorithm for parameters estimation of various models of solar cells and pv modules. IEEE Access 8:111102–111140

    Article  Google Scholar 

  22. Lhi L, Zhen Y, Jiaying Y, Yang D, Du H (2015) A linear identification of diode models from single I-V characteristics of PV panels. IEEE Trans Industr Electron 62(7):4181–4193

    Article  Google Scholar 

  23. J. Soon, K. Low, and S. Goh, (2014) Multi-dimension diode photo-photovoltaic (PV) model for different PV cell technologies, IEEE 23rd Int Symp Ind Electron IEEE. https://doi.org/10.1109/ISIE.2014.6865012.

  24. Nassar-eddine I et al (2016) Parameter estimation of photovoltaic modules using iterative method and the Lambert W function: A comparative study. Energy Convers Manage 119:37–48

    Article  Google Scholar 

  25. Dericka M et al (2017) An improved optimization technique for estimation of solar photovoltaic parameters. Sol Energy 157:116–124

    Article  Google Scholar 

  26. Yourim Y, Geem Z (2015) Parameter optimization of single-diode model of photovoltaic cell using memetic algorithm. Int J Photoenergy 2015:1–7

    Google Scholar 

  27. Bharadwaj P, Chaudhury K, John V (2016) Sequential Optimization for PV Panel Parameter Estimation. IEEE J Photovolt 6(5):1261–1268

    Article  Google Scholar 

  28. Wang J, Pei D, Tong N, Wendong Y (December 2017) A novel hybrid system based on a new proposed algorithm—Multi-Objective Whale Optimization Algorithm for wind speed forecasting. Appl Energy 208:15

    Google Scholar 

  29. Askarzadeh A, Rezazadeh A (2012) Parameter identification for solar cell models using harmony search-based algorithms. Sol Energy. https://doi.org/10.1016/j.solener.2012.08.018

    Article  Google Scholar 

  30. Rabeh A, Abdelkader A, Jemlic M, Chebbi S (2018) Identification of unknown parameters of solar cell models: A comprehensive overview of available approaches. Renew Sustain Energy Rev 90:453–474

    Article  Google Scholar 

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

    Article  Google Scholar 

  32. Faramarzi A, Heidarinejad M, Mirjalili S, Gandomic A (2020) Marine Predators Algorithm: A Nature-inspired Metaheuristic. Expert Syst Appl 152:1–10

    Article  Google Scholar 

  33. Qamar A, Irfan Y, Mehreen S (2020) Political Optimizer: A novel socio-inspired meta-heuristic for global optimization. Knowl-Based Syst 195:1–30

    Google Scholar 

  34. Abdel-Basset M, Reda M, Elhoseny M, Ripon K, Michael R (2020) A Hybrid COVID-19 Detection Model Using an Improved Marine Predators Algorithm and a Ranking-Based Diversity Reduction Strategy. IEEE Access 8:79521–79540

    Article  Google Scholar 

  35. Oliva D, Cuevas E, Pajares G (2014) Parameter identification of solar cells using artificial bee colony optimization. Energy 72:93–102. https://doi.org/10.1016/j.energy.2014.05.011

    Article  Google Scholar 

  36. Yu K, Liang J, Qu B, Chen X, Wang H (2017) Parameters identification of photovoltaic models using an improved JAYA optimization algorithm. Energy Convers Manage 150:742–753. https://doi.org/10.1016/j.enconman.2017.08.063

    Article  Google Scholar 

  37. M. Louzazni, et al., “Metaheuristic Algorithm for Photovoltaic Parameters: Comparative Study and Prediction with a Firefly Algorithm,” Appl. Sci., vol. 8, no. 339, pp. 1–22, 2018. www.mdpi.com/journal/applsci

  38. J. Cubas, et al., “Analytical Calculation of Photovoltaic Systems Maximum Power Point (MPP) Based on the Operation Point,” Appl. Sci., vol. 7, no. 870, pp. 1–15, 2017. www.mdpi.com/journal/applsci

  39. SIEMENS ST40 photovoltaic solar module. URL <https://www.sciencecenter.net/eesolar/siemens/st40.pdf>

  40. Askarzadeh A, Rezazadeh A (2012) Artificial bee swarm optimization algorithm for parameters identification of solar cell models. Appl Energy 102:943–949. https://doi.org/10.1016/j.apenergy.2012.09.052

    Article  Google Scholar 

  41. M AIHajri, F ALHajri, K EI-Naggar and A AI-Othman (2011) A new estimation approach for determining the I-V characteristics of solar cells. Solar Energy. https://doi.org/10.1016/j.solener.2011.04.013

  42. Niu Q, Zhang H, Li K (2014) An improved TLBO with elite strategy for parameters identification of PEM fuel cell and solar cell models. Int J Hydrogen Energy 39:3837–3854. https://doi.org/10.1016/j.ijhydene.2013.12.110

    Article  Google Scholar 

  43. Bouzidi K, Chegaar M, Aillerie M (2012) Solar cells parameters evaluation from dark I-V characteristics. Energy Procedia. https://doi.org/10.1016/j.egypro.2012.05.096

    Article  Google Scholar 

  44. Nunes H, Pombo J, Bento P, Mariano S, Calado M (2019) Collaborative swarm intelligence to estimate PV parameters. Energy Convers Manage 185:866–890. https://doi.org/10.1016/j.enconman.2019.02.003

    Article  Google Scholar 

  45. Mares O, Paulescu M, Badescu V (2015) A simple but accurate procedure for solving the five-parameter model. Energy Convers Manage 105:139–148. https://doi.org/10.1016/j.enconman.2015.07.046

    Article  Google Scholar 

  46. AlHajri M, El-Naggar Kh, Alrashidi M, Al-Othman A (2012) Optimal extraction of solar cell parameters using pattern search. Renewable Energy 44:238–245. https://doi.org/10.1016/j.renene.2012.01.082

    Article  Google Scholar 

  47. Peijie L, Shuying Ch, Ye. Weichang, Ch. Zhicong, W. Lijun, (2017) Parameters extraction of solar cell models using a modified simplified swarm optimization algorithm. Sol Energy 144:594–603. https://doi.org/10.1016/j.solener.2017.01.064

    Article  Google Scholar 

  48. Batzelis E, Papathanassiou S (2015) A method for the analytical extraction of the single-diode PV model parameters. IEEE Trans Sustain Energy 7(2):504–512. https://doi.org/10.1109/TSTE.2015.2503435

    Article  Google Scholar 

  49. Chegaar M, Nehaoua N, Bouhemadou A (2008) Organic and inorganic solar cells parameters evaluation from single I-V plot. Energy Conver Managem 49:1376–1379. https://doi.org/10.1016/j.enconman.2008.01.002

    Article  Google Scholar 

  50. Nunes H, Pombo J, Mariano S, Calado M, Felippe J (2017) 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. https://doi.org/10.1016/J.APENERGY.2017.11.078

    Article  Google Scholar 

  51. Jordehi A (2016) Time varying acceleration coefficients particle swarm optimization (TVACPSO): a new optimization algorithm for estimating parameters of PV cells and modules. Energy Convers Manage 129:262–274. https://doi.org/10.1016/j.enconman.2016.09.085

    Article  Google Scholar 

  52. Hachana O, Hemsas K, Tina G, Ventura C (2013) Comparison of different metaheuristic algorithms for parameter identification of photovoltaic cell/module. J. Renew Sustain Energy 5:1–8. https://doi.org/10.1063/1.4822054

    Article  Google Scholar 

  53. Chin VJ, Salam Z (2019) Coyote optimization algorithm for the parameter extraction of photovoltaic cells. Sol Energy 194:656–670

    Article  Google Scholar 

Download references

Acknowledgements

This paper was supported by Khalifa University, Abu Dhabi, United Arab Emirates under Award No. FSU-2018-25.

Author information

Authors and Affiliations

  1. Department of Electrical Engineering, Faculty of Engineering, South Valley University, Qena, 83523, Egypt

    Montaser Abd El Sattar

  2. Departmen of Electrical Engineering and Computer Science, Khalifa University of Science and Technology, Abu Dhabi, UAE

    Ameena Al Sumaiti

  3. Department of Electrical and Computers Engineering, El-Minia High Institute of Engineering and Technology, El-Minia, Egypt

    Hamdi Ali

  4. Electrical Engineering Department, Faculty of Engineering, Minia University, Minia, 61111, Egypt

    Ahmed A. Zaki Diab

Authors
  1. Montaser Abd El Sattar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ameena Al Sumaiti
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hamdi Ali
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ahmed A. Zaki Diab
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Montaser Abd El Sattar or Ahmed A. Zaki Diab.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest

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

Sattar, M.A.E., Al Sumaiti, A., Ali, H. et al. Marine predators algorithm for parameters estimation of photovoltaic modules considering various weather conditions. Neural Comput & Applic 33, 11799–11819 (2021). https://doi.org/10.1007/s00521-021-05822-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-021-05822-0

Keywords

Search

Navigation