Skip to main content

Advertisement

SpringerLink
Log in

Opposition-based equilibrium optimizer algorithm for identification of equivalent circuit parameters of various photovoltaic models

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

Abstract

The simulation, assessment, and harvesting of maximum energy of the solar photovoltaic (PV) system require accurate and fast parameter estimation for solar cell/module models. No complete information on the PV module parameters is provided in the manufacturer’s datasheets. This leads to a nonlinear PV model with a number of unknown parameters. Recently, a new meta-heuristic algorithm called equilibrium optimizer (EO) is suggested to solve global problems. However, the EO is trapped to local optima when it is applied to real-world problems. Therefore, this paper proposes a novel and efficient algorithm called opposition-based equilibrium optimization (OBEO) for extracting the parameters of various PV models, including the single-diode model (SDM), double-diode model (DDM), and three-diode model (TDM). This paper presents opposition-based learning as an update mechanism to produce the best solutions to find better search space. In this paper, the PV module parameters are extracted using three distinct points: open-circuit voltage, Voc, short-circuit current, Isc, and the point at which maximum power in the I–V curve is provided by the datasheet. The proposed OBEO algorithm minimizes the error of the I–V relationship, and the OBEO algorithm helps to find the optimal solution by generating zero error, and the search agent updates the position randomly with respect to the best solution to reach the optimal state. The proposed algorithm optimizes the parameters of the module without any assumptions. Finally, the proposed method of extracting the parameter is compared with the state-of-the-art methods to validate its performance. The proposed OBEO can achieve zero error values (fitness values) for all PV models, and the average runtime of the OBEO is 14.78 s, 28.33 s, and 32.62 s for SDM, DDM, and TDM, respectively, of all selected PV modules.

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.
Fig. 20.
Fig. 21
Fig. 22

Similar content being viewed by others

References

  1. Jäger-Waldau, A.: JRC PV Status Report 2018. Publications Office of the European Union, Luxembourg (2018). ISBN 978-92-79-97465-6.

  2. Global Market Outlook for Solar Power 2017/2021 by Solar Power Europe. Available online: http://www.solarpowereurope.org. Accessed 15 May 2020.

  3. Keerthisinghe, C., Chapman, A.C., Verbic, G.: PV, and demand models for a Markov decision process formulation of the home energy management problem. IEEE Trans. Ind. Electron. 66, 1424–1433 (2019)

    Article  Google Scholar 

  4. Premkumar, M., Kumar, C., Sowmya, R.: Mathematical modelling of solar photovoltaic cell/panel/array based on the physical parameters from the manufacturer’s datasheet. Int. J Renew. Energy Dev. 9, 7–22 (2020)

    Article  Google Scholar 

  5. Batzelis, E.I., Kampitsis, G.E., Papathanassiou, S.A., Manias, S.N.: Direct MPP calculation in terms of the single-diode PV model parameters. IEEE Trans. Energy Convers. 30, 226–236 (2015)

    Article  Google Scholar 

  6. 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 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  9. Muhsen, D.H., Ghazali, A.B., Khatib, T., Abed, I.A.: Parameters extraction of double diode photovoltaic module’s model based on hybrid evolutionary algorithm. Energy Convers. Manag. 105, 552–561 (2015)

    Article  Google Scholar 

  10. Chin, V.J., Salam, Z., Ishaque, K.: An accurate modelling of the two-diode model of PV module using a hybrid solution based on differential evolution. Energy Convers. Manag. 124, 42–50 (2016)

    Article  Google Scholar 

  11. Lim, H.I., Ye, Z., Ye, J., Yang, D., Du, H.: A linear identification of diode models from single I–V characteristics of PV panels. IEEE Trans. Ind. Electron. 62, 4181–4193 (2015)

    Article  Google Scholar 

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

    Article  Google Scholar 

  13. Omnia, S., Elazab, H.M., Hasanien, M.A.E., Abdeen, A.M.: Parameters estimation of single- and multiple-diode photovoltaic model using whale optimization algorithm. IET Renew. Power Gener. 12, 1755–1761 (2018)

    Article  Google Scholar 

  14. Dileep, G., Singh, S.N.: Application of soft computing techniques for maximum power point tracking of SPV system. Sol. Energy 141, 182–202 (2017)

    Article  Google Scholar 

  15. Premkumar, M., Sowmya, R.: Certain study on MPPT algorithms to track the global MPP under partial shading on solar PV module/array. Int. J. Comput. Digit. Syst. 8, 405–416 (2019)

    Article  Google Scholar 

  16. Premkumar, M., Sudhakar Babu, T., Sowmya, R.: Modified perturb and observe maximum power point technique for solar photovoltaic systems. Int. J. Sci. Technol. Res. 9, 5117–5122 (2020)

    Google Scholar 

  17. Premkumar, M., Sowmya, R.: An effective maximum power point tracker for partially shaded solar photovoltaic systems. Energy Rep. 5, 1445–1462 (2019)

    Article  Google Scholar 

  18. Diab, A.Z., Sultan, H.M., Aljendy, R., Al-Sumaiti, A.S., Shoyama, M., Ali, Z.M.: Tree growth based optimization algorithm for parameter extraction of different models of photovoltaic cells and modules. IEEE Access 8, 119668–119687 (2020)

    Article  Google Scholar 

  19. 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. Appl. (2021). https://doi.org/10.1007/s00521-021-05822-0

    Article  Google Scholar 

  20. Gao, X., Cui, Y., Hu, J., Xu, G., Yu, Y.: Lambert W-function based exact representation for double diode model of solar cells: comparison on fitness and parameter extraction. Energy Convers. Manag. 127, 443–460 (2016)

    Article  Google Scholar 

  21. El-Naggar, K.M., AlRashidi, M.R., AlHajri, M.F., Al-Othman, A.K.: Simulated annealing algorithm for photovoltaic parameters identification. Sol. Energy 86, 266–274 (2012)

    Article  Google Scholar 

  22. 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)

    Article  Google Scholar 

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

    Article  Google Scholar 

  24. Hachana, O., Hemsas, K.E., Tina, G.M., Ventura, C.: Comparison of different metaheuristic algorithms for parameter identification of photovoltaic cell/module. J. Renew. Sustain. Energy 5, 053122 (2013)

    Article  Google Scholar 

  25. Jordehi, A.R.: Time-varying acceleration coefficients particle swarm optimization (TVACPSO): a new optimization algorithm for estimating parameters of PV cells and modules. Energy Convers. Manag. 129, 262–274 (2016)

    Article  Google Scholar 

  26. Jiang, L.L., Maskell, D.L., Patra, J.C.: Parameter estimation of solar cells and modules using an improved adaptive differential evolution algorithm. Appl. Energy 112, 185–193 (2013)

    Article  Google Scholar 

  27. Zhang, Y., Lin, P., Chen, Z., Cheng, S.: A population classification evolution algorithm for the parameter extraction of solar cell models. Int. J. Photoenergy 2016, 2174573 (2016)

    Google Scholar 

  28. Askarzadeh, A., Rezazadeh, A.: Parameter identification for solar cell models using harmony search-based algorithms. Sol. Energy 86, 3241–3249 (2012)

    Article  Google Scholar 

  29. Chen, X., Yu, K., Du, W., Zhao, W., Liu, G.: Parameters identification of solar cell models using generalized oppositional teaching learning-based optimization. Energy 99, 170–180 (2016)

    Article  Google Scholar 

  30. Fathy, A., Rezk, H.: Parameter estimation of photovoltaic system using imperialist competitive algorithm. Renew. Energy 111, 307–320 (2017)

    Article  Google Scholar 

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

    Article  Google Scholar 

  32. Premkumar, M., Mohamed Ibrahim, A., Mohan Kumar, R., Sowmya, R.: Analysis and simulation of bio-inspired intelligent salp swarm MPPT method for the PV systems under partial shaded conditions. Int. J. Comput. Digit. Syst. 8, 480–488 (2019)

    Google Scholar 

  33. Lin, P., Cheng, S., Yeh, W., Chen, Z., Wu, L.: Parameters extraction of solar cell models using a modified simplified swarm optimization algorithm. Sol. Energy 144, 594–603 (2017)

    Article  Google Scholar 

  34. Alam, D.F., Yousri, D.A., Eteiba, M.B.: Flower pollination algorithm based solar PV parameter estimation. Energy Convers. Manag. 101, 410–422 (2015)

    Article  Google Scholar 

  35. Guo, L., Meng, Z., Sun, Y., Wang, L.: Parameter identification and sensitivity analysis of solar cell models with CSO algorithm. Energy Convers. Manag. 108, 520–528 (2016)

    Article  Google Scholar 

  36. Ma, J., Ting, T.O., Man, K.L., Zhang, N., Guan, S., Wong, P.W.H.: Parameter estimation of photovoltaic models via cuckoo search. J. Appl. Math. 2013, 362619 (2013)

    MathSciNet  Google Scholar 

  37. Louzazni, M., Craciunescu, A., Aroudam, E.H., Dumitrache, A.: Identification of solar cell parameters with firefly algorithm. In: Proceedings of 2nd International Conference on Mathematics and Computers in Sciences and in Industry, (pp. 7–12) (2016)

  38. Oliva, D., Abd El Aziz, M., Ella Hassanien, A.: Parameter estimation of photovoltaic cells using an chaotic whale optimization algorithm. Appl. Energy 200, 141–154 (2017)

    Article  Google Scholar 

  39. Sudhakar Babu, T., Prasanth Ram, J., Sangeetha, K., Laudani, A., Rajasekar, N.: Parameter extraction of two diode solar PV model using fireworks algorithm. Sol. Energy 140, 265–276 (2016)

    Article  Google Scholar 

  40. Ramzi, B.M.: Extraction of uncertain parameters of single and double diode model of a photovoltaic panel using Salp Swarm algorithm. Measurement 154, 107446 (2020)

    Article  Google Scholar 

  41. Ali, E.E., El-Hameed, M.A., El-Fergany, A.A., El-Arini, M.M.: Parameter extraction of photovoltaic generating units using multi-verse optimizer. Sustain. Energy Technol. Assess. 17, 68–76 (2016)

    Google Scholar 

  42. Darmansyah, D., Robandi, I.: Photovoltaic parameter estimation using grey wolf optimization. In: Proceedings of 3rd International Conference on Control, Automation and Robotics (ICCAR), Nagoya, pp. 593–597 (2017)

  43. Kanimozhi, G., Harish, K.M.: Modeling of solar cell under different conditions by ant lion optimizer with Lambert-W function. Appl. Soft Comput. 71, 141–151 (2018)

    Article  Google Scholar 

  44. Shan, J., Guoshuang, C., Changcheng, H., Hanqing, H., Mingjing, W., Ali Asghar, H., Huiling, C., Xuehua, Z.: Orthogonally adapted Harris hawks optimization for parameter estimation of photovoltaic models. Energy 203, 117804 (2020)

    Article  Google Scholar 

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

    Article  Google Scholar 

  46. 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). https://doi.org/10.1016/j.ijleo.2020.165277

    Article  Google Scholar 

  47. Şenel, F.A., Gökçe, F., Yüksel, A.S.: A novel hybrid PSO–GWO algorithm for optimization problems. Eng. Comput. 35, 1359–1373 (2019)

    Article  Google Scholar 

  48. Wen, L., Shaohong, C., Jianjun, J., Ming, X., Tiebin, W.: A new hybrid algorithm based on grey wolf optimizer and cuckoo search for parameter extraction of solar photovoltaic models. Energy Convers. Manag. 203, 112243 (2020)

    Article  Google Scholar 

  49. Manoharan, P., Jangir, P., Ravichandran, S., Elavarasan, R.M., Kumar, B.S.: Enhanced chaotic JAYA algorithm for parameter estimation of photovoltaic cell/modules. ISA Trans. (2021). https://doi.org/10.1016/j.isatra.2021.01.045

    Article  Google Scholar 

  50. Diab, A.A.Z., Sultan, H.M., Do, T.D., Kamel, O.M., Mossa, M.A.: Coyote optimization algorithm for parameters estimation of various models of solar cells and PV modules. IEEE Access 8, 11102–111140 (2020)

    Google Scholar 

  51. Afshin, F., Mohammad, H., Brent, S., Seyedali, M.: Equilibrium optimizer: a novel optimization algorithm. Knowle. Based Syst. 191, 105190 (2020)

    Article  Google Scholar 

  52. Lin, Z., Zhang, Gu.: Genetic algorithm-based parameter optimization for EO-1 Hyperion remote sensing image classification. Eur. J. Remote Sens. 53(1), 124–131 (2020)

    Article  MathSciNet  Google Scholar 

  53. Too, J., Mirjalili, S.: General learning equilibrium optimizer: a new feature selection method for biological data classification. Appl. Artif. Intell. 35(3), 247–263 (2021)

    Article  Google Scholar 

  54. Fu, Z., Hu, P., Li, W., Pan, J., Chu, S.: Parallel equilibrium optimizer algorithm and its application in capacitated vehicle routing problem. Intell. Autom. Soft Comput. 27(1), 233–247 (2021)

    Article  Google Scholar 

  55. Gao, Y., Zhou, Y., Luo, Q.: An efficient binary equilibrium optimizer algorithm for feature selection. IEEE Access 8, 140936–140963 (2020)

    Article  Google Scholar 

  56. Abdel-Basset, M., Mohamed, R., Mirjalili, S., Chakrabortty, R.K., Ryanc, M.J.: Solar photovoltaic parameter estimation using an improved equilibrium optimizer. Sol. Energy 209, 694–708 (2020)

    Article  Google Scholar 

  57. Wang, W., Wang, H., Sun, H., Rahnamayan, S.: Using opposition-based learning to enhance differential evolution: a comparative study. In: 2016 IEEE Congress on Evolutionary Computation (CEC), Vancouver, BC, Canada, pp. 71–77 (2016)

  58. Premkumar, M., Subramaniam, U., Babu, T.S., Elavarasan, R.M., Mihet-Popa, L.: Evaluation of mathematical model to characterize the performance of conventional and hybrid PV array topologies under static and dynamic shading patterns. Energies 13, 3216 (2020)

    Article  Google Scholar 

  59. Attivissimo, F., Adamo, F., Carullo, A., Lanzolla, A.M.L., Spertino, F., Vallan, A.: On the performance of the double-diode model in estimating the maximum power point for different photovoltaic technologies. Measurement 46, 3549–3559 (2013)

    Article  Google Scholar 

  60. Khanna, V., Das, B.K., Bisht, D., Singh, V.P.K.: 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 

  61. ASB-7–355 Bifacial Photovoltaic Module Datasheet ADANI: [Online]. Available: https://www.adanisolar.com/-media/Project/AdaniSolar/Media/Downloads/Downloads/Datasheets/N-Type-Elan/Bifacial-N-PERT-72-355-380.pdf

  62. ASM-7-PERC-365 Monocrystalline Photovoltaic Module Datasheet ADANI: [Online]. Available: https://www.adanisolar.com/-/media/Project/AdaniSolar/Media/Downloads/Downloads/Datasheets/Mono-Eternal/Eternal-72--Mono-PERC-365-390.pdf

  63. TP 285 Polycrystalline Photovoltaic Module Datasheet TATA: [Online]. Available: http://www.greenice.in/download/Datasheet%20-%20TP300.pdf

Download references

Author information

Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, Bannari Amman Institute of Technology, Sathyamangalam, Tamil Nadu, India

    Natarajan Shankar, Vijayarangan Kamatchi Kannan & Balasubramanian Indu Rani

  2. Department of Electronics and Communication Engineering, Dr. Mahalingam College of Engineering and Technology, Pollachi, Tamil Nadu, India

    Natarajan Saravanakumar

  3. Department of Electrical and Electronics Engineering, M. Kumarasamy College of Engineering, Karur, Tamil Nadu, India

    Chandrasekaran Kumar

Authors
  1. Natarajan Shankar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Natarajan Saravanakumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chandrasekaran Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Vijayarangan Kamatchi Kannan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Balasubramanian Indu Rani
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Natarajan Shankar.

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

Shankar, N., Saravanakumar, N., Kumar, C. et al. Opposition-based equilibrium optimizer algorithm for identification of equivalent circuit parameters of various photovoltaic models. J Comput Electron 20, 1560–1587 (2021). https://doi.org/10.1007/s10825-021-01722-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10825-021-01722-7

Keywords

Search

Navigation