Skip to main content

Advertisement

Log in

A Jaya algorithm based on self-adaptive method for parameters identification of photovoltaic cell and module

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

Accurate parameters identification of photovoltaic(PV) models is essential for state assessment of PV systems, as well as for supporting maximum power point tracking and system control, thus holding significant importance. To precisely identify parameters of different PV models, this paper proposes an improved JAYA algorithm based on self-adaptive method, termed Sjaya. Sjaya incorporates three position update strategies, all utilizing adaptive factors, automatically transitioning from explorative to exploitative behaviors, enhancing the population’s ability to escape local optima in the solution space and avoiding premature convergence. The first strategy involves learning towards the best and worst individuals in the population, with the individual iteration direction perturbed by adaptive and normal distribution probability factors to enhance population exploration. The second strategy entails learning towards superior and inferior subgroups, effectively leveraging information from the population, with the ranges of these two subgroups continuously evolving throughout the iteration process. In the third strategy, a novel individual selection mechanism is devised, allocating selection probabilities to individuals based on the exploration phase. Individual updates entail learning from three selected individuals within the population, thereby enhancing population diversity. The proposed Sjaya method is employed to address the parameters identification problem of single diode, double diode, and photovoltaic module models of various photovoltaic types. In numerical experiments, each algorithm was tested 30 times. The average root mean square error (RMSE) of Sjaya for the single diode model and double diode model of RTC France were 9.86022E-04 and 9.849674E-04, respectively. In addition, we use three PV modules to detect Sjaya and competing algorithms. The RMSE of Sjaya on the Photo Watt-PWP 201 module, STM6-40/36 module and STP6-120/36 module is 2.431177E-03, 1.772275E-03 and 1.568231E-02 respectively. The synthesis of experimental findings and analysis indicates that Sjaya outperforms other methods in terms of competitiveness, while also demonstrating high effectiveness and robustness.

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

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Fig. 1
Algorithm 1
Fig. 2
Algorithm 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

Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

References

  1. Sharma, A., Lim, W.H., El-Kenawy, E.S.M., Tiang, S.S., Bhandari, A.S., Alharbi, A.H., Khafaga, D.S.: Identification of photovoltaic module parameters by implementing a novel teaching learning based optimization with unique exemplar generation scheme (tlbo-uegs). Energy Rep. 10, 1485–1506 (2023)

    Article  Google Scholar 

  2. Yang, C., Su, C., Hu, H., Habibi, M., Safarpour, H., Khadimallah, M.A.: Performance optimization of photovoltaic and solar cells via a hybrid and efficient chimp algorithm. Solar Energy 253, 343–359 (2023)

    Article  Google Scholar 

  3. Maden, D., Çelik, E., Houssein, E.H., Sharma, G.: Squirrel search algorithm applied to effective estimation of solar pv model parameters: a real-world practice. Neural Comput. Appl. 35, 13529–13546 (2023)

    Article  Google Scholar 

  4. Premkumar, M., Jangir, P., Sowmya, R.: Parameter extraction of three-diode solar photovoltaic model using a new metaheuristic resistance-capacitance optimization algorithm and improved newton-raphson method. J. Comput. Electron. 22, 439–470 (2023)

    Google Scholar 

  5. Memon, Z.A., Akbari, M.A., Zare, M.: An improved cheetah optimizer for accurate and reliable estimation of unknown parameters in photovoltaic cell and module models. Appl. Sci. 13, 9997 (2023)

    Article  Google Scholar 

  6. Duan, Z., Yu, H., Zhang, Q., Tian, L.: Parameter extraction of solar photovoltaic model based on nutcracker optimization algorithm. Appl. Sci. 13, 6710 (2023)

    Article  Google Scholar 

  7. Choulli, I., Elyaqouti, M., Saadaoui, D., Lidaighbi, S., Elhammoudy, A., Abazine, I., et al.: Hybrid optimization based on the analytical approach and the particle swarm optimization algorithm (ana-pso) for the extraction of single and double diode models parameters. Energy 283, 129043 (2023)

    Article  Google Scholar 

  8. Ayyarao, T.S., Kishore, G.I.: Parameter estimation of solar pv models with artificial humming bird optimization algorithm using various objective functions. Soft Comput. 28, 3371–3392 (2024)

    Article  Google Scholar 

  9. Qaraad, M., Amjad, S., Hussein, N.K., Badawy, M., Mirjalili, S., Elhosseini, M.A.: Photovoltaic parameter estimation using improved moth flame algorithms with local escape operators. Comput. Electr. Eng. 106, 108603 (2023)

    Article  Google Scholar 

  10. Navarro, M.A., Oliva, D., Ramos-Michel, A., Haro, E.H.: An analysis on the performance of metaheuristic algorithms for the estimation of parameters in solar cell models. Energy Conv. Manag. 276, 116523 (2023)

    Article  Google Scholar 

  11. Ali, F., Sarwar, A., Bakhsh, F.I., Ahmad, S., Shah, A.A., Ahmed, H.: Parameter extraction of photovoltaic models using atomic orbital search algorithm on a decent basis for novel accurate rmse calculation. Energy Conv. Manag. 277, 116613 (2023)

    Article  Google Scholar 

  12. Senthilkumar, S., Mohan, V., Krithiga, G.: Brief review on solar photovoltaic parameter estimation of single and double diode model using evolutionary algorithms. Int. J. Eng. Tech. Mgmt. Res. 10, 64–78 (2023)

    Article  Google Scholar 

  13. Sharma, A., Sharma, A., Averbukh, M., Jately, V., Rajput, S., Azzopardi, B., Lim, W.H.: Performance investigation of state-of-the-art metaheuristic techniques for parameter extraction of solar cells/module. Sci. Rep. 13, 11134 (2023)

    Article  Google Scholar 

  14. Garip, Z.: Parameters estimation of three-diode photovoltaic model using fractional-order harris hawks optimization algorithm. Optik 272, 170391 (2023)

    Article  Google Scholar 

  15. Shaheen, A.M., Ginidi, A.R., El-Sehiemy, R.A., El-Fergany, A., Elsayed, A.M.: Optimal parameters extraction of photovoltaic triple diode model using an enhanced artificial gorilla troops optimizer. Energy 283, 129034 (2023)

    Article  Google Scholar 

  16. Beşkirli, A., Dağ, İ: Parameter extraction for photovoltaic models with tree seed algorithm. Energy Rep. 9, 174–185 (2023)

    Article  Google Scholar 

  17. Gu, Z., Xiong, G., Fu, X., Mohamed, A.W., Al-Betar, M.A., Chen, H., Chen, J.: Extracting accurate parameters of photovoltaic cell models via elite learning adaptive differential evolution. Energy Conv. Manag. 285, 116994 (2023)

    Article  Google Scholar 

  18. Chandrasekaran, K., Thaveedhu, A.S.R., Manoharan, P., Periyasamy, V.: Optimal estimation of parameters of the three-diode commercial solar photovoltaic model using an improved berndt-hall-hall-hausman method hybridized with an augmented mountain gazelle optimizer. Environ. Sci. Pollut. Res. 30, 57683–57706 (2023)

    Article  Google Scholar 

  19. El-Sehiemy, R., Shaheen, A., El-Fergany, A., Ginidi, A.: Electrical parameters extraction of pv modules using artificial hummingbird optimizer. Sci. Rep. 13, 9240 (2023)

    Article  Google Scholar 

  20. Zhu, D., Shen, J., Zhang, Y., Li, W., Zhu, X., Zhou, C., Cheng, S., Yao, Y.: Multi-strategy particle swarm optimization with adaptive forgetting for base station layout. Swarm Evol. Comput. 91, 101737 (2024)

    Article  Google Scholar 

  21. Zhu, D., Wang, S., Zhou, C., Yan, S.: Manta ray foraging optimization based on mechanics game and progressive learning for multiple optimization problems. Appl. Soft Comput. 145, 110561 (2023)

    Article  Google Scholar 

  22. Abd El-Mageed, A.A., Abohany, A.A., Saad, H.M., Sallam, K.M.: Parameter extraction of solar photovoltaic models using queuing search optimization and differential evolution. Appl. Soft. Comput. 134, 110032 (2023)

    Article  Google Scholar 

  23. Gu, Z., Xiong, G., Fu, X.: Parameter extraction of solar photovoltaic cell and module models with metaheuristic algorithms: a review. Sustainability 15, 3312 (2023)

    Article  Google Scholar 

  24. Zhu, D., Wang, S., Shen, J., Zhou, C., Li, T., Yan, S.: A multi-strategy particle swarm algorithm with exponential noise and fitness-distance balance method for low-altitude penetration in secure space. J. Comput. Sci. 74, 102149 (2023)

    Article  Google Scholar 

  25. Zhu, D., Wang, S., Zhou, C., Yan, S., Xue, J.: Human memory optimization algorithm: a memory-inspired optimizer for global optimization problems. Expert Syst. Appl. 237, 121597 (2024)

    Article  Google Scholar 

  26. Bogar, E.: Chaos game optimization-least squares algorithm for photovoltaic parameter estimation. Arab. J. Sci. Eng. 48, 6321–6340 (2023)

    Article  Google Scholar 

  27. Arandian, B., Eslami, M., Khalid, S.A., Khan, B., Sheikh, U.U., Akbari, E., Mohammed, A.H.: An effective optimization algorithm for parameters identification of photovoltaic models. IEEE Access 10, 34069–34084 (2022)

    Article  Google Scholar 

  28. Bakır, H.: Comparative performance analysis of metaheuristic search algorithms in parameter extraction for various solar cell models. Environ. Challen. 11, 100720 (2023)

    Article  Google Scholar 

  29. Long, W., Wu, T., Xu, M., Tang, M., Cai, S.: Parameters identification of photovoltaic models by using an enhanced adaptive butterfly optimization algorithm. Energy 229, 120750 (2021)

    Article  Google Scholar 

  30. Mohamed, M.A.E., Nasser Ahmed, S., Eladly Metwally, M.: Arithmetic optimization algorithm based maximum power point tracking for grid-connected photovoltaic system. Sci. Rep. 13, 5961 (2023)

    Article  Google Scholar 

  31. Yang, B., Wang, J., Zhang, X., Yu, T., Yao, W., Shu, H., Zeng, F., Sun, L.: Comprehensive overview of meta-heuristic algorithm applications on pv cell parameter identification. Energy Conv. Manag. 208, 112595 (2020)

    Article  Google Scholar 

  32. Aribia, H.B., El-Rifaie, A.M., Tolba, M.A., Shaheen, A., Moustafa, G., Elsayed, F., Elshahed, M.: Growth optimizer for parameter identification of solar photovoltaic cells and modules. Sustainability 15, 7896 (2023)

    Article  Google Scholar 

  33. El-Dabah, M.A., El-Sehiemy, R.A., Hasanien, H.M., Saad, B.: Photovoltaic model parameters identification using northern goshawk optimization algorithm. Energy 262, 125522 (2023)

    Article  Google Scholar 

  34. Yu, X., Hu, Z., Wang, X., Luo, W.: Ranking teaching-learning-based optimization algorithm to estimate the parameters of solar models. Eng. Appl. Artif. Intell. 123, 106225 (2023)

    Article  Google Scholar 

  35. Gu, Q., Li, S., Gong, W., Ning, B., Hu, C., Liao, Z.: L-shade with parameter decomposition for photovoltaic modules parameter identification under different temperature and irradiance. Appl. Soft Comput. 143, 110386 (2023)

    Article  Google Scholar 

  36. Satria, H., Syah, R.B., Nehdi, M.L., Almustafa, M.K., Adam, A.O.I.: Parameters identification of solar pv using hybrid chaotic northern goshawk and pattern search. Sustainability 15, 5027 (2023)

    Article  Google Scholar 

  37. Yu, X., Duan, Y., Cai, Z.: Sub-population improved grey wolf optimizer with gaussian mutation and lévy flight for parameters identification of photovoltaic models. Expert Syst. Appl. 232, 120827 (2023)

    Article  Google Scholar 

  38. Elhammoudy, A., Elyaqouti, M., Hmamou, D.B., Lidaighbi, S., Saadaoui, D., Choulli, I., Abazine, I., et al.: Dandelion optimizer algorithm-based method for accurate photovoltaic model parameter identification. Energy Conv. Manag. X 19, 100405 (2023)

    Google Scholar 

  39. Lu, Y., Liang, S., Ouyang, H., Li, S., Wang, G.G.: Hybrid multi-group stochastic cooperative particle swarm optimization algorithm and its application to the photovoltaic parameter identification problem. Energy Rep. 9, 4654–4681 (2023)

    Article  Google Scholar 

  40. Premkumar, M., Jangir, P., Elavarasan, R.M., Sowmya, R.: Opposition decided gradient-based optimizer with balance analysis and diversity maintenance for parameter identification of solar photovoltaic models. J. Ambient Intell. Humaniz. Comput. 14, 7109–7131 (2023)

    Article  Google Scholar 

  41. Dehghani, M., Trojovská, E., Trojovskỳ, P.: A new human-based metaheuristic algorithm for solving optimization problems on the base of simulation of driving training process. Sci. Rep. 12, 9924 (2022)

    Article  Google Scholar 

  42. Naik, A., Satapathy, S.C.: Past present future: a new human-based algorithm for stochastic optimization. Soft Comput. 25, 12915–12976 (2021)

    Article  Google Scholar 

  43. Trojovskỳ, P.: A new human-based metaheuristic algorithm for solving optimization problems based on preschool education. Sci. Rep. 13, 21472 (2023)

    Article  Google Scholar 

  44. Singh, A.P., Kumar, G., Dhillon, G.S., Taneja, H.: Hybridization of chaos theory and dragonfly algorithm to maximize spatial area coverage of swarm robots. Evol. Intell. 17, 1327–1340 (2024)

    Article  Google Scholar 

  45. Aydemir, S.B.: A novel arithmetic optimization algorithm based on chaotic maps for global optimization. Evol. Intell. 16, 981–996 (2023)

    Article  Google Scholar 

  46. Rao, R.: Jaya: a simple and new optimization algorithm for solving constrained and unconstrained optimization problems. Int. J. Ind. Eng. Comput. 7, 19–34 (2016)

    Google Scholar 

  47. Yu, K., Liang, J., Qu, B., Chen, X., Wang, H.: Parameters identification of photovoltaic models using an improved jaya optimization algorithm. Energy Conv. Manag. 150, 742–753 (2017)

    Article  Google Scholar 

  48. 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 

  49. Yu, X., Wu, X., Luo, W.: Parameter identification of photovoltaic models by hybrid adaptive jaya algorithm. Mathematics 10, 183 (2022)

    Article  Google Scholar 

  50. Abdel-Basset, M., Mohamed, R., Chakrabortty, R.K., Sallam, K., Ryan, M.J.: An efficient teaching-learning-based optimization algorithm for parameters identification of photovoltaic models: analysis and validations. Energy Conv. Manag. 227, 113614 (2021)

    Article  Google Scholar 

  51. Ahmed, W.A.E.M., Mageed, H.M.A., Mohamed, S.A., Saleh, A.A.: Fractional order darwinian particle swarm optimization for parameters identification of solar pv cells and modules. Alex. Eng. J. 61, 1249–1263 (2022)

    Article  Google Scholar 

  52. Huang, T., Zhang, C., Ouyang, H., Luo, G., Li, S., Zou, D.: Parameter identification for photovoltaic models using an improved learning search algorithm. Ieee Access 8, 116292–116309 (2020)

    Article  Google Scholar 

  53. Shaheen, A.M., El-Seheimy, R.A., Xiong, G., Elattar, E., Ginidi, A.R.: Parameter identification of solar photovoltaic cell and module models via supply demand optimizer. Ain Shams Eng. J. 13, 101705 (2022)

    Article  Google Scholar 

  54. Xiong, G., Li, L., Mohamed, A.W., Yuan, X., Zhang, J.: A new method for parameter extraction of solar photovoltaic models using gaining-sharing knowledge based algorithm. Energy Rep. 7, 3286–3301 (2021)

    Article  Google Scholar 

  55. Duman, S., Kahraman, H.T., Sonmez, Y., Guvenc, U., Kati, M., Aras, S.: A powerful meta-heuristic search algorithm for solving global optimization and real-world solar photovoltaic parameter estimation problems. Eng. Appl. Artif. Intell. 111, 104763 (2022)

    Article  Google Scholar 

  56. Chen, X., Tianfield, H., Mei, C., Du, W., Liu, G.: Biogeography-based learning particle swarm optimization. Soft Comput. 21, 7519–7541 (2017)

    Article  Google Scholar 

  57. Wu, R., Huang, H., Wei, J., Ma, C., Zhu, Y., Chen, Y., Fan, Q.: An improved sparrow search algorithm based on quantum computations and multi-strategy enhancement. Expert Syst. Appl. 215, 119421 (2023)

    Article  Google Scholar 

Download references

Funding

The National Natural Science Foundation of China (Nos. 62272418, 62102058), Zhejiang Provincial Natural Science Foundation Major Project (NO. LD24F020004), The Major Open Project of Key Laboratory for Advanced Design and Intelligent Computing of the Ministry of Education (NO. ADIC2023ZD001).

Author information

Authors and Affiliations

Authors

Contributions

Zhiyu Feng: Conceptualization, Data Curation, Software, Visualization, Validation, Writing- original draft; Donglin Zhu: Methodology, Formal analysis, review; Huaiyu Guo: Investigation, Project; Jiankai Xue: Investigation, Formal analysis; Changjun Zhou: Funding acquisition, Supervision.

Corresponding author

Correspondence to Changjun Zhou.

Ethics declarations

Conflict of interest

All the authors certify that there is no Conflict of interest with any individual or organization for the present work.

Ethical and informed consent for data used

Not applicable.

Additional information

Publisher's Note

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

Appendix A

Appendix A

See Figs. 23, 24, 25, 26, 27.

Fig. 23
figure 23

Comparisons between experimental and calculated data of Sjaya for single diode model of RTC France

Fig. 24
figure 24

Comparisons between experimental and calculated data of Sjaya for double diode model of RTC France

Fig. 25
figure 25

Comparisons between experimental and calculated data of Sjaya for Photo Watt-PWP 201

Fig. 26
figure 26

Comparisons between experimental and calculated data of Sjaya for STM6-40/36

Fig. 27
figure 27

Comparisons between experimental and calculated data of Sjaya for STP6-120/36

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Feng, Z., Zhu, D., Guo, H. et al. A Jaya algorithm based on self-adaptive method for parameters identification of photovoltaic cell and module. Cluster Comput 28, 145 (2025). https://doi.org/10.1007/s10586-024-04877-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10586-024-04877-7

Keywords

Navigation