Abstract
Analysis and modeling of photovoltaic (PV) solar cells and modules based on experimentally measured data are critical for optimizing their design. The need for new algorithms to optimize the PV parameters, many of which owe their inspiration to the metaheuristic search concepts, is still a principal subject of interest and discussion. In this paper, an optimization algorithm that simulates the identity formation behavior of adolescents in the peer group, namely the adolescent identity search algorithm (AISA), was applied to identify the unknown parameters of PV models. In AISA, the updating process proceeds in the exploitation and exploration stages as follows. First, the new best position is generated by identifying and imitating the best identity features of a selected peer from the group to accelerate the exploitation process and produce better performance using a dynamic selection strategy. Second, any locally optimal solution is avoided in the exploration stage for the global optimal solution by adopting the negative/undesirable identity features observed in the peer group. In this context, AISA is applied to identify the unknown parameters of various benchmark test PV models, i.e., single-diode, double-diode, and PV module models. Obtained results showed that this algorithm performed very accurately since lower values of root mean square errors (RMSE) are achieved \((9.8602\times 10^{-4},2.4251\times 10^{-3},1.7298\times 10^{-3},1.6212\times 10^{-2},\ and\ 6.3666\times 10^{-4})\) when compared with other competitor algorithms. Further, a lower RMSE \((9.79352834\times 10^{-4})\) was obtained in the case of the double-diode model by adapting some parameters ranges. Also, the high closeness between the simulated current–voltage (I–V) curve is achieved by AISA compared with the experimental data.
Similar content being viewed by others
Data availability statement
The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.
References
Smets, A., Jäger, K., Isabella, O., van Swaaij, R., Zeman, M.: Solar Energy: The physics and engineering of photovoltaic conversion, technologies and systems. UIT Cambridge Limited, Cambridge (2016)
Easwarakhanthan, T., Bottin, J., Bouhouch, I., Boutrit, C.: Nonlinear minimization algorithm for determining the solar cell parameters with microcomputers. International Journal of Solar Energy 4(1), 1–12 (1986)
Sharma, A., Sharma, A., Dasgotra, A., Dasgotra, A., Jately, V., Ram, M., Rajput, S., Averbukh, M., Azzopardi, B.: An Effective Method for Parameter Estimation of Solar PV Cell Using Grey-Wolf Optimization Technique. International Journal of Mathematical, Engineering and Management Sciences 06, 911–931 (2021)
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)
Jordehi, A.R.: Parameter estimation of solar photovoltaic (PV) cells: A review. Renew. Sustain. Energy Rev. 61, 354–371 (2016)
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)
Eberhart, Russell, Kennedy, James: A new optimizer using particle swarm theory. In: MHS’95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science, pp. 39–43. IEEE (1995)
Trelea, I.C.: The particle swarm optimization algorithm: convergence analysis and parameter selection. Inf. Process. Lett. 85(6), 317–325 (2003)
Ye, M., Wang, X., Yousheng, X.: Parameter extraction of solar cells using particle swarm optimization. J. Appl. Phys. 105(9), 094502 (2009)
Ishaque, K., Salam, Z.: An improved modeling method to determine the model parameters of photovoltaic (PV) modules using differential evolution (DE). Sol. Energy 85(9), 2349–2359 (2011)
Jordehi, A.R.: Time varying acceleration coefficients particle swarm optimisation (TVACPSO): A new optimisation algorithm for estimating parameters of PV cells and modules. Energy Convers. Manage. 129, 262–274 (2016)
Mughal, M.A., Ma, Q., Xiao, C.: Photovoltaic cell parameter estimation using hybrid particle swarm optimization and simulated annealing. Energies 10(8), 1213 (2017)
Jordehi, A.R.: Enhanced leader particle swarm optimisation (ELPSO): An efficient algorithm for parameter estimation of photovoltaic (PV) cells and modules. Sol. Energy 159, 78–87 (2018)
Liang, J., Ge, S., Boyang, Q., Kunjie, Yu., Liu, F., Yang, H., Wei, P., Li, Z.: Classified perturbation mutation based particle swarm optimization algorithm for parameters extraction of photovoltaic models. Energy Convers. Manage. 203, 112138 (2020)
Ebrahimi, S.M., Salahshour, E., Malekzadeh, M., Gordillo, F.: Parameters identification of PV solar cells and modules using flexible particle swarm optimization algorithm. Energy 179, 358–372 (2019)
Wei, Huang, Cong, Jiang, Lingyun, Xue, Deyun, Song: Extracting solar cell model parameters based on chaos particle swarm algorithm. In: 2011 International Conference on Electric Information and Control Engineering. IEEE (2011)
Yousri, D., Thanikanti, S.B., Allam, D., Ramachandaramurthy, V.K., Eteiba, M.B.: Fractional chaotic ensemble particle swarm optimizer for identifying the single, double, and three diode photovoltaic models’ parameters. Energy 195, 116979 (2020)
Rajasekar, N., Kumar, N.K., Venugopalan, R.: Bacterial foraging algorithm based solar PV parameter estimation. Sol. Energy 97, 255–265 (2013)
Passino, K.M.: Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control. Syst. 22(3), 52–67 (2002)
Awadallah, M.A.: Variations of the bacterial foraging algorithm for the extraction of PV module parameters from nameplate data. Energy Convers. Manage. 113, 312–320 (2016)
Oliva, D., Cuevas, E., Pajares, G.: Parameter identification of solar cells using artificial bee colony optimization. Energy 72, 93–102 (2014)
Karaboga, D., Basturk, B.: On the performance of artificial bee colony (ABC) algorithm. Appl. Soft Comput. 8(1), 687–697 (2008)
Chen, X., Bin, X., Mei, C., Ding, Y., Li, K.: Teaching-learning-based artificial bee colony for solar photovoltaic parameter estimation. Appl. Energy 212, 1578–1588 (2018)
Oliva, D., Ewees, A.A., Abd El Aziz, M., Hassanien, A.E., Peréz-Cisneros, M.: A chaotic improved artificial bee colony for parameter estimation of photovoltaic cells. Energies 10(7), 865 (2017)
Chen, M.-R., Chen, J.-H., Zeng, G.-Q., Kang-Di, L., Jiang, X.-F.: An improved artificial bee colony algorithm combined with extremal optimization and Boltzmann selection probability. Swarm Evol. Comput. 49, 158–177 (2019)
Hasanien, H.M.: Shuffled frog leaping algorithm for photovoltaic model identification. IEEE Transactions on Sustainable Energy 6(2), 509–515 (2015)
Eusuff, M., Lansey, K., Pasha, F.: Shuffled frog-leaping algorithm: a memetic meta-heuristic for discrete optimization. Eng. Optim. 38(2), 129–154 (2006)
Elazab, O.S., Hasanien, H.M., Elgendy, M.A., Abdeen, A.M.: Parameters estimation of single- and multiple-diode photovoltaic model using whale optimisation algorithm. IET Renew. Power Gener. 12(15), 1755–1761 (2018)
Mirjalili, S., Lewis, A.: The whale optimization algorithm. Adv. Eng. Softw. 95, 51–67 (2016)
Oliva, D., Abd El Aziz, M., Hassanien, A.E.: Parameter estimation of photovoltaic cells using an improved chaotic whale optimization algorithm. Appl. Energy 200, 141–154 (2017)
Xiong, G., Zhang, J., Yuan, X., Shi, D., He, Yu., Yao, G.: Parameter extraction of solar photovoltaic models by means of a hybrid differential evolution with whale optimization algorithm. Sol. Energy 176, 742–761 (2018)
Deotti, L.M.P., Pereira, J.L.R., da Silva Júnior, I.C.: Parameter extraction of photovoltaic models using an enhanced Lévy flight bat algorithm. Energy Convers. Manage. 221, 113114 (2020)
Yang, Xin-She: A new metaheuristic bat-inspired algorithm. In: Nature Inspired Cooperative Strategies for Optimization (NICSO 2010), pp. 65–74. Springer Berlin Heidelberg (2010)
Nayak, B., Mohapatra, A., Mohanty, K.B.: Parameter estimation of single diode PV module based on GWO algorithm. Renew. Energy Focus 30, 1–12 (2019)
Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Adv. Eng. Softw. 69, 46–61 (2014)
Long, W., Cai, S., Jiao, 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. Manage. 203, 112243 (2020)
Pan, J., Gao, Y., Qian, Q., Feng, Y., Fu, Y., sun, M., Sardari, F.: Parameters identification of photovoltaic cells using improved version of the chaotic grey wolf optimizer. Optik 242, 167150 (2021)
Jiao, S., Chong, G., Huang, C., Hanqing, H., Wang, M., Heidari, A.A., Chen, H., Zhao, X.: Orthogonally adapted Harris hawks optimization for parameter estimation of photovoltaic models. Energy 203, 117804 (2020)
Heidari, A.A., Mirjalili, S., Faris, H., Aljarah, I., Mafarja, M., Chen, H.: Harris hawks optimization: algorithm and applications. Futur. Gener. Comput. Syst. 97, 849–872 (2019)
Chen, H., Jiao, S., Wang, M., Heidari, A.A., Zhao, X.: Parameters identification of photovoltaic cells and modules using diversification-enriched Harris hawks optimization with chaotic drifts. J. Clean. Prod. 244, 118778 (2020)
Qais, M.H., Hasanien, H.M., Alghuwainem, S.: Parameters extraction of three-diode photovoltaic model using computation and Harris hawks optimization. Energy 195, 117040 (2020)
Ridha, H.M., Heidari, A.A., Wang, M., Chen, H.: Boosted mutation-based Harris hawks optimizer for parameters identification of single-diode solar cell models. Energy Convers. Manage. 209, 112660 (2020)
Alabool, Hamzeh Mohammad, Alarabiat, Deemah, Abualigah, Laith, Heidari, Ali Asghar: Harris hawks optimization: a comprehensive review of recent variants and applications. Neural Computing and Applications (2021)
Long, W., Tiebin, W., Ming, X., Tang, M., Cai, S.: Parameters identification of photovoltaic models by using an enhanced adaptive butterfly optimization algorithm. Energy 229, 120750 (2021)
Arora, S., Singh, S.: Butterfly optimization algorithm: a novel approach for global optimization. Soft. Comput. 23(3), 715–734 (2018)
Bogar, E., Beyhan, S.: Adolescent identity search algorithm (AISA): a novel metaheuristic approach for solving optimization problems. Appl. Soft Comput. 95, 106503 (2020)
Jose, J., Gautam, N., Tiwari, M., Tiwari, T., Suresh, A., Sundararaj, V., Rejeesh, M.R.: An image quality enhancement scheme employing adolescent identity search algorithm in the NSST domain for multimodal medical image fusion. Biomed. Signal Process. Control 66, 102480 (2021)
Wolpert, D.H., Macready, W.G.: No free lunch theorems for optimization. IEEE Trans. Evol. Comput. 1(1), 67–82 (1997)
Patra, J.C., Kot, A.C.: Nonlinear dynamic system identification using Chebyshev functional link artificial neural networks. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 32(4), 505–511 (2002)
Çetin, M., Bahtiyar, B., Beyhan, S.: Adaptive uncertainty compensation-based nonlinear model predictive control with real-time applications. Neural Comput. Appl. 31(S2), 1029–1043 (2017)
Chen, X., Yue, H., Kunjie, Yu.: Perturbed stochastic fractal search for solar PV parameter estimation. Energy 189, 116247 (2019)
Li, S., Qiong, G., Gong, W., Ning, B.: An enhanced adaptive differential evolution algorithm for parameter extraction of photovoltaic models. Energy Convers. Manage. 205, 112443 (2020)
Xiong, G., Zhang, J., Shi, D., Zhu, L., Yuan, X., Tan, Z.: Winner-leading competitive swarm optimizer with dynamic gaussian mutation for parameter extraction of solar photovoltaic models. Energy Convers. Manage. 206, 112450 (2020)
Zhang, Y., Ma, M., Jin, Z.: Comprehensive learning Jaya algorithm for parameter extraction of photovoltaic models. Energy 211, 118644 (2020)
Liu, Y., Chong, G., Heidari, A.A., Chen, H., Liang, G., Ye, X., Cai, Z., Wang, M.: Horizontal and vertical crossover of Harris hawk optimizer with Nelder-Mead simplex for parameter estimation of photovoltaic models. Energy Convers. Manage. 223, 113211 (2020)
Xiong, G., Zhang, J., Shi, D., He, Yu.: Parameter extraction of solar photovoltaic models using an improved whale optimization algorithm. Energy Convers. Manage. 174, 388–405 (2018)
Sharma, A., Sharma, A., Moshe, A., Raj, N., Pachauri, R.K.: An effective method for parameter estimation of solar PV cell using Grey-wolf optimization technique. Int. J. Math. Eng. Manage. Sci. 06, 911–931 (2021)
Sharma, A., Sharma, A., Averbukh, M., Jately, V., Azzopardi, B.: An effective method for parameter estimation of a solar cell. Electronics 10, 312 (2021)
Naeijian, M., Rahimnejad, A., Ebrahimi, S.M., Pourmousa, N., Gadsden, S.A.: Parameter estimation of PV solar cells and modules using Whippy Harris hawks optimization algorithm. Energy Rep. 07, 4047–4063 (2021)
Guojiang, X., Lei, L., Wagdy, M.A., Xufeng, Y., Jing, Z.: A new method for parameter extraction of solar photovoltaic models using gaining-sharing knowledge based algorithm. Energy Rep. 07, 3286–3301 (2021)
Acknowledgements
The authors gratefully acknowledge the DGRSDT Science Foundation of Algeria for the financial support.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Lekouaghet, B., Khelifa, M.A. & Boukabou, A. Adolescent identity search algorithm for parameter extraction in photovoltaic solar cells and modules. J Comput Electron 21, 859–881 (2022). https://doi.org/10.1007/s10825-022-01881-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10825-022-01881-1