Top

Journal of Computational Electronics

Published in:

06-08-2024

Estimating equivalent circuit parameters in various photovoltaic models and modules using the dingo optimization algorithm

Authors: Hasan Temurtaş, Gürcan Yavuz, Serdar Özyön, Aybüke Ünlü

Published in: Journal of Computational Electronics | Issue 5/2024

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

While the demand for electrical energy in the world increases daily, a large part of this demand is still provided by fossil fuels. However, the most significant contribution to solving the economic and environmental problems that arise is the spread of renewable energy production systems. Solar power generation systems are one of these renewable energy generation systems. In this study, cell and module parameters are modeled and estimated in different ways to obtain maximum energy from solar cells used in solar power generation systems. Cell and model vendors need to provide complete information to the end user. Therefore, the systems created turn into a nonlinear problem with many unknown parameters. In this study, single-diode model (SDM), double-diode model (DDM), and triple diode model (TDM) for photovoltaic (PV) cells as well as parameter estimations of four different PV modules produced by other vendors were performed for the first time with the dingo optimization algorithm (DOA). The mathematical model of PV module parameters is derived using open-circuit voltage (V_oc), short-circuit current (I_sc), and maximum power point values (P_mpp). The parameter values obtained by the algorithm aim to get the maximum power point curve for each model and module with minimum error. These values are compared with five traditional and five recent meta-heuristic algorithms, which have extreme positions in the literature.

previous article Precise parameter estimation of PEM fuel cell via weighted mean of vectors optimizer

next article A new behavioral-level model of superconducting Josephson junctions with Simulink

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Chen, X., Yu, K.: Hybridizing cuckoo search algorithm with biogeography-based optimization for estimating photovoltaic model parameters. Sol. Energy 180, 192–206 (2019). https://doi.org/10.1016/j.solener.2019.01.025CrossRef

Li, S., Gong, W., Yan, X., Hu, C., Bai, D., Wang, L.: Parameter estimation of photovoltaic models with memetic adaptive differential evolution. Sol. Energy 190, 465–474 (2019). https://doi.org/10.1016/j.solener.2019.08.022CrossRef

Chen, H., Jiao, S., Heidari, A.A., Wang, M., Chen, X., Zhao, X.: An opposition-based sine cosine approach with local search for parameter estimation of photovoltaic models, energy convers. Manage 195, 927–942 (2019). https://doi.org/10.1016/j.enconman.2019.05.057CrossRef

Namazov, R.A., Popal, X.M.: The role of solar panels in energy production. Ecoenergetics 4, 51–55 (2022)

Abdel-Basset, M., El-Shahat, D., Chakrabortty, R.K., Ryan, M.: Parameter estimation of photovoltaic models using an improved marine predators algorithm. Energy Convers. Manage. 227, 113491 (2021). https://doi.org/10.1016/j.enconman.2020.113491CrossRef

Kumar, C., Mary, D.M.: Parameter estimation of three-diode solar photovoltaic model using an Improved-African Vultures optimization algorithm with Newton-Raphson method. J. Comput. Electron. 20, 2563–2593 (2021). https://doi.org/10.1007/s10825-021-01812-6CrossRef

Wang, S., Yu, Y., Hu, W.: Static and dynamic solar photovoltaic models’ parameters estimation using hybrid Rao optimization algorithm. J. Clean Prod. 315, 128080 (2021). https://doi.org/10.1016/J.JCLEPRO.2021.128080CrossRef

Chen, Z., Wu, L., Lin, P., Wu, Y., Cheng, S.: Parameters identification of photovoltaic models using hybrid adaptive Nelder-Mead simplex algorithm based on eagle strategy. Appl. Energy 182, 47–57 (2016). https://doi.org/10.1016/j.apenergy.2016.08.083CrossRef

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). https://doi.org/10.1016/j.rser.2015.11.051CrossRef

10.

Abbassi, R., Abbassi, A., Jemli, M., Chebbi, S.: Identification of unknown parameters of solar cell models: a comprehensive overview of available approaches. Renew. Sustain. Energy Rev. 90, 453–474 (2018). https://doi.org/10.1016/j.rser.2018.03.011CrossRef

11.

Abbassi, A., Gammoudi, R., Dami, M.A., Hasnaoui, O., Jemli, M.: An improved single-diode model parameters extraction at different operating conditions with a view to modeling a photovoltaic generator: a comparative study. Sol. Energy 155, 478–489 (2019). https://doi.org/10.1016/j.solener.2017.06.057CrossRef

12.

Khanna, V., Das, B.K., Bisht Vandana, D., Singh, 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). https://doi.org/10.1016/j.renene.2014.12.072CrossRef

13.

Allam, D., Yousri, D.A., Eteiba, M.B.: Parameters extraction of the three diode model for the multi-crystalline solar cell/module using Moth-Flame optimization algorithm. Energy Convers. Manage. 123, 535–548 (2016). https://doi.org/10.1016/j.enconman.2016.06.052CrossRef

14.

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 Convers. Manage. 208, 112595 (2020). https://doi.org/10.1016/j.enconman.2020.112595CrossRef

15.

Toledo, F.J., Galiano, V., Blanes, J.M., Herranz, V., Batzelis, E.: Photovoltaic single-diode model parametrization an application to the calculus of the Euclidean distance to an I-V curve. Math. Comput. Simul. (2023). https://doi.org/10.1016/j.matcom.2023.01.005CrossRef

16.

Kumar, C., Mary, D.M.: A novel chaotic-driven Tuna Swarm Optimizer with Newton-Raphson method for parameter identification of three-diode equivalent circuit model of solar photovoltaic cells/modules. Optik 264, 169379 (2022). https://doi.org/10.1016/j.ijleo.2022.169379CrossRef

17.

Ayang, A., Wamkeue, R., Ouhrouche, M., Djongyang, N., Salome, N.E., Pombe, J.K., Ekemb, G.: Maximum likelihood parameters estimation of single-diode model of photovoltaic generator. Renew. Energy 130, 111–121 (2019). https://doi.org/10.1016/j.renene.2018.06.039CrossRef

18.

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. Manage. 127, 443–460 (2016). https://doi.org/10.1016/j.enconman.2016.09.005CrossRef

19.

Ridha, H.M.: Parameters extraction of single and double diodes photovoltaic models using marine predators algorithm and Lambert W function. Sol. Energy 209, 674–693 (2020). https://doi.org/10.1016/j.solener.2020.09.047CrossRef

20.

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). https://doi.org/10.1016/j.energy.2020.117040CrossRef

21.

Rawat, N., Thakur, P., Singh, A.K., Bansal, R.C.: Performance analysis of solar PV parameter estimation techniques. Optik 279, 170785 (2023). https://doi.org/10.1016/j.ijleo.2023.170785CrossRef

22.

Halim, A.H., Ismail, I., Das, S.: Performance assessment of the metaheuristic optimization algorithms: an exhaustive review. Artif. Intell. Rev. 54, 2323–2409 (2020). https://doi.org/10.1007/S10462-020-09906-6CrossRef

23.

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

24.

Jadli, U., Thakur, P., Shukla, R.D.: A new parameter estimation method of solar photovoltaic. IEEE J. Photovolt. 8, 239–247 (2018). https://doi.org/10.1109/JPHOTOV.2017.2767602CrossRef

25.

El-Fergany, A.A.: Parameters identification of PV model using improved slime mould optimizer and Lambert W-function. Energy Rep. 7, 875–887 (2021). https://doi.org/10.1016/j.egyr.2021.01.093CrossRef

26.

Reis, L.R.D., Camacho, J.R., Novacki, D.F.: The newton raphson method in the extraction of parameters of PV modules. Renew. Energy Power Qual. J. 1, 634–639 (2017). https://doi.org/10.24084/repqj15CrossRef

27.

Rawat, N., Thakur, P.: Parameter extraction of PV solar cell: a comparative assessment using newton raphson, simulated annealing, and particle swarm optimization. J. Graph. Era. Univ. 7, 119–131 (2019)

28.

Durmuş, B., Gün, A.: Development of incremental average differential evolution algorithm for photovoltaic system identification. Sol. Energy 244, 242–254 (2022). https://doi.org/10.1016/j.solener.2022.08.046CrossRef

29.

Qaraad, M., Amjad, S., Hussein, N.H., 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). https://doi.org/10.1016/j.compeleceng.2023.108603CrossRef

30.

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). https://doi.org/10.1016/j.egyr.2023.03.105CrossRef

31.

Wang, R.: Parameter identification of photovoltaic cell model based on enhanced particle swarm optimization. Sustainability 13(2), 840 (2021). https://doi.org/10.3390/su13020840CrossRef

32.

Shankar, N., Saravanakumar, N., Kumar, C., Kannan, V.K., Rani, B.I.: 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/s10825-021-01722-7CrossRef

33.

Zhang, Y., Lyden, S., León de la Barra, B. A., Haque, M. E.: A genetic algorithm approach to parameter estimation for PV modules. In: 2016 IEEE Power and Energy Society General Meeting (PESGM), pp. 16464203.IEEE, Boston, USA (2016). https://doi.org/10.1109/PESGM.2016.7741781

34.

Hamid, N., Abounacer, R., Idali Oumhand, M., Feddaoui, M.B., Agliz, D.: Parameters identification of photovoltaic solar cells and module using the genetic algorithm with convex combination crossover. Int J Ambient Energy 40(5), 517–524 (2018). https://doi.org/10.1080/01430750.2017.1421577CrossRef

35.

Pillai, D.S., Rajasekar, N.: Metaheuristic algorithms for PV parameter identification: A comprehensive review with an application to threshold setting for fault detection in PV systems. Renew. Sustain. Energy Rev. 82(3), 3503–3525 (2018). https://doi.org/10.1016/j.rser.2017.10.107CrossRef

36.

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/s10825-021-01796-3CrossRef

37.

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). https://doi.org/10.1016/j.isatra.2021.01.045CrossRef

38.

Messaoud, R.B.: Extraction of uncertain parameters of double-diode model of a photovoltaic panel using Ant Lion Optimization. SN Appl. Sci. (2020). https://doi.org/10.1007/s42452-020-2013-zCrossRef

39.

Ye, X., Liu, W., Li, H., Wang, M., Chi, C., Liang, G., Chen, H., Huang, H.: Modified whale optimization algorithm for solar cell and PV module parameter identification. Complexity 2021(8878686), 1–23 (2021). https://doi.org/10.1155/2021/8878686CrossRef

40.

Li, S., Gong, W., Gu, Q.: A comprehensive survey on meta-heuristic algorithms for parameter extraction of photovoltaic models. Renew. Sustain. Energy Rev. 141, 110828 (2021). https://doi.org/10.1016/j.rser.2021.110828CrossRef

41.

Yang, X., Gong, W., Wang, L.: Comparative study on parameter extraction of photovoltaic models via differential evolution. Energy Convers. Manage. 201, 112113 (2019). https://doi.org/10.1016/j.enconman.2019.112113CrossRef

42.

Peraza-Vazquez, H., Pena-Delgado, A.F., Echavarria-Castillo, G., Morales-Cepeda, A.B., Velaasco-Alvarez, J., Ruiz-Perez, F.: A bio-inspired method for engineering design optimization inspired by dingoes hunting strategies. Math. Probl. Eng. 2021, 1–19 (2021). https://doi.org/10.1155/2021/9107547CrossRef

43.

Almazán-Covarrubias, J.H., Peraza-Vázquez, H., Peña-Delgado, A.F., García-Vite, P.M.: An improved Dingo optimization algorithm applied to SHE-PWM modulation strategy. Appl. Sci. 12(3), 992 (2022). https://doi.org/10.3390/app12030992CrossRef

44.

Storn, R., Price, K.: Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 11, 341–359 (1997). https://doi.org/10.1023/A:1008202821328MathSciNetCrossRef

45.

Eberhart, R., Kennedy, J.: A new optimizer using particle swarm theory, In: MHS’95. Proceedings of the Sixth International Symposium on machine and Human Science, pp.39–43. IEEE, Ngoya (1995). https://doi.org/10.1109/MHS.1995.494215

46.

Karaboga, D., Basturk, B.: Artificial bee colony (ABC) optimization algorithm for solving constrained optimization problems. In: International fuzzy systems association world congress. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-72950-1_77

47.

Gao, X.Z., Govindasamy, V., Xu, H., Wang, X., Zenger, K.: Harmony search method: theory and applications. Comput. Intell. Neurosci. 1, 258491 (2015). https://doi.org/10.1155/2015/258491CrossRef

48.

Civicioglu, P.: Backtracking search optimization algorithm for numerical optimization problems. Appl. Math. Comput. 219(15), 8121–8144 (2013). https://doi.org/10.1016/j.amc.2013.02.017MathSciNetCrossRef

49.

Jia, H., Rao, H., Wen, C., Mirjalili, S.: Crayfish optimization algorithm. Artif. Intell. Rev. 56, 1919–1979 (2023). https://doi.org/10.1007/s10462-023-10567-4CrossRef

50.

Amiri, M.H., MehrabiHashjin, N., Montazeri, M., Mirjalili, S., Khodadadi, N.: Hippopotamus optimization algorithm: a novel nature-inspired optimization algorithm. Sci. Rep. 14(1), 5032 (2024). https://doi.org/10.1038/s41598-024-54910-3CrossRef

51.

Ghasemi, M., Zare, M., Trojovský, P., Rao, R.V., Trojovská, E., Kandasamy, V.: Optimization based on the smart behavior of plants with its engineering applications: Ivy algorithm. Knowl.-Based Syst. 295, 1–36 (2024). https://doi.org/10.1016/j.knosys.2024.111850CrossRef

52.

Bai, J., Li, Y., Zheng, M., Khatir, S., Benaissa, B., Abualigah, L., Wahab, M.A.: A sinh cosh optimizer. Knowl.-Based Syst. 282(111081), 1–29 (2023). https://doi.org/10.1016/j.knosys.2023.111081CrossRef

53.

Abdel-Basset, M., Mohamed, R., Jameel, M., Abouhawwash, M.: Spider wasp optimizer: a novel meta-heuristic optimization algorithm. Artif. Intell. Rev. 56(10), 11675–11738 (2023). https://doi.org/10.1007/s10462-023-10446-yCrossRef

54.

Ayyarao, T.S.L.V.: Parameter estimation of solar PV models with quantum-based avian navigation optimizer and Newton-Raphson method. J. Comput. Electron. 21, 1338–1356 (2022). https://doi.org/10.1007/s10825-022-01931-8CrossRef

55.

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 (2022). https://doi.org/10.1007/s10825-022-01987-6CrossRef

56.

Dkhichi, F.: Parameter extraction of photovoltaic module model by using Levenberg-Marquardt algorithm based on simulated annealing method. J. Comput. Electron. 22, 1128–1139 (2023). https://doi.org/10.1007/s10825-023-02058-0CrossRef

57.

Chen, X., Wang, S., He, K.: Parameter estimation of various PV cells and modules using an improved simultaneous heat transfer search algorithm. J. Comput. Electron. 23, 584–599 (2024). https://doi.org/10.1007/s10825-024-02153-wCrossRef

Title: Estimating equivalent circuit parameters in various photovoltaic models and modules using the dingo optimization algorithm
Authors: Hasan Temurtaş
Gürcan Yavuz
Serdar Özyön
Aybüke Ünlü
Publication date: 06-08-2024
Publisher: Springer US
Published in: Journal of Computational Electronics / Issue 5/2024
Print ISSN: 1569-8025
Electronic ISSN: 1572-8137
DOI: https://doi.org/10.1007/s10825-024-02205-1

Study of chalcogenide-based metal perovskites BrZrX3 (X = S and Se): DFT insight into fundamental properties for sustainable energy generation using AMPS-1D

Springer Professional

Estimating equivalent circuit parameters in various photovoltaic models and modules using the dingo optimization algorithm

Abstract

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Other articles of this Issue 5/2024

Nonlinear dynamics of a Josephson junction coupled to a diode and a negative conductance

Feasibility of a 9 THz HgTe/HgCdTe quantum-well vertical-cavity surface-emitting laser

Precise parameter estimation of PEM fuel cell via weighted mean of vectors optimizer

Enhancing the analysis of external quantum efficiency in OLEDs utilizing thin transport layer materials

Study of chalcogenide-based metal perovskites BrZrX3 (X = S and Se): DFT insight into fundamental properties for sustainable energy generation using AMPS-1D

Compact model for MFIS-NCFETs considering deep-level interface trap states

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Nonlinear dynamics of a Josephson junction coupled to a diode and a negative conductance

Feasibility of a 9 THz HgTe/HgCdTe quantum-well vertical-cavity surface-emitting laser

Precise parameter estimation of PEM fuel cell via weighted mean of vectors optimizer

Enhancing the analysis of external quantum efficiency in OLEDs utilizing thin transport layer materials

Study of chalcogenide-based metal perovskites BrZrX3 (X = S and Se): DFT insight into fundamental properties for sustainable energy generation using AMPS-1D

Compact model for MFIS-NCFETs considering deep-level interface trap states