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Year 2023, Volume: 12 Issue: 4, 1041 - 1053, 28.12.2023

Seyit Alperen Celtek Seda Kul

https://doi.org/10.17798/bitlisfen.1317696

Abstract

References

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  • [2] H. Zhang, Z. Lu, W. Hu, Y. Wang, L. Dong, and J. Zhang, “Coordinated optimal operation of hydro–wind–solar integrated systems,” Appl Energy, vol. 242, 2019, doi: 10.1016/j.apenergy.2019.03.064.
  • [3] J. Liu et al., “Impact of Power Grid Strength and PLL Parameters on Stability of Grid-Connected DFIG Wind Farm,” IEEE Trans Sustain Energy, vol. 11, no. 1, pp. 545–557, Jan. 2020, doi: 10.1109/TSTE.2019.2897596.
  • [4] M. Abdel-Basset, R. Mohamed, M. Sharawi, L. Abdel-Fatah, M. Abouhawwash, and K. Sallam, “A comparative study of optimization algorithms for parameter estimation of PV solar cells and modules: Analysis and case studies,” Energy Reports, vol. 8, pp. 13047–13065, Nov. 2022, doi: 10.1016/j.egyr.2022.09.193.
  • [5] B. Aboagye, S. Gyamfi, E. A. Ofosu, and S. Djordjevic, “Investigation into the impacts of design, installation, operation and maintenance issues on performance and degradation of installed solar photovoltaic (PV) systems,” Energy for Sustainable Development, vol. 66, 2022, doi: 10.1016/j.esd.2021.12.003.
  • [6] S. M. Ebrahimi, E. Salahshour, M. Malekzadeh, and Francisco Gordillo, “Parameters identification of PV solar cells and modules using flexible particle swarm optimization algorithm,” Energy, vol. 179, pp. 358–372, Jul. 2019, doi: 10.1016/j.energy.2019.04.218.
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  • [8] S. Kumar Patro and R. P. Saini, “Mathematical modeling framework of a PV model using novel differential evolution algorithm,” Solar Energy, vol. 211, 2020, doi: 10.1016/j.solener.2020.09.065.
  • [9] H. Rezk, T. S. Babu, M. Al-Dhaifallah, and H. A. Ziedan, “A robust parameter estimation approach based on stochastic fractal search optimization algorithm applied to solar PV parameters,” Energy Reports, vol. 7, 2021, doi: 10.1016/j.egyr.2021.01.024.
  • [10] M. Naeijian, A. Rahimnejad, S. M. Ebrahimi, N. Pourmousa, and S. A. Gadsden, “Parameter estimation of PV solar cells and modules using Whippy Harris Hawks Optimization Algorithm,” Energy Reports, vol. 7, 2021, doi: 10.1016/j.egyr.2021.06.085.
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Parameter Extraction of PV Solar Cell Using Metaheuristic Methods

Year 2023, Volume: 12 Issue: 4, 1041 - 1053, 28.12.2023

Seyit Alperen Celtek Seda Kul

https://doi.org/10.17798/bitlisfen.1317696

Abstract

Due to the increasing crises in energy and environmental factors, the importance of renewable energy is increasing. However, it is gaining importance in developing photovoltaic energy systems. Therefore, great efforts are made to maximize success in accurately modeling PV parameters. Parameter estimation is a complex problem and requires advanced design tools such as optimization techniques because the current voltage (I–V) characteristics of PVs are nonlinear. This study investigates the best technique for the most accurate estimation of the parameters obtained in single-diode and double-diode cases. The Gray Wolf Optimization (GWO), Improved Gray Wolf Optimization (IGWO), Sine Cosine Algorithm (SCA), Whale Optimization Algorithm (WOA), and Multi-Verse Optimizer (MVO) are the algorithms used in this paper. Apart from the literature, this study considers that the PV parameter extraction problem is not just an offline optimization problem but also a real-time optimization issue. The performance of all methods has been compared with experimental data. The lowest error on minimum iteration and highest convergence accuracy have been achieved for offline optimization by using IGWO. The results clearly state that the IGWO is not usable in real-time applications even though IGWO is the best optimizer in offline optimization.

Keywords

Solar energy PV parameter Metaheuristic Single diode PV Improved Grey Wolf Optimizer.

References

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  • [2] H. Zhang, Z. Lu, W. Hu, Y. Wang, L. Dong, and J. Zhang, “Coordinated optimal operation of hydro–wind–solar integrated systems,” Appl Energy, vol. 242, 2019, doi: 10.1016/j.apenergy.2019.03.064.
  • [3] J. Liu et al., “Impact of Power Grid Strength and PLL Parameters on Stability of Grid-Connected DFIG Wind Farm,” IEEE Trans Sustain Energy, vol. 11, no. 1, pp. 545–557, Jan. 2020, doi: 10.1109/TSTE.2019.2897596.
  • [4] M. Abdel-Basset, R. Mohamed, M. Sharawi, L. Abdel-Fatah, M. Abouhawwash, and K. Sallam, “A comparative study of optimization algorithms for parameter estimation of PV solar cells and modules: Analysis and case studies,” Energy Reports, vol. 8, pp. 13047–13065, Nov. 2022, doi: 10.1016/j.egyr.2022.09.193.
  • [5] B. Aboagye, S. Gyamfi, E. A. Ofosu, and S. Djordjevic, “Investigation into the impacts of design, installation, operation and maintenance issues on performance and degradation of installed solar photovoltaic (PV) systems,” Energy for Sustainable Development, vol. 66, 2022, doi: 10.1016/j.esd.2021.12.003.
  • [6] S. M. Ebrahimi, E. Salahshour, M. Malekzadeh, and Francisco Gordillo, “Parameters identification of PV solar cells and modules using flexible particle swarm optimization algorithm,” Energy, vol. 179, pp. 358–372, Jul. 2019, doi: 10.1016/j.energy.2019.04.218.
  • [7] D. Kler, Y. Goswami, K. P. S. Rana, and V. Kumar, “A novel approach to parameter estimation of photovoltaic systems using hybridized optimizer,” Energy Convers Manag, vol. 187, 2019, doi: 10.1016/j.enconman.2019.01.102.
  • [8] S. Kumar Patro and R. P. Saini, “Mathematical modeling framework of a PV model using novel differential evolution algorithm,” Solar Energy, vol. 211, 2020, doi: 10.1016/j.solener.2020.09.065.
  • [9] H. Rezk, T. S. Babu, M. Al-Dhaifallah, and H. A. Ziedan, “A robust parameter estimation approach based on stochastic fractal search optimization algorithm applied to solar PV parameters,” Energy Reports, vol. 7, 2021, doi: 10.1016/j.egyr.2021.01.024.
  • [10] M. Naeijian, A. Rahimnejad, S. M. Ebrahimi, N. Pourmousa, and S. A. Gadsden, “Parameter estimation of PV solar cells and modules using Whippy Harris Hawks Optimization Algorithm,” Energy Reports, vol. 7, 2021, doi: 10.1016/j.egyr.2021.06.085.
  • [11] A. Askarzadeh and A. Rezazadeh, “Parameter identification for solar cell models using harmony search-based algorithms,” Solar Energy, vol. 86, no. 11, 2012, doi: 10.1016/j.solener.2012.08.018.
  • [12] K. M. El-Naggar, M. R. AlRashidi, M. F. AlHajri, and A. K. Al-Othman, “Simulated Annealing algorithm for photovoltaic parameters identification,” Solar Energy, vol. 86, no. 1, 2012, doi: 10.1016/j.solener.2011.09.032.
  • [13] A. Askarzadeh and A. Rezazadeh, “Extraction of maximum power point in solar cells using bird mating optimizer-based parameters identification approach,” Solar Energy, vol. 90, 2013, doi: 10.1016/j.solener.2013.01.010.
  • [14] M. S. Ismail, M. Moghavvemi, and T. M. I. Mahlia, “Characterization of PV panel and global optimization of its model parameters using genetic algorithm,” Energy Convers Manag, vol. 73, 2013, doi: 10.1016/j.enconman.2013.03.033.
  • [15] S. J. Patel, A. K. Panchal, and V. Kheraj, “Extraction of solar cell parameters from a single current-voltage characteristic using teaching learning based optimization algorithm,” Appl Energy, vol. 119, 2014, doi: 10.1016/j.apenergy.2014.01.027.
  • [16] X. Chen, B. Xu, C. Mei, Y. Ding, and K. Li, “Teaching–learning–based artificial bee colony for solar photovoltaic parameter estimation,” Appl Energy, vol. 212, 2018, doi: 10.1016/j.apenergy.2017.12.115.
  • [17] D. Oliva, E. Cuevas, and G. Pajares, “Parameter identification of solar cells using artificial bee colony optimization,” Energy, vol. 72, 2014, doi: 10.1016/j.energy.2014.05.011.
  • [18] A. El-Fergany, “Efficient tool to characterize photovoltaic generating systems using mine blast algorithm,” Electric Power Components and Systems, vol. 43, no. 8–10, 2015, doi: 10.1080/15325008.2015.1014579.
  • [19] D. Allam, D. A. Yousri, and M. B. Eteiba, “Parameters extraction of the three diode model for the multi-crystalline solar cell/module using Moth-Flame Optimization Algorithm,” Energy Convers Manag, vol. 123, 2016, doi: 10.1016/j.enconman.2016.06.052.
  • [20] O. S. Elazab, H. M. Hasanien, M. A. Elgendy, and A. M. Abdeen, “Parameters estimation of single‐ and multiple‐diode photovoltaic model using whale optimisation algorithm,” IET Renewable Power Generation, vol. 12, no. 15, pp. 1755–1761, Nov. 2018, doi: 10.1049/iet-rpg.2018.5317.
  • [21] D. F. Alam, D. A. Yousri, and M. B. Eteiba, “Flower Pollination Algorithm based solar PV parameter estimation,” Energy Convers Manag, vol. 101, 2015, doi: 10.1016/j.enconman.2015.05.074.
  • [22] L. Guo, Z. Meng, Y. Sun, and L. Wang, “Parameter identification and sensitivity analysis of solar cell models with cat swarm optimization algorithm,” Energy Convers Manag, vol. 108, 2016, doi: 10.1016/j.enconman.2015.11.041.
  • [23] D. Kler, P. Sharma, A. Banerjee, K. P. S. Rana, and V. Kumar, “PV cell and module efficient parameters estimation using Evaporation Rate based Water Cycle Algorithm,” Swarm Evol Comput, vol. 35, 2017, doi: 10.1016/j.swevo.2017.02.005.
  • [24] M. Derick, C. Rani, M. Rajesh, M. E. Farrag, Y. Wang, and K. Busawon, “An improved optimization technique for estimation of solar photovoltaic parameters,” Solar Energy, vol. 157, 2017, doi: 10.1016/j.solener.2017.08.006.
  • [25] K. Yu, B. Qu, C. Yue, S. Ge, X. Chen, and J. Liang, “A performance-guided JAYA algorithm for parameters identification of photovoltaic cell and module,” Appl Energy, vol. 237, 2019, doi: 10.1016/j.apenergy.2019.01.008.
  • [26] M. H. Qais, H. M. Hasanien, and S. Alghuwainem, “Identification of electrical parameters for three-diode photovoltaic model using analytical and sunflower optimization algorithm,” Appl Energy, vol. 250, 2019, doi: 10.1016/j.apenergy.2019.05.013.
  • [27] H. Chen, S. Jiao, M. Wang, A. A. Heidari, and X. Zhao, “Parameters identification of photovoltaic cells and modules using diversification-enriched Harris hawks optimization with chaotic drifts,” J Clean Prod, vol. 244, 2020, doi: 10.1016/j.jclepro.2019.118778.
  • [28] M. Abd Elaziz and D. Oliva, “Parameter estimation of solar cells diode models by an improved opposition-based whale optimization algorithm,” Energy Convers Manag, vol. 171, 2018, doi: 10.1016/j.enconman.2018.05.062.
  • [29] C. Kumar, T. D. Raj, M. Premkumar, and T. D. Raj, “A new stochastic slime mould optimization algorithm for the estimation of solar photovoltaic cell parameters,” Optik (Stuttg), vol. 223, 2020, doi: 10.1016/j.ijleo.2020.165277.
  • [30] N. Pourmousa, S. M. Ebrahimi, M. Malekzadeh, and F. Gordillo, “Using a novel optimization algorithm for parameter extraction of photovoltaic cells and modules,” Eur Phys J Plus, vol. 136, no. 4, 2021, doi: 10.1140/epjp/s13360-021-01462-4.
  • [31] N. F. Nicaire, P. N. Steve, N. E. Salome, and A. O. Grégroire, “Parameter Estimation of the Photovoltaic System Using Bald Eagle Search (BES) Algorithm,” International Journal of Photoenergy, vol. 2021. 2021. doi: 10.1155/2021/4343203.
  • [32] M. Abdel-Basset, D. El-Shahat, R. K. Chakrabortty, and M. Ryan, “Parameter estimation of photovoltaic models using an improved marine predators algorithm,” Energy Convers Manag, vol. 227, 2021, doi: 10.1016/j.enconman.2020.113491.
  • [33] R. Bisht and A. Sikander, “A novel way of parameter estimation of solar photovoltaic system,” COMPEL, vol. 41, no. 1, 2022, doi: 10.1108/COMPEL-05-2021-0166.
  • [34] T. S. L. V. Ayyarao and P. P. Kumar, “Parameter estimation of solar PV models with a new proposed war strategy optimization algorithm,” Int J Energy Res, vol. 46, no. 6, 2022, doi: 10.1002/er.7629.
  • [35] T. Düzenli̇, F. Kutlu Onay, and S. B. Aydemi̇r, “Improved honey badger algorithms for parameter extraction in photovoltaic models,” Optik (Stuttg), vol. 268, p. 169731, Oct. 2022, doi: 10.1016/j.ijleo.2022.169731.
  • [36] A. M. Eltamaly, “Musical chairs algorithm for parameters estimation of PV cells,” Solar Energy, vol. 241, pp. 601–620, Jul. 2022, doi: 10.1016/j.solener.2022.06.043.
  • [37] D. M. Djanssou, A. Dadjé, and N. Djongyang, “Estimation of Photovoltaic Cell Parameters using the Honey Badger Algorithm,” Int J Eng Adv Technol, vol. 11, no. 5, pp. 109–124, Jun. 2022, doi: 10.35940/ijeat. E3552.0611522.
  • [38] T. T. Nguyen, T. T. Nguyen, and T. N. Tran, “Parameter estimation of photovoltaic cell and module models relied on metaheuristic algorithms including artificial ecosystem optimization,” Neural Comput Appl, vol. 34, no. 15, 2022, doi: 10.1007/s00521-022-07142-3.
  • [39] C. Kumar and D. Magdalin Mary, “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 (Stuttg), vol. 264, p. 169379, Aug. 2022, doi: 10.1016/j.ijleo.2022.169379.
  • [40] S. Mirjalili, S. M. Mirjalili, and A. Lewis, “Grey wolf optimizer,” Advances in engineering software, vol. 69, pp. 46–61, 2014.
  • [41] M. H. Nadimi-Shahraki, S. Taghian, and S. Mirjalili, “An improved grey wolf optimizer for solving engineering problems,” Expert Syst Appl, vol. 166, p. 113917, 2021.
  • [42] S. Mirjalili, S. M. Mirjalili, and A. Hatamlou, “Multi-verse optimizer: a nature-inspired algorithm for global optimization,” Neural Comput Appl, vol. 27, no. 2, pp. 495–513, 2016.
  • [43] J. Khoury, B. A. Ovrut, N. Seiberg, P. J. Steinhardt, and N. Turok, “From big crunch to big bang,” Physical Review D, vol. 65, no. 8, p. 086007, 2002.
  • [44] M. Tegmark, Barrow, JD Davies, PC Harper, CL, Jr eds,” Science and Ultimate Reality Cambridge University Press Cambridge, 2004.
  • [45] S. Mirjalili and A. Lewis, “The whale optimization algorithm,” Advances in engineering software, vol. 95, pp. 51–67, 2016.
  • [46] W. A. Watkins and W. E. Schevill, “Aerial observation of feeding behavior in four baleen whales: Eubalaena glacialis, Balaenoptera borealis, Megaptera novaeangliae, and Balaenoptera physalus,” J Mammal, vol. 60, no. 1, pp. 155–163, 1979.
  • [47] J. A. Goldbogen, A. S. Friedlaender, J. Calambokidis, M. F. McKenna, M. Simon, and D. P. Nowacek, “Integrative approaches to the study of baleen whale diving behavior, feeding performance, and foraging ecology,” Bioscience, vol. 63, no. 2, pp. 90–100, 2013.
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There are 52 citations in total.

Details

Primary Language English
Subjects Civil Engineering (Other)
Journal Section Araştırma Makalesi
Authors

Seyit Alperen Celtek 0000-0002-7097-2521

Seda Kul 0000-0001-8278-4723

Early Pub Date December 25, 2023
Publication Date December 28, 2023
Submission Date June 20, 2023
Acceptance Date November 23, 2023
Published in Issue Year 2023 Volume: 12 Issue: 4

Cite

IEEE S. A. Celtek and S. Kul, “Parameter Extraction of PV Solar Cell Using Metaheuristic Methods”, Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, vol. 12, no. 4, pp. 1041–1053, 2023, doi: 10.17798/bitlisfen.1317696.
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Bitlis Eren University
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Bitlis Eren University Graduate Institute
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