Estimation and Analysis of the Characteristic Parameters of Photovoltaic Cells by Mayfly Algorithm

Abstract

In recent years, renewable energy sources such as solar energy have been increasing their importance in energy production day by day. Various studies have been carried out in the literature for the effective performance and control of solar cells that generate energy from the sun. Various solar cell models, such as single diode and double diode models, have been developed to improve performance and control. However, the main problem in these studies is the estimation of characteristic parameters accurately and efficiently. In the last decade, this problem has been tried to be solved by using metaheuristic algorithms in the literature. In this study, for the first time, the Mayfly algorithm (MA) is used for characteristic parameter estimation of photovoltaic models. In order to analyze the estimation performance of the proposed approach, frequently used solar cells and diode models are examined. The results were compared with literature studies. Current-voltage and Power-voltage graphs used to find the maximum point were created using the estimated parameters. The results obtained and the graphs drawn show that the proposed approach is correct and effective in parameter estimation of photovoltaic cells.

Keywords

• Mayfly Algorithm
• Photovoltaic
• Parameter Estimation
• Optimization

Fotovoltaik Hücrelerin Karakteristik Parametrelerinin Mayıs Sineği Algoritması ile Kestirimi ve İncelemesi

Öz

Son yıllarda Güneş enerjisi gibi yenilenebilir enerji kaynakları enerji üretimindeki önemini gün geçtikçe artırmaktadır. Literatürde güneşten enerji üreten güneş pillerinin etkin performansı ve kontrolü için çeşitli çalışmalar yapılmaktadır. Tek diyot ve çift diyot modelleri gibi çeşitli güneş pili modelleri performansı ve kontrolü artırmak için geliştirilmiştir. Ancak bu çalışmalarda asıl problem doğru ve verimli bir şekilde karakteristik parametrelerin kestirimidir. Son on yılda literatürde metasezgisel algoritmalar kullanılarak bu problem çözülmeye çalışılmıştır. Bu çalışmada, ilk kez, Mayıs sineği algoritması (MA) fotovoltaik modellerin karakteristik parametre kestirimi için kullanılmıştır. Önerilen yaklaşımın kestirim performansını analiz etmek amacıyla sık kullanılan güneş pilleri ve diyot modelleri incelenmiştir. Sonuçlar literatür çalışmaları ile karşılaştırılmıştır. Maksimum noktanın bulunmasında kullanılan Akım-gerilim ve Güç-gerilim grafikleri kestirilen parametreler kullanılarak oluşturulmuştur. Elde edilen sonuçlar ve çizilen grafikler, fotovoltaik hücrelerin parametre kestiriminde önerilen yaklaşımın doğru ve etkili olduğunu göstermektedir.

Anahtar Kelimeler

• Mayıs Sineği Algoritması
• Parametre Kestirimi
• Fotovoltaik
• Optimizasyon.

References

1. Abbassi, R., Abbassi, A., Heidari, A.A., Miajalili, S. (2019). An efficient salp swarm-inspired algorithm for parameters identification of photovoltaic cell models. Energy Convers Management,179, 362–372. https://doi.org/10.1016/j.enconman.2018.10.069.
2. Alam, D.F,, Yousri, D.A., Eteiba, M.B. (2015). Flower pollination algorithm based solar PV parameter estimation. Energy Convers Management, 101, 410–22. https://doi.org/10.1016/j.enconman.2015.05.074.
3. Ali, E.E., El-Hameed, M.A., El-Fergany, A.A., El-Arini, M.M. (2016). Parameter extraction of photovoltaic generating units using multi-verse optimizer. Sustain Energy Technologies Assessment, 17, 68–76. https://doi.org/10.1016/j.seta.2016.08.004.
4. Allam, D., Yousri, D.A., Eteiba, M.B. (2016). Parameters extraction of the three diode model for the multi-crystalline solar cell/ module using moth-flame optimization algorithm. Energy Convers Management, 123, 535–48. https://doi.org/10.1016/j.enconman.2016.06.052.
5. Askarzadeh, A., Coelho, L.S. (2015). Determination of photovoltaic modules parameters at different operating conditions using a novel bird mating optimizer approach. Energy Convers Management, 89, 608–14. https://doi.org/10.1016/j.enconman.2014.10.025.
6. Askarzadeh, A., Rezazadeh, A. (2012). Parameter identification for solar cell models using harmony search-based algorithms. Sol Energy, 86(11):3241–9. https://doi.org/10.1016/j.solener.2012.08.018.
7. Awadallah, M.A. (2016). Variations of the bacterial foraging algorithm for the extraction of PV module parameters from nameplate data. Energy Convers Management, 113:312–20. https://doi.org/10.1016/j.enconman.2016.01.071.
8. Ayala, H.V.H., dos Santos Coelho, L., Mariani, V.C., Askarzadeh, A. (2015). An improved free search differential evolution algorithm: a case study on parameters identification of one diode equivalent circuit of a solar cell module. Energy, 93:1515–22. https://doi.org/10.1016/j.energy.2015.08.019.

9. Balasubramanian, K., Jacob, B., Priya, K., Sangeetha, K., Rajasekar, N., Babu, TS. (2015). Critical evaluation of genetic algorithm based fuel cell parameter extraction. Energy Procedia, 75:1975–1982. https://doi.org/10.1016/j.egypro.2015.07.244.
10. Beigi, A.M., Maroosi A. (2018). Parameter identification for solar cells and module using a hybrid firefly and pattern search algorithm. Sol Energy, 171:435–46. https://doi.org/10.1016/j.solener.2018.06.092.
11. Brano, V.L., Ciulla, G. (2013). An efficient analytical approach for obtaining a five parameters model of photovoltaic modules using only reference data. Applied Energy, 111, 894–903. https://doi.org/10.1016/j.apenergy.2013.06.046.
12. Chan, D.S.H., Phang, J.C.H. (1987). Analytical methods for the extraction of solar-cell single and double-diode model parameters from i–v characteristics. IEEE Trans Electron Devices, 34(2), 286–93. https://doi.org/10.1109/T-ED.1987.22920.
13. Chen, H., Jiao, S., Heidari, A.A., Wang, M., Chen, X., Zhao, X. (2019). An opposition-based sine cosine approach with local search for parameter estimation of photovoltaic models. Energy Convers Management, 195, 927–42. https://doi.org/10.1016/j.enconman.2019.05.057.
14. Chen, X., Xu, B., Mei, C., Ding, Y., Li, K. (2018). Teaching-learning- based artificial bee colony for solar photovoltaic parameter estimation. Applied Energy, 212, 1578–88. https://doi.org/10.1016/j.apenergy.2017.12.115.
15. Chen, X., Yu, K., Du, W., Zhao, W., Liu, G. (2016). Parameters identification of solar cell models using generalized oppositional teaching learning based optimization. Energy, 99, 170–80. https://doi.org/10.1016/j.energy.2016.01.052.
16. Chen, X., Yu, K. (2019). Hybridizing cuckoo search algorithm with biogeography-based optimization for estimating photovoltaic model parameters. Solar Energy, 180, 192–206. https://doi.org/10.1016/j.solener.2019.01.025.
17. Chen, Z., Wu, L., Lin, P., Wu, Y., Cheng, S. (2016). Parameters identify-cation of photovoltaic models using hybrid adaptive Nelder-Mead simplex algorithm based on eagle strategy. Applied Energy, 182, 47–57. https://doi.org/10.1016/j.apenergy.2016.08.083.
18. Easwarakhanthan, T., Bottin, J., Bouhouch, I., Boutrit, C. (1986) Nonlinear minimization algorithm for determining the solar cell parameters with microcomputers. International Journal of Solar Energy, 4(1), 1–12. https://doi.org/10.1080/01425918608909835.
19. El-Naggar, K.M., Alrashidi, M.R., Alhajri, M.F., Al-Othman, A.K. (2012) Simulated annealing algorithm for photovoltaic parameter identification. Solar Energy, 86, 266–274. https://doi.org/10.1016/j.solener.2011.09.032.
20. Fathy, A., Rezk, H. (2017). Parameter estimation of photovoltaic system using imperialist competitive algorithm. Renewable Energy, 111, 307–20. https://doi.org/10.1016/j.renene.2017.04.014.
21. Gong, W., Zhihua, Cai. (2013). Parameter extraction of solar cell models using repaired adaptive differential evolution. Solar Energy, 94, 209-220. https://doi.org/10.1016/j.solener.2013.05.007.
22. Guo,, L., Meng, Z., Sun, Y., Wang, L. (2016). Parameter identification and sensitivity analysis of solar cell models with cat swarm optimization algorithm. Energy Conversion and Management, 108, 520–8. https://doi.org/10.1016/j.enconman.2015.11.041.
23. Hasanien, HM. (2015). Shuffled frog leaping algorithm for photo-voltaic model identification. IEEE Transactions on Sustainable Energy, 6, 509–15. https://doi.org/ 10.1109/TSTE.2015.2389858.
24. Ishaque, K., Salam, Z., Mekhilef, S., Shamsudin, A. (2012). Parameter extraction of solar photovoltaic modules using penalty-based differential evolution. Applied Energy, 99, 297–308. https://doi.org/10.1016/j.apenergy.2012.05.017.
25. Ismail, M., Moghavvemi, M., Mahlia, T. (2013). Characterization of PV panel and global optimization of its model parameters using genetic algorithm. Energy Conversion and Management, 73, 10–25. https://doi.org/10.1016/j.enconman.2013.03.033.
26. Jordehi, AR. (2018). Enhanced leader particle swarm optimization (ELPSO): An efficient algorithm for parameter estimation of photovoltaic (PV) cells and modules. Solar Energy, 159, 78–87. https://doi.org/10.1016/j.solener.2017.10.063.
27. Kler, D., Goswami, Y., Rana, K.P.S., Kumar, V. (2019). A novel approach to parameter estimation of photovoltaic systems using hybridized optimizer. Energy Conversion and Management, 187, 486–511. https://doi.org/10.1016/j.enconman.2019.01.102.
28. Kler, D., Sharma, P., Banerjee, A., Rana, K.P.S., Kumar V. (2017). PV cell and module efficient parameters estimation using evaporation rate based water cycle algorithm. Swarm and Evolutionary Computation, 35, 93–110. https://doi.org/10.1016/j.swevo.2017.02.005.
29. Li, S., Gong, W., Yan, X., Hu, C., Bai, D., Wang, L., et al. (2019). Parameter extraction of photovoltaic models using an improved teaching-learning-based optimization. Energy Conversion Management,186, 293–305. https://doi.org/10.1016/j.enconman.2019.02.048.
30. Li, S., Gong, W., Yan, X., Hu, C., Bai, D., Wang, L. (2019). Parameter estimation of photovoltaic models with memetic adaptive differential evolution. Solar Energy, 190, 465–74. https://doi.org/10.1016/j.solener.2019.08.022.
31. Lin, P., Cheng, S., Yeh, W., Chen, Z., Wu, L. (2017). Parameters extraction of solar cell models using a modified simplified swarm optimization algorithm. Solar Energy, 144, 594–603. https://doi.org/10.1016/j.solener.2017.01.064.
32. Long, W., Cai, S., Jiao, J., et al. (2020). A new hybrid algorithm based on grey wolf optimizer and cuckoo search for parameter extraction of solar photovoltaic models. Energy Conversion and Management, 203, 112243. https://doi.org/10.1016/j.enconman.2019.112243.
33. Nassar-Eddine, I., Obbadi, A., Errami, Y., Fajri, A.E., Agunaou, M. (2016). Parameter estimation of photovoltaic modules using iterative method and the lambert w function: a comparative study. Energy Conversion Management, 119, 37–48. https://doi.org/10.1016/j.enconman.2016.04.030.
34. Niu, Q., Zhang, L., Li, K. (2014). A biogeography-based optimization algorithm with mutation strategies for model parameter estimation of solar and fuel cells. Energy Conversion Management, 86, 1173–85. https://doi.org/10.1016/j.enconman.2014.06.026.
35. Nunes, H.G.G., Pombo, J.A.N., Mariano, S.J.P.S., Calado, M.R.A., Felippe de Souza, J.A.M. (2018). A new high performance method for determining the parameters of PV cells and modules based on guaranteed convergence particle swarm optimization. Applied Energy, 211, 774–91. https://doi.org/10.1016/j.apenergy.2017.11.078.
36. Oliva, D., Aziz, M.A.E., Hassanien, A.E. (2017). Parameter estimation of photovoltaic cells using an improved chaotic whale optimization algorithm. Applied Energy, 200, 141–54. https://doi.org/10.1016/j.apenergy.2017.05.029.
37. Oliva, D., Cuevas, E., Pajares, G. (2014). Parameter identification of solar cells using artificial bee colony optimization. Energy, 72(7), 93–102. https://doi.org/10.1016/j.energy.2014.05.011.
38. Oliva, D., Elaziz, M.A., Elsheikh, A.H., Ewees, A.A. (2019). A review on meta-heuristics methods for estimating parameters of solar cells. Journal of Power Sources, 435, 126683. https://doi.org/10.1016/j.jpowsour.2019.05.089.
39. Ortiz-Conde, A., Sïnchez, F.J.G., Muci, J. (2006). New method to extract the model parameters of solar cells from the explicit analytic solutions of their illuminated characteristics. Solar Energy Mater Sol Cells, 90(3), 352–61. https://doi.org/10.1016/j.solmat.2005.04.023.
40. Parida, B., Iniyan, S., Goic, R. (2011). A review of solar photovoltaic technologies. Renewable Sustainable Energy Reviews, 15(3), 1625–36. https://doi.org/10.1016/j.rser.2010.11.032.
41. Patel, S.J., Panchal, A.K., Kheraj, V. (2014). Extraction of solar cell parameters from a single current-voltage characteristic using teaching learning based optimization algorithm. Applied Energy, 119, 384–93. https://doi.org/10.1016/j.apenergy.2014.01.027.
42. Rajasekar, N., Kumar, N.K., Venugopalan, R. (2013). Bacterial foraging algorithm based solar PV parameter estimation. Solar Energy, 97, 255–65. https://doi.org/10.1016/j.solener.2013.08.019.
43. Rezk, H., Fathy, A. (2017). A novel optimal parameters identification of triple-junction solar cell based on a recently meta-heuristic water cycle algorithm. Solar Energy, 157, 778–91. https://doi.org/10.1016/j.solener.2017.08.084.
44. Saleem, H., Karmalkar, S. (2009). An analytical method to extract the physical parameters of a solar cell from four points on the illuminated j-v curve. IEEE Electron Device Letters, 30(4), 349–52. https://doi.org/10.1109/LED.2009.2013882.
45. Subudhi, B., Pradhan, R. (2018). Bacterial foraging optimization approach to parameter extraction of a photovoltaic module. IEEE Trans Sustain Energy, 9, 381–9. https://doi.org/10.1109/TSTE.2017.2736060.
46. Tong, N.T., Kamolpattana, K., Pora W. (2015). A deterministic method for searching the maximum power point of a pv panel. In: International conference on electrical engineering/electronics, computer, telecommunications and information technology, p. 1–6. https://doi.org/10.1109/ECTICon.2015.7206928.
47. Wu, Z., Yu, D., Kang, X. (2017). Parameter identification of photovoltaic cell model based on improved ant lion optimizer. Energy Conversion Management, 151, 107–15. https://doi.org/10.1016/j.enconman.2017.08.088.
48. Xiong, G., Zhang, J., Yuan, X., Shi, D., He, Y., Yao, G. (2018). Parameter extraction of solar photovoltaic models by means of a hybrid differential evolution with whale optimization algorithm. Solar Energy, 176, 742–61. https://doi.org/10.1016/j.solener.2018.10.050.
49. Xu, S., Wang, Y. (2017). Parameter estimation of photovoltaic modules using a hybrid flower pollination algorithm. Energy Conversion Management, 144, 53–68. https://doi.org/10.1016/j.enconman.2017.04.042.
50. Yousri, D., Allam, D., Eteiba, M.B., Suganthan, P.N. (2019). Static and dynamic photovoltaic models’ parameters identification using chaotic heterogeneous comprehensive learning particle swarm optimizer variants. Energy Conversion Management, 182, 546–63. https://doi.org/10.1016/j.enconman.2018.12.022.
51. Yu, K., Chen, X., Wang, X., Wang, Z. (2017). Parameters identification of photovoltaic models using self-adaptive teaching-learning-based optimization. Energy Conversion Management, 145, 233–46. https://doi.org/10.1016/j.enconman.2017.04.054.
52. Yu, K., Liang, J., Qu, B., Chen, X., Wang, H. (2017). Parameters identification of photovoltaic models using an improved JAYA optimization algorithm. Energy Conversion Management, 150, 742–53. https://doi.org/10.1016/j.enconman.2017.08.063.
53. Yu, K., Liang, J., Qu, B., Cheng, Z., Wang, H. (2018). Multiple learning backtracking search algorithm for estimating parameters of photovoltaic models. Applied Energy, 226, 408–22. https://doi.org/10.1016/j.apenergy.2018.06.010.
54. Yu, K., Qu, B., Yue, C., Ge, S., Chen, X., Liang, J. (2019). A performance guided JAYA algorithm for parameters identification of photovoltaic cell and module. Applied Energy, 237, 241–57. https://doi.org/10.1016/j.apenergy.2019.01.008.
55. Zervoudakis, K., Tsafarakis, S. (2020). A mayfly optimization algorithm. Computers & Industrial Engineering, 145, 106559. https://doi.org/10.10https://doi.org/10.1016/j.cie.2020.106559.