Simplified swarm optimisation for the solar cell models parameter estimation problem

Wei-Chang Yeh; Peijie Lin; Chia-Ling Huang

Simplified swarm optimisation for the solar cell models parameter estimation problem

View Fulltext

Author(s): Wei-Chang Yeh ¹ ; Peijie Lin ² ; Chia-Ling Huang ³
- Affiliations: 1: Department of Industrial Engineering and Engineering Management, National Tsing Hua University, P.O. Box 24-60, Hsinchu 300, Taiwan ;
  2: School of Physics and Information Engineering, Fuzhou University, 2 Xue Yuan Road, University Town, Fuzhou, Fujian, 350108, People's Republic of China ;
  3: Department of Logistics and Shipping Management, Kainan University, No. 1, Kainan Road, Taoyuan 33857, Taiwan
Source: Volume 11, Issue 8, 28 June 2017, p. 1166 – 1173
DOI: 10.1049/iet-rpg.2016.0473 , Print ISSN 1752-1416, Online ISSN 1752-1424

Received 03/06/2016, Accepted 23/11/2016, Revised 01/09/2016, Published 01/12/2016

Solar energy applications and research are becoming increasingly popular, and photovoltaics (PVs) are among the most significant solar energy applications. To simulate and optimise PV system performance, the optimal parameters of the solar cell models should be estimated exactly. In this study, improved simplified swarm optimisation (iSSO), a recently introduced soft computing method based on simplified swarm optimisation, is proposed to minimise the least square error between the extracted and the measured data for the solar cell models parameter estimation of the single- and double-diode model problems. Based on the new all-variable difference update mechanism and survival of the fittest policy, the proposed algorithm is able to find an improved approximation for estimating the parameters of single- and double-diode solar cell models. As evidence of the utility of the proposed iSSO, the authors present extensive computational results for two benchmark problems. The comparison of the computational results supports the proposed iSSO algorithm outperforms the previously developed algorithms for all of the experiments in the literature.

References

1. 1)
  - 34. Yeh, W.C.: ‘Optimization of the disassembly sequencing problem on the basis of self-adaptive simplified swarm optimization’, IEEE Trans. Syst. Man Cybern. A, Syst. Humans, 2012, 42, (1), pp. 250–261.
2. 2)
  - 17. Kim, W., Choi, W.: ‘A novel parameter extraction method for the one-diode solar cell model’, Sol. Energy, 2010, 84, (6), pp. 1008–1019.
3. 3)
  - 13. Saleem, H., Karmalkar, S.: ‘An analytical method to extract the physical parameters of a solar cell from four points on the illuminated curve’, IEEE Electron Device Lett., 2009, 30, pp. 349–352.
4. 4)
  - 21. Tossa, A.K., Soro, Y.M., Azoumah, Y., et al: ‘A new approach to estimate the performance and energy productivity of photovoltaic modules in real operating conditions’, Sol. Energy, 2014, 110, pp. 543–560.
5. 5)
  - 9. Askarzadeh, A., Rezazadeh, A.: ‘Parameter identification for solar cell models using harmony search-based algorithms’, Sol. Energy, 2012, 86, (11), pp. 3241–3249.
6. 6)
  - 3. Ishaque, K., Salam, Z.: ‘An improved modeling method to determine the model parameters of photovoltaic (PV) modules using differential evolution (DE)’, Sol. Energy, 2011, 85, (9), pp. 2349–2359.
7. 7)
  - 5. Mares, O., Paulescu, M., Badescu, V.: ‘A simple but accurate procedure for solving the five-parameter model’, Energy Convers. Manage., 2015, 105, pp. 139–148.
8. 8)
  - 39. Villalva, M.G., Gazoli, J.R.: ‘Comprehensive approach to modeling and simulation of photovoltaic arrays’, IEEE Trans. Power Electron., 2009, 24, (5), pp. 1198–1208.
9. 9)
  - 31. Yuan, X., Xiang, Y., He, Y.: ‘Parameter extraction of solar cell models using mutative-scale parallel chaos optimization algorithm’, Sol. Energy, 2014, 108, pp. 238–251.
10. 10)
  - 11. Chin, V.J., Salam, Z., Ishaque, K.: ‘Cell modelling and model parameters estimation techniques for photovoltaic simulator application: A review’, Appl. Energy, 2015, 154, pp. 500–519.
11. 11)
  - 7. Kassis, A., Saad, M.: ‘Analysis of multi-crystalline silicon solar cells at low illumination levels using a modified two-diode model’, Sol. Energy Mater. Sol. Cells, 2010, 94, (12), pp. 2108–2112.
12. 12)
  - 10. Renno, C., Petito, F., Gatto, A.: ‘Artificial neural network models for predicting the solar radiation as input of a concentrating photovoltaic system’, Energy Convers. Manage., 2015, 106, pp. 999–1012.
13. 13)
  - 25. Wei, H., Cong, J., Lingyun, X., et al: ‘Extracting solar cell model parameters based on chaos particle swarm algorithm’. Int. Conf. on Electric Information and Control Engineering (ICEICE), 2011, 2011, pp. 398–402.
14. 14)
  - 32. http://integrationandcollaboration.org/SSO.html.
15. 15)
  - 37. Azizipanah-Abarghooee, R., Niknam, T., Gharibzadeh, M., et al: ‘Robust, fast and optimal solution of practical economic dispatch by a new enhanced gradient-based simplified swarm optimisation algorithm’, IET Gener. Transm. Distrib., 2013, 7, (6), pp. 620–635.
16. 16)
  - 8. Nishioka, K., Sakitani, N., Uraoka, Y., et al: ‘Analysis of multicrystalline silicon solar cells by modified 3-diode equivalent circuit model taking leakage current through periphery into consideration’, Sol. Energy Mater. Sol. Cells, 2007, 91, (13), pp. 1222–1227.
17. 17)
  - 28. AlRashidi, M.R., AlHajri, M.F., El-Naggar, K.M., et al: ‘A new estimation approach for determining the I–V characteristics of solar cells’, Sol. Energy, 2011, 85, (7), pp. 1543–1550.
18. 18)
  - 22. Zagrouba, M., Sellami, A., Bouaïcha, M., et al: ‘Identification of PV solar cells and modules parameters using the genetic algorithms: application to maximum power extraction’, Sol. Energy, 2010, 84, (5), pp. 860–866.
19. 19)
  - 12. Chan, D.S.H., Phang, J.C.H.: ‘Analytical methods for the extraction of solar-cell single-and double-diode model parameters from IV characteristics’, IEEE Trans. Electron Devices, 2014, 86, pp. 3241–3249.
20. 20)
  - 1. International Energy Agency, Photovoltaic Power Systems Programme.: ‘2015 snapshot of global photovoltaic markets’. Report IEA PVPS T1-29, 2016.
21. 21)
  - 33. Yeh, W.C.: ‘Novel swarm optimization for mining classification rules on thyroid gland data’, Inf. Sci., 2012, 197, pp. 65–76.
22. 22)
  - 15. Jain, A., Kapoor, A.: ‘Exact analytical solutions of the parameters of real solar cells using Lambert W-function’, Sol. Energy Mater. Sol. Cells, 2004, 81, pp. 269–277.
23. 23)
  - 18. Easwarakhanthan, T., Bottin, J., Bouhouch, I., et al: ‘Nonlinear minimization algorithm for determining the solar cell parameters with microcomputers’, Int. J. Sol. Energy , 1986, 4, (1), pp. 1–12.
24. 24)
  - 6. Wolf, M., Noel, G.T., Stirn, R.J.: ‘Investigation of the double exponential in the current–voltage characteristics of silicon solar cells’, IEEE Trans. Electron Devices, 1977, 24, (4), pp. 419–428.
25. 25)
  - 20. Ma, T., Yang, H., Lu, L.: ‘Development of a model to simulate the performance characteristics of crystalline silicon photovoltaic modules/strings/arrays’, Sol. Energy, 2014, 100, pp. 31–41.
26. 26)
  - 26. Oliva, D., Cuevas, E., Pajares, G.: ‘Parameter identification of solar cells using artificial bee colony optimization’, Energy, 2014, 72, pp. 93–102.
27. 27)
  - 35. Yeh, W.C.: ‘Simplified swarm optimization in disassembly sequencing problems with learning effects’, Comput. Oper. Res., 2012, 39, (9), pp. 2168–2177.
28. 28)
  - 40. Yeh, W.C.: ‘An improved simplified swarm optimization’, Knowl.-Based Syst., 2015, 82, pp. 60–69.
29. 29)
  - 36. Yeh, W.C.: ‘New parameter-free simplified swarm optimization for artificial neural network training and its application in the prediction of time series’, IEEE Trans. Neural Netw. Learn. Syst., 2013, 24, (4), pp. 661–665.
30. 30)
  - 24. Ye, M., Wang, X., Xu, Y.: ‘Parameter extraction of solar cells using particle swarm optimization’, J. Appl. Phys., 2009, 105, (9), pp. 094502–094508.
31. 31)
  - 4. Alam, D.F., Yousri, D.A., Eteiba, M.B.: ‘Flower pollination algorithm based solar PV parameter estimation’, Energy Convers. Manage., 2015, 101, pp. 410–422.
32. 32)
  - 30. Askarzadeh, A., Rezazadeh, A.: ‘Artificial bee swarm optimization algorithm for parameters identification of solar cell models’, Appl. Energy, 2013, 102, pp. 943–949.
33. 33)
  - 14. Lun, S., Du, C., Yang, G., et al: ‘An explicit approximate I–V characteristic model of a solar cell based on pad approximants’, Sol. Energy, 2013, 92, pp. 147–159.
34. 34)
  - 27. El-Naggar, K.M., AlRashidi, M.R., AlHajri, M.F., et al: ‘Simulated annealing algorithm for photovoltaic parameters identification’, Sol. Energy, 2012, 86, (1), pp. 266–274.
35. 35)
  - 29. Ma, J., Ting, T.O., Man, K.L., et al: ‘Parameter estimation of photovoltaic models via cuckoo search’, J. Appl. Math., 2013, 85, pp. 1543–1550.
36. 36)
  - 23. Jervase, J.A., Bourdoucen, H., Al-Lawati, A.: ‘Solar cell parameter extraction using genetic algorithms’, Meas. Sci. Technol., 2001, 12, (11), p. 1922.
37. 37)
  - 2. Ishaque, K., Salam, Z., Mekhilef, S., et al: ‘Parameter extraction of solar photovoltaic modules using penalty-based differential evolution’, Appl. Energy, 2012, 99, pp. 297–308.
38. 38)
  - 16. Jain, A., Sharma, S., Kapoor, A.: ‘Solar cell array parameters using Lambert W-function’, Sol. Energy Mater. Sol. Cells, 2006, 90, pp. 25–31.
39. 39)
  - 38. Azizipanah-Abarghooee, R.: ‘A new hybrid bacterial foraging and simplified swarm optimization algorithm for practical optimal dynamic load dispatch’, Int. J. Electr. Power Energy Syst., 2013, 49, pp. 414–429.
40. 40)
  - 19. Elbaset, A.A., Ali, H., Abd-El Sattar, M.: ‘Novel seven-parameter model for photovoltaic modules’, Sol. Energy Mater. Sol. Cell, 2014, 130, pp. 442–455.

Login

Not registered yet?

Share

Tools

Login to add to favourites

Key

Simplified swarm optimisation for the solar cell models parameter estimation problem

References

Related content