Parameter Extraction of PV Solar Cell: A Comparative Assessment Using Newton Raphson, Simulated Annealing and Particle Swarm Optimization

  • Nikita Rawat Department of Electrical Engineering, Graphic Era (Deemed to be University), Dehradun, Uttarakhand, India
  • Padmanabh Thakur Department of Electrical Engineering, Graphic Era (Deemed to be University), Dehradun, Uttarakhand, India
Keywords: PV Cell, Simulated Annealing, Newton Raphson, Particle Swarm Optimization, Single-Diode Model

Abstract

Proper modelling of PV cell is important to calculate its unknown parameters close to the accurate values, to attain the I-V characteristic curve close to the hardware model. This can help for simulation, computing efficiency, maximum power point tracing design, optimization and regulation of PV system. This paper estimates single diode PV model parameters such as photocurrent, the saturation current, the series resistance, the shunt resistance and the ideality factor. The estimation is done by three different optimization methods for single-diode model in an attempt to judge which method is surpassing in terms of convergence time and relative error. The first method Newton-Raphson is a numerical method based on gradient descent approach, while the second and third methods are evolutionary methods, simulated annealing and particle swarm optimization respectively. It was observed that particle swarm optimization algorithmis best among the methods and simulated annealing showed the worse performance.

Downloads

Download data is not yet available.

References

Almonacid, F. J. M. F., Rus, C., Hontoria, L., & Muñoz, F. J. (2010). Characterisation of PV CIS module by artificial neural networks. A comparative study with other methods. Renewable Energy, 35(5), 973-980.

Almonacid, F., Rus, C., Hontoria, L., Fuentes, M., & Nofuentes, G. (2009). Characterisation of Si-crystalline PV modules by artificial neural networks. Renewable Energy, 34(4), 941-949.

Appelbaum, J., & Peled, A. (2014). Parameters extraction of solar cells–A comparative examination of three methods. Solar Energy Materials and Solar Cells, 122, 164-173.

Askarzadeh, A., & dos Santos Coelho, L. (2015). Determination of photovoltaic modules parameters at different operating conditions using a novel bird mating optimizer approach. Energy Conversion and Management, 89, 608-614.

Awadallah, M. A., & Venkatesh, B. (2015, March). Estimation of PV module parameters from datasheet information using optimization techniques. In Industrial Technology (ICIT), 2015 IEEE International Conference on(pp. 2777-2782). IEEE.

Chan, D. S. H., Phillips, J. R., & Phang, J. C. H. (1986). A comparative study of extraction methods for solar cell model parameters. Solid-State Electronics, 29(3), 329-337.

De Soto, W., Klein, S. A., & Beckman, W. A. (2006). Improvement and validation of a model for photovoltaic array performance. Solar Energy, 80(1), 78-88.

Dizqah, A. M., Maheri, A., & Busawon, K. (2014). An accurate method for the PV model identification based on a genetic algorithm and the interior-point method. Renewable Energy, 72, 212-222.

Eberhart, R., & Kennedy, J. (1995, October). A new optimizer using particle swarm theory. In Micro Machine and Human Science, 1995. MHS'95., Proceedings of the Sixth International Symposium on(pp. 39-43). IEEE.

El-Naggar, K. M., AlRashidi, M. R., AlHajri, M. F., & Al-Othman, A. K. (2012). Simulated annealing algorithm for photovoltaic parameters identification. Solar Energy, 86(1), 266-274.

Enebish, N., Agchbayar, D., Dorjkhand, S., Baatar, D., & Ylemj, I. (1993). Numerical analysis of solar cell current-voltage characteristics. Solar Energy Materials and Solar Cells, 29(3), 201-208.

Ghani, F., & Duke, M. (2011). Numerical determination of parasitic resistances of a solar cell using the Lambert W-function. Solar Energy, 85(9), 2386-2394.

Ghani, F., Duke, M., & Carson, J. (2013). Numerical calculation of series and shunt resistances and diode quality factor of a photovoltaic cell usingthe Lambert W-function. Solar Energy, 91, 422-431.

Hunt, T. (2015). The solar singularity is nigh. Greentech Media. Retrieved, 29.

Ishaque, K., & Salam, Z. (2011). An improved modeling method to determine the model parameters of photovoltaic (PV) modules using differential evolution (DE). Solar Energy, 85(9), 2349-2359.

Ishaque, K., Salam, Z., Taheri, H., & Shamsudin, A. (2011). A critical evaluation of EA computational methods for Photovoltaic cell parameter extraction based on two diode model. Solar Energy, 85(9), 1768-1779.

Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by simulated annealing. Science, 220(4598), 671-680.

Lun, S. X., Du, C. J., Yang, G. H., Wang, S., Guo, T. T., Sang, J. S., & Li, J. P. (2013). An explicit approximate I–V characteristic model of a solar cell based on padé approximants. Solar Energy, 92, 147-159.

Ma, J., Ting, T. O., Man, K. L., Zhang, N., Guan, S. U., & Wong, P. W. (2013). Parameter estimation of photovoltaic models via cuckoo search. Journal of Applied Mathematics, 2013.

Mahmoud, Y. A., Xiao, W., & Zeineldin, H. H. (2013). A parameterization approach for enhancing PV model accuracy. IEEE Transactions on Industrial Electronics, 60(12), 5708-5716.

Nayak, B. K., Mohapatra, A., & Mohanty, K. B. (2013, December). Parameters estimation of photovoltaic module using nonlinear least square algorithm: A comparative study. In India Conference (INDICON), 2013 Annual IEEE(pp. 1-6). IEEE.

Ortiz-Conde, A., Sánchez, F. J. G., & Muci, J. (2006). New method to extract the model parameters of solar cells from the explicit analytic solutions of their illuminated I–V characteristics. Solar Energy Materials and Solar Cells, 90(3), 352-361.

Quaschning, V., & Hanitsch, R. (1996). Numerical simulation of current-voltage characteristics of photovoltaic systems with shaded solar cells. Solar Energy, 56(6), 513-520.

Siddiqui, M. U., & Abido, M. (2013). Parameter estimation for five-and seven-parameter photovoltaic electrical models using evolutionary algorithms. Applied Soft Computing, 13(12), 4608-4621.

Silva, E. A., Bradaschia, F., Cavalcanti, M. C., & Nascimento, A. J. (2016). Parameter estimation method to improve the accuracy of photovoltaic electrical model. IEEE Journal of Photovoltaics, 6(1), 278-285.

Soon, J. J., & Low, K. S. (2012). Photovoltaic model identification using particle swarm optimization with inverse barrier constraint. IEEE Transactions on Power Electronics, 27(9), 3975-3983.

Tivanov, M., Patryn, A., Drozdov, N., Fedotov, A., & Mazanik, A. (2005). Determination of solar cell parameters from its current–voltage and spectral characteristics. Solar Energy Materials and Solar Cells, 87(1-4), 457-465.

Villalva, M. G., Gazoli, J. R., & Ruppert Filho, E. (2009). Comprehensive approach to modeling and simulation of photovoltaic arrays. IEEE Transactions on Power Electronics, 24(5), 1198-1208.

Wei, H., Cong, J., Lingyun, X., & Deyun, S. (2011, April). Extracting solar cell model parameters based on chaos particle swarm algorithm. In Electric Information and Control Engineering (ICEICE), 2011 International Conference on(pp. 398-402). IEEE.

Xiao, W., Dunford, W. G., & Capel, A. (2004, June). A novel modeling method for photovoltaic cells. In Power Electronics Specialists Conference, 2004. PESC 04. 2004 IEEE 35th Annual(Vol. 3, pp. 1950-1956). IEEE.

Ye, M., Wang, X., & Xu, Y. (2009). Parameter extraction of solar cells using particle swarm optimization. Journal of Applied Physics, 105(9), 094502.

Published
2019-09-20
Section
Articles