Abstract
Solar cell/module modeling involves the formulation of the non-linear current versus voltage (I-V) curve. Determination of parameters plays an important role in solar cell/module modeling. This paper presents an application of the improved PSO search method and Particle Swarm Optimization technique for identifying the unknown parameters of solar cell and photovoltaic module models, namely, the series resistance, shunt resistance, generated photocurrent, saturation current, and ideality factor that govern the current-voltage relationship of a solar cell/module. For the confirmation of accuracy of the proposed method, a measurement data of 57 mm diameter commercial (R.T.C. France) silicon solar cell and a module consisting of 36 polycrystalline silicon cells (Photowatt-PWP 201) has been selected and the best optimal value of each parameter has been obtained using Improved PSO. Comparative study among different parameter estimation techniques is presented to demonstrate to verify the accurateness and the effectiveness of the proposed approach.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
AlRashidi, M.R., AlHajri, M.F., El-Naggar, K.M., Al-Othman, A.K.: A new estimation approach for determining the I-V characteristics of solar cells. Sol. Energy 85(7), 1543–1550 (2011)
Clerc, M.: The swarm and the queen: towards a deterministic and adaptive particle swarm optimization. In: Proceedings of the Congress of Evolutionary Computation, Washington, pp. 1951–1957 (1999)
Ye, M., Wang, X., Xu, Y.: Parameter extraction of solar cells using particle swarm optimization. J. Appl. Phys. 105(9), 094502–094508 (2009)
Bouzidi, K., Chegaar, M., Nehaoua, N.: New method to extract the parameters of solar cells from their illuminated I–V curve. In: 4th International Conference on Computer Integrated Manufacturing, Setif, Algeria, November 2007
Safari, S., Ardehali, M.M., Sirizi, M.J.: Particle swarm optimization based fuzzy logic controller for autonomous green power energy system with hydrogen storage. In: 2011 Global Conference on Renewable Energy and Energy Efficiency for Desert Regions “GCREEDER 2011”. Energy Conversion and Management, vol. 65, pp. 41–49, January 2013
Orioli, A., Di Gangi, A.: A procedure to calculate the five-parameter model of crystalline silicon photovoltaic modules on the basis of the tabular performance data. Appl. Energy 102, 1160–1177 (2013)
Lineykin, S.: Five-parameter model of photovoltaic cell based on STC data and dimensionless. In: Electrical Engineering and Electronics, pp. 1–5 (2012)
Lo Brano, V., Orioli, A., Ciulla, G., Di Gangi, A.: An improved five-parameter model for photovoltaic modules. Sol. Energy Mater. Sol. Cells 94(8), 1358–1370 (2010)
Askarzadeh, A., Rezazadeh, A.: Parameter identification for solar cell models using harmony search-based algorithms. Sol. Energy 86(11), 3241–3249 (2012)
Chegaar, M., Ouennough, Z., Guechi, F., Langueur, H.: Determination of solar cells parameters under illuminated conditions. J. Electron. Devices 2, 17–21 (2003)
Easwarakhanthan, T., Bottin, J., Bouhouch, I., Boutrit, C.: Nonlinear minimization algorithm for determining the solar cell parameters with microcomputers. Int. J. Sol. Energy 4, 1–12 (1986)
Askarzadeh, A., Rezazadeh, A.: Artificial bee swarm optimization algorithm for parameters identification of solar cell models. Appl. Energy 102, 943–949 (2013)
Ishaque, K., Salam, Z., Mekhilef, S., Shamsudin, A.: Parameter extraction of solar photovoltaic modules using penalty-based differential evolution. Appl. Energy 99, 297–308 (2012)
Gong, W., Cai, Z.: Parameter extraction of solar cell models using repaired adaptive differential evolution. Sol. Energy 94, 209–220 (2013)
Alam, D., Yousri, D., Eteiba, M.: Flower pollination algorithm based solar PV parameter estimation. Energy Convers. Manage. 101, 410–422 (2015)
Bai, J., Liu, S., Hao, Y., Zhang, Z., Jiang, M., Zhang, Y.: Development of a new compound method to extract the five parameters of PV modules. Energy Convers. Manage. 79, 294–303 (2014)
Ismail, M., Moghavvemi, M., Mahlia, T.: Characterization of PV panel and global optimization of its model parameters using genetic algorithm. Energy Convers. Manage. 73, 10–25 (2013)
Hamid, N.F.A., Rahim, N.A., Selvaraj, J.: Solar cell parameters identification using hybrid Nelder-Mead and modified particle swarm optimization. J. Renew. Sustain. Energy 8, 015502 (2016)
Sirjani, R., Shareef, H.: Parameter extraction of solar cell models using the lightning search algorithm in different weather conditions. J. Sol. Energy Eng. 138, 041007 (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Amokrane, Z., Haddadi, M., Ould Cherchali, N. (2018). An Improved Technique Based on PSO to Estimate the Parameters of the Solar Cell and Photovoltaic Module. In: Hatti, M. (eds) Artificial Intelligence in Renewable Energetic Systems. ICAIRES 2017. Lecture Notes in Networks and Systems, vol 35. Springer, Cham. https://doi.org/10.1007/978-3-319-73192-6_46
Download citation
DOI: https://doi.org/10.1007/978-3-319-73192-6_46
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-73191-9
Online ISBN: 978-3-319-73192-6
eBook Packages: EngineeringEngineering (R0)