Abstract
The main goal of this paper is the application of PSO (Particle Swarm Optimization) and Genetic Algorithm (GA) in Renewable energy in general and particularly photovoltaics (PV) in order to extract the five parameters that governs the PV module (the photocurrent, the serial resistance, the saturation current, the parallel resistance and the ideality factor). Indeed, PSO and GA are intelligent post-analytic global optimization algorithms that give a minimal error. The application of these algorithms aimed at comparing the experimental results of a fairly well known photovoltaic module with is the MSX 60 has given good results. This is confirmed by the calculation of statistical performance measurement factors such as RMSE (root-mean-square error) and MAPE (mean absolute percentage error).
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Zainal, N.A., et al.: Modelling of photovoltaic module using matlab simulink. IOP Conf. Series Mater. Sci. En. 114, 1–9 (2016)
Bonkoungou, D., et al.: Modelling and simulation of photovoltaic module considering single-diode equivalent circuit model in MATLAB. Int. J. Emerg. Technol. Adv. Eng., 493–502 (2008)
de Blas, M.A., Torres, J.L., Prieto, E., Garcı́a, A.: Selecting a suitable model for characterizing photovoltaic devices. Renew. Energy 25, 371–380 (2002)
Ye, M., et al.: Parameter extraction of solar cells using particle warm optimization. J. Appl. Phys. 105, 0945021–0945028 (2009)
Ismail, M.S., Moghavvemi, M., Mahlia, T.M.I.: Characterization of PV panel and global optimization of its model parameters using genetic algorithm. Energy Convers. Manag. 73, 10–25 (2013)
Chan, D.S.H., Phang, J.C.H.: Analytical methods for the extraction of solar-cell single- and double-diode model parameters from I–V characteristics. IEEE Trans. Electron Devices 34, 286–293 (1987)
Wolf, P., Benda, V.: Identification of PV solar cells and modules parameters by combining statistical and analytical methods. Sol. Energy 93, 151–157 (2013)
AlHajri, M.F., et al.: Optimal extraction of solar cell parameters using pattern search. Renewable Energy 44, 238–245 (2012)
Reis, L.R.D., Camacho, J.R., Novacki, D.F.: The Newton Raphson method in the extraction of parameters of PV modules. Renew. Energy Power Qual. J. (RE&PQJ) 1, 634–639 (2017)
Khezzar, R., Zereg, M., Khezzar, A.: Comparative study of mathematical methods for parameters calculation of current-voltage characteristic of photovoltaic module. In: IEEE International Conference on Electrical and Electronics Engineering ‘ELECO’, pp. 24–28, November 2009
Villalva, M., Gazoli, J.: Comprehensive approach to modeling and simulation of photovoltaic arrays. IEEE Trans. Power Electron. 24, 1198–1208 (2009)
Lodhi, E., et al.: Application of particle swarm optimization for extracting global maximum power point in PV system under partial shadow conditions. Int. J. Electron. Electrical Eng. 5, 223–229 (2017)
Amokrane, Z., Haddadi, M.: An improved technique based on PSO to estimate the parameters of the photovoltaics cell/module. In: The 5th International Conference on Electrical Engineering – Boumerdes (ICEE-B), Boumerdes, Algeria, pp. 1–9, 29–31 October 2017
Zagrouba, M., Sellami, A., Bouaicha, M., Ksouri, M.: Identification of PV solar cells and modules parameters using the genetic algorithms: application to maximum power extraction. Sol. Energy 84, 860–866 (2010)
AlRashidi, M.R., et al.: A new estimation approach for determining the I–V characteristics of solar cells. Sol. Energy 85, 1543–1550 (2011)
Rajasekar, N., et al.: Bacterial foraging algorithm based solar PV parameter estimation. Sol. Energy 97, 255–265 (2013)
Peng, W., et al.: Evolutionary algorithm and parameters extraction for dye-sensitized solar cells one-diode equivalent circuit model. Micro Nano Lett. 8, 86–89 (2013)
Carr, J.: An introduction to genetic algorithms. Senior Project 16, 1–40 (2014)
Gopalakrishnan, K.: Particle swarm optimization in civil infrastructure systems: state-of-the-art review. In: Metaheuristic Applications in Structures and Infrastructures, pp. 49–76 (2013)
Askarzadeh, A., Rezazadeh, A.: Parameter identification for solar cell models using harmony search-based algorithms. Sol. Energy 86, 3241–3249 (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Rezki, M., Bensaid, S., Griche, I., Houassine, H. (2020). Five PV Model Parameters Determination Through PSO and Genetic Algorithm, a Comparative Study. In: Hatti, M. (eds) Smart Energy Empowerment in Smart and Resilient Cities. ICAIRES 2019. Lecture Notes in Networks and Systems, vol 102. Springer, Cham. https://doi.org/10.1007/978-3-030-37207-1_2
Download citation
DOI: https://doi.org/10.1007/978-3-030-37207-1_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-37206-4
Online ISBN: 978-3-030-37207-1
eBook Packages: EngineeringEngineering (R0)