Parameters Estimation of the Single Diode Model of a Photovoltaic Module Based on the Improved Patterns Search Method

Andres Felipe Tobon Mejia | Bio
Instituto Tecnológico Metropolitano
Jhon Jairo Rojas Montano | Bio
Instituto Tecnológico Metropolitano
Sergio Ignacio Serna Garces | Bio
Universidad Nacional de Colombia
Jorge Aurelio Herrera Cuartas | Bio
Universidad de Bogotá Jorge Tadeo Lozano


In this article, we propose the use of the optimization algorithm based on improved pattern search (IPSM), applying it to the estimation of the model parameters of a single diode of a photovoltaic cell. The parameters to be estimated are the photovoltaic current, the saturation current of the diode, the series resistance, the resistance in parallel, and the ideality factor of the diode. The estimation is made from the data obtained from a known curve, that is to say, that a photovoltaic cell could be characterized and from the data of the curve the parameters are extracted. The results are the identification of the parameters and the accuracy of the model concerning the reference at the point of maximum power (MPP). Additionally, a comparison is made with the model obtained with three estimations made with the particle swarm optimization algorithm (PSO), under the same conditions in the number of particles and iterations. The error found shows the similarity of the model with the reference obtained using the IPSM algorithm. 


  1. Congreso de la república, “Ley n.º 1743”, 26 de diciembre de 2014. Disponible:
  2. N. Di, “Market Report Series energy efficiency 2017", 2017.
  3. E. E. Henao-Bravo and D. A. Márquez-Viloria, “Modelo matemático de sistemas fotovoltaicos para búsqueda distribuida del punto de máxima potencia Mathematical model of photovoltaic systems for distributed maximum power point tracking,” Tecno Lógicas, vol. 19, 37, pp. 107-124, 2016.
  4. D. J. Coyle et al., “LDi, “Market Report Series energy efficiency 2017”, 2017.ife prediction for CIGS solar modules part 2,” Prog. Photovoltaics, pp. 156-172, 2013,
  5. E. Skoplaki and J. A. Palyvos, “On the temperature dependence of photovoltaic module electrical performance: A review of efficiency/power correlations,” Sol. Energy, vol. 83, 5, pp. 614-624, 2009,
  6. E. Henao, D. Márquez, J. Villegas, S. Serna, C. Ramos, and D. González, “Modelo matemático de sistemas fotovoltaicos para búsqueda distribuida del punto de máxima potencia”, Tecno Lógicas, vol. 19, n.º 37, pp. 108-124, 2016.
  7. J. D. Bastidas-Rodriguez, E. Franco, G. Petrone, C. A. Ramos-Paja, and G. Spagnuolo, “Model-Based Degradation Analysis of Photovoltaic Modules Through Series Resistance Estimation,” IEEE Trans. Ind. Electron., vol. 62, n.º 11, pp. 7256-7265, 2015,
  8. P. Bhatnagar and R. K. Nema, “Maximum power point tracking control techniques: Stateof-the-art in photovoltaic applications,” Renew. Sustain. Energy Rev., vol. 23, pp. 224-241, 2013,
  9. D. González Montoya, “Control and optimization strategies to maximize the energy generated by photovoltaic sources” (Doctoral dissertation, Universidad Nacional de Colombia-Sede Manizales), 2017,
  10. G. Petrone, C. Ramos-Paja y G. Spagnuolo, “Photovoltaic Sources Modeling”. PV Models, 2017,
  11. X. H. Nguyen and M. P. Nguyen, “Mathematical modeling of photovoltaic cell/module/arrays with tags in Matlab/Simulink,” Environ. Syst. Res., vol. 4, n.º 1, 2015,
  12. Pranahita, B. S., Kumar, A. S. y Babu, A. P. A. (2014). A Study on Modelling and Simulation of Photovoltaic Cells. Int J Res Eng Technol, 3, 101-8.
  13. H. Tian, F. Mancilla-David, K. Ellis, E. Muljadi, and P. Jenkins, “A cell-to-module-to-array detailed model for photovoltaic panels,” Sol. Energy, vol. 86, n.º 9, pp. 2695-2706, 2012,
  14. P. Suskis and I. Galkin, “Enhanced photovoltaic panel model for MATLAB-simulink environment considering solar cell junction capacitance,” Iecon Proc. (Industrial Electron. Conf., pp. 1613-1618, 2013,
  15. T. Ahmad, S. Sobhan, and M. F. Nayan, “Comparative Analysis between Single Diode and Double Diode Model of PV Cell: Concentrate Different Parameters Effect on Its Efficiency,” J. Power Energy Eng., vol. 04, n.º 03, pp. 31-46, 2016,
  16. A. R. Jordehi, “Parameter estimation of solar photovoltaic (PV) cells: A review,” Renew. Sustain. Energy Rev., vol. 61, pp. 354-371, 2016,
  17. O. Mares, M. Paulescu, and V. Badescu, “A simple but accurate procedure for solving the five-parameter model,” Energy Convers. Manag., vol. 105, pp. 139-148, 2015,
  18. J. Bai, S. Liu, Y. Hao, Z. Zhang, M. Jiang, and Y. Zhang, “Development of a new compound method to extract the five parameters of PV modules,” Energy Convers. Manag., vol. 79, pp. 294-303, 2014,
  19. L. Peng, Y. Sun, and Z. Meng, “An improved model and parameters extraction for photovoltaic cells using only three state points at standard test condition,” J. Power Sources, vol. 248, pp. 621-631, 2014,
  20. J. J. Soon, K. S. Low, and S. T. Goh, “Multi-dimension diode photovoltaic (PV) model for different PV cell technologies,” IEEE Int. Symp. Ind. Electron., pp. 2496-2501, 2014,
  21. A. Orioli and A. Di Gangi, “A procedure to calculate the five-parameter model of crystalline silicon photovoltaic modules on the basis of the tabular performance data,” Appl. Energy, vol. 102, pp. 1160-1177, 2013,
  22. A. Chouder, S. Silvestre, N. Sadaoui, and L. Rahmani, “Modeling and simulation of a grid connected PV system based on the evaluation of main PV module parameters,” Simul. Model. Pract. Theory, vol. 20, n.º 1, pp. 46-58, 2012,
  23. K. Ding, X. Bian, H. Liu, and T. Peng, “A MATLAB-simulink-based PV module model and its application under conditions of nonuniform irradiance,” IEEE Trans. Energy Convers., vol. 27, n.º 4, pp. 864-872, 2012,
  24. V. Lo Brano, A. Orioli, G. Ciulla, and A. Di Gangi, “An improved five-parameter model for photovoltaic modules,” Sol. Energy Mater. Sol. Cells, vol. 94, n.º 8, pp. 1358-1370, 2010,
  25. D. Oliva, E. Cuevas, and G. Pajares, “Parameter identification of solar cells using artificial bee colony optimization,” Energy, vol. 72, pp. 93-102, 2014,
  26. A. Askarzadeh and A. Rezazadeh, “Artificial bee swarm optimization algorithm for parameters identification of solar cell models,” Appl. Energy, vol. 102, pp. 943-949, 2013,
  27. D. H. Muhsen, A. B. Ghazali, T. Khatib, and I. A. Abed, “Extraction of photovoltaic module model’s parameters using an improved hybrid differential evolution/electromagnetism-like algorithm,” Sol. Energy, vol. 119, pp. 286-297, 2015,
  28. L. L. Jiang, D. L. Maskell, and J. C. Patra, “Parameter estimation of solar cells and modules using an improved adaptive differential evolution algorithm,” Appl. Energy, vol. 112, pp. 185-193, 2013,
  29. W. Gong and Z. Cai, “Parameter extraction of solar cell models using repaired adaptive differential evolution,” Sol. Energy, vol. 94, pp. 209-220, 2013,
  30. V. Khanna, B. K. Das, D. Bisht, Vandana, and P. K. Singh, “A three diode model for industrial solar cells and estimation of solar cell parameters using PSO algorithm,” Renew. Energy, vol. 78, pp. 105-113, 2015,
  31. M. F. AlHajri, K. M. El-Naggar, M. R. AlRashidi, and A. K. Al-Othman, “Optimal extraction of solar cell parameters using pattern search,” Renew. Energy, vol. 44, pp. 238-245, 2012,
  32. K. M. El-Naggar, M. R. AlRashidi, M. F. AlHajri, and A. K. Al-Othman, “Simulated Annealing algorithm for photovoltaic parameters identification,” Sol. Energy, vol. 86, n.º 1, pp. 266-274, 2012,
  33. Q. Niu, L. Zhang, and K. Li, “A biogeography-based optimization algorithm with mutation strategies for model parameter estimation of solar and fuel cells,” Energy Convers. Manag., vol. 86, pp. 1173-1185, 2014,
  34. A. Askarzadeh and A. Rezazadeh, “Parameter identification for solar cell models using harmony search-based algorithms,” Sol. Energy, vol. 86, n.º 11, pp. 3241-3249, 2012,
  35. V. J. Chin, Z. Salam, and K. Ishaque, “Cell modelling and model parameters estimation techniques for photovoltaic simulator application: A review,” Appl. Energy, vol. 154, n.º 1, pp. 500-519, 2015,
  36. J. D. Bastidas-Rodriguez, E. Franco, G. Petrone, C. A. Ramos-Paja, and G. Spagnuolo, “Quantification of photovoltaic module degradation using model based indicators,” Math. Comput. Simul., vol. 131, pp. 101-113, 2017,
  37. R. M. Corless, G. H. Gonnet, D. E. G. Hare, and D. E. Knuth, “On the Lambert W Function,” D.E.G. al. Adv Comput Math, vol. 5, n.º 5, pp. 329-330, 1996,
  38. J. Accarino, G. Petrone, C. a. Ramos-Paja, and G. Spagnuolo, “Symbolic algebra for the calculation of the series and parallel resistances in
  39. V module model,” 4th Int. Conf. Clean Electr. Power Renew. Energy Resour. Impact, ICCEP, pp. 62-66, 2013,
  40. M. Clerc, “Standard Particle Swarm Optimisation: From 2006 to 2011,” Preprint work document, 2012, hal-00764996.
  41. S. Lineykin, M. Averbukh, and A. Kuperman, “Five-parameter model of photovoltaic cell based on STC data and dimensionless,” IEEE 27th Conv. Electr. Electron. Eng. Isr., pp. 1-5, 2012,
  42. H. J. A. P. J. Tobon Andres Felipe, “Estimación de los parámetros de un modelo matemático de una celda fotovoltaica utilizando un algoritmo de optimización de búsqueda de patrones,” Ingenio Magno, vol. 5, pp. 95-101, 2015,
How to Cite
Tobon Mejia, A. F., Rojas Montano, J. J., Serna Garces, S. I., & Herrera Cuartas, J. A. (2021). Parameters Estimation of the Single Diode Model of a Photovoltaic Module Based on the Improved Patterns Search Method. Revista Ingenierías Universidad De Medellín, 20(38), 13-32.


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