Articles
SSolution of series-parallel photovoltaic generator model using optimization algorithms Trust Region Dogleg and PSO
Rodrigo Correa
Published 2019-12-31
Keywords
- optimization,
- model,
- photovoltaic generator,
- partial shadow,
- non-homogeneous condition
How to Cite
Perez- Archila, L. M., Bastidas-Rodriguez, J. D., & Correa, R. (2019). SSolution of series-parallel photovoltaic generator model using optimization algorithms Trust Region Dogleg and PSO. Revista UIS Ingenierías, 19(1), 37–48. https://doi.org/10.18273/revuin.v19n1-2020003
Copyright (c) 2020 Revista UIS Ingenierías
This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License.
Abstract
Mathematical Model of a Series-Parallel (SP) photovoltaic (PV) generator represents each module through an equivalent electrical circuit denominated single diode model, this model has associated a nonlinear equation system that describes the electrical behavior of SP generator. This paper presents a solution of this system using optimization methods widely using: Trust Region Dogleg and Particle swarm optimization (PSO) for solving the electrical model of PV generator operating under homogeneous or non-homogeneous conditions, changing the number of submodules and shading pattern. It has been made simulations about generators composed by 3 and 15 series submodules, operating under different partial shading conditions. Between the implemented methods, Trust Region Dogleg show a better performance than other methods, with 2 and 14 times less computation time than the reference method and PSO, respectively, and a RMSE equal or 50 % lower than PSO.
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References
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[2] S. R. Pendem and S. Mikkili, “Modelling and performance assessment of PV array topologies under partial shading conditions to mitigate the mismatching power losses,” Solar Energy, vol. 160, no. October 2017, pp. 303–321, 2018.
[3] A. Pandian, K. Bansal, D. J. Thiruvadigal, and S. Sakthivel, “Fire Hazards and Overheating Caused by Shading Faults on Photo Voltaic Solar Panel,” Fire Technology, vol. 52, no. 2, pp. 349–364, mar 2016.
[4] P. Manganiello, M. Balato, and M. Vitelli, “A Survey on Mismatching and Aging of PV Modules : The Closed Loop,” vol. 62, no. 11, pp. 7276–7286, 2015.
[5] X. Qing, H. Sun, X. Feng, and C. Y. Chung, “Submodule-Based Modeling and Simulation of a Series-Parallel Photovoltaic Array Under Mismatch Conditions,” IEEE Journal of Photovoltaics, vol. 7, no. 6, pp. 1731–1739, nov 2017.
[6] M. L. Orozco-gutierrez, J. M. Ramirez-scarpetta, G. Spagnuolo, and C. A. Ramos-paja, “A technique for mismatched PV array simulation,” Renewable Energy, vol. 55, pp. 417–427, 2013.
[7] S. Gallardo-saavedra and B. Karlsson, “Simulation , validation and analysis of shading e ff ects on a PV system,” Solar Energy, vol. 170, no. June, pp. 828–839, 2018.
[8] G. Petrone and C. A. Ramos-paja, “Modeling of photovoltaic fields in mismatched conditions for energy yield evaluations,” Electric Power Systems Research, vol. 81, no. 4, pp. 1003–1013, 2011.
[9] E. Karatepe, M. Boztepe, and M. Colak, “Development of a suitable model for characterizing photovoltaic arrays with shaded solar cells,” Solar Energy, vol. 81, no. 8, pp. 977– 992, 2007.
[10] A. M. Humada, M. Hojabri, S. Mekhilef, and H. M. Hamada, “Solar cell parameters extraction based on single and doublediode models : A review,” vol. 56, pp. 494–509, 2016.
[11] O. W. Yin and B. C. Babu, “Simple and easy approach for mathematical analysis of photovoltaic (PV) module under normal and partial shading conditions,” Optik, vol. 169, no. May, pp. 48–61, sep 2018
[12] J. Accarino, G. Petrone, C. A. Ramos-Paja, and G. Spagnuolo, “Symbolic algebra for the calculation of the series and parallel resistances in PV module model,” 4th International Conference on Clean Electrical Power: Renewable Energy Resources Impact, ICCEP 2013, pp. 62–66, 2013.
[13] G. Petrone, G. Spagnuolo, and M. Vitelli, “Analytical model of mismatched photovoltaic fields by means of Lambert Wfunction,” Solar Energy Materials and Solar Cells, vol. 91, no. 18, pp. 1652–1657, 2007.
[14] D. Gonzalez Montoya, J. D. Bastidas-Rodriguez, L. A. TrejosGrisales, C. A. Ramos-Paja, G. Petrone, and G. Spagnuolo, “A procedure for modeling photovoltaic arrays under any configuration and shading conditions,” Energies, vol. 11, no. 4, 2018.
[15] G. Petrone, C. A. Ramos-Paja, and G. Spagnuolo, Index. Chichester, UK: John Wiley & Sons, Ltd, jan 2017, pp. 185– 186.
[16] W. De Soto, S. Klein, and W. Beckman, “Improvement and validation of a model for photovoltaic array performance,” Solar Energy, vol. 80, no. 1, pp. 78–88, jan 2006.
[17] M. Gradella Villalva, J. Rafael Gazoli, and E. Ruppert Filho, “Comprehensive {A}pproach to {M}odeling and {S}imulation of {P}hotovoltaic {A}rrays,” IEEE Transactions on Power Electronics, vol. 24, no. 5, pp. 1198–1208, 2009.
[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 Conversion and Management, vol. 79, pp. 294–303, 2014.
[19] J. Vélez-Sánchez, J. D. Bastidas-Rodríguez, C. A. Ramos-Paja, D. González Montoya, and L. A. Trejos-Grisales, “A noninvasive procedure for estimating the exponential model parameters of bypass diodes in photovoltaic modules,” Energies, vol. 12, no. 2, pp. 1–20, 2019.
[20] C. Grosan and A. Abraham, “A New Approach for Solving Nonlinear Equations Systems,” IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, vol. 38, no. 3, pp. 698–714, may 2008.
[21] Y. Shi and R. Eberhart, “A modified particle swarm optimizer,” in 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360). IEEE, 2002, pp. 69–73.
[22] A. Khare and S. Rangnekar, “A review of particle swarm optimization and its applications in Solar Photovoltaic system,” Applied Soft Computing Journal, vol. 13, no. 5, pp. 2997–3006, 2013.
[23] Zhi-Hui Zhan, Jun Zhang, Yun Li, and H.-H. Chung, “Adaptive Particle Swarm Optimization,” IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 39, no. 6, pp. 1362–1381, dec 2009.
[24] V. K. Chauhan, K. Dahiya, and A. Sharma, “Trust Region Levenberg-Marquardt Method for Linear SVM,” 2017 9th International Conference on Advances in Pattern Recognition, ICAPR 2017, vol. 2, no. 2, pp. 380–385, 2018.
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