Artículos
Publicado 2019-12-31
Palabras clave
- optimización,
- modelo,
- generador fotovoltaico,
- sombreado parcial,
- condición no-homogénea
Cómo citar
Perez- Archila, L. M., Bastidas-Rodriguez, J. D., & Correa, R. (2019). Solución del modelo de un generador fotovoltaico utilizando los algoritmos de optimización Trust Region Dogleg y PSO. Revista UIS Ingenierías, 19(1), 37–48. https://doi.org/10.18273/revuin.v19n1-2020003
Derechos de autor 2020 Revista UIS Ingenierías
Esta obra está bajo una licencia internacional Creative Commons Atribución-SinDerivadas 4.0.
Resumen
El modelo matemático de un generador fotovoltaico en conexión Serie-Paralelo representado mediante el modelo de diodo simple, tiene asociado a él un sistema de ecuaciones no lineales. En este trabajo se propone la solución de estos sistemas empleando los métodos de optimización Trust Region Dogleg y Optimización por Enjambre de Partículas, para resolver el modelo de un generador fotovoltaico operando en condiciones homogéneas y no homogéneas, variando el número de submódulos y el patrón de sombreado que incide sobre el generador. Se realizó la simulación de los modelos para generadores compuestos por 3 y 15 submódulos en serie, bajo diferentes condiciones de sombreado. De los métodos implementados, Trust Region Dogleg mostró un mejor desempeño con tiempos de cómputo 2 y 14 veces menores que el método de referencia y Optimización por Enjambre de Partículas, respectivamente. Y un error medio cuadrático igual o un 50 % inferior a los otros métodos.
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Referencias
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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.
[25] J. Nocedal and S. Wright, Numerical Optimization, ser. Springer Series in Operations Research and Financial Engineering. Springer New York, 2006.
[26] Trina-Solar, “TSM-PD05 Datasheet,” Beijing, China, pp. 4–5, 2017.
[27] W. De Soto, S. A. Klein, and W. A. Beckman, “Improvement and validation of a model for photovoltaic array performance,” Solar Energy, vol. 80, no. 1, pp. 78–88, 2006.
[28] Huajing Microelectronics, “GF3045T Photovoltaic Bypass Diodes Datasheet,” Beijing, China, pp. 1–4, 2018.