All Issue

2024 Vol.34, Issue 12

Research Paper

Modeling and Identification the Parameters of a Silicon-Based Solar Cell to Optimize Its Energy Recovery
Nacera Mazouz1†
,
Moadh Kichene2
1Laboratory of Power Systems and Solar Energy and Automatic, Department of Electronics, Faculty of Electrical Engineering, University of Sciences and Technologies of Oran Mohamed-Boudiaf, Oran 31000, Algeria
2Department of Electronics, Faculty of Technology, University Mohamed Boudiaf of M’sila, M’sila 28000, Algeria
†Corresponding author E-Mail : nacera.mazouz@univ-usto.dz (N. Mazouz, USTOMB)
27 December 2024. pp. 593-601
PDF XML Citation
Abstract

Genetic algorithms (GAs) are used to optimize solutions to problems, particularly those that are analytically impossible to solve. As their name suggests, they are inspired by the biological concepts of genetics and evolution. Our work aims to study and model a silicon-based photovoltaic generator (PVG). Among the various models available is that of the diode. Modeling was used to approximate the PVG output (voltage, current) as a function of two inputs: temperature and irradiation. The parameters of our model were identified using a real coding algorithm, with the cumulative square error was used for selection. To test the effectiveness of our model, we carried out simulation tests on the power-voltage (P-V) and current-voltage (I-V) characteristics of a wide range of irradiation and temperature variations. This study demonstrates the effectiveness and accuracy of the proposed approach (GAs) and validates the parameters obtained and used in the single-diode electrical model. The results indicate that the GA technique is a better conventional parameter extraction strategy in terms of convergence. It provides globally optimal solutions.

Keywords
genetic algorithms
modelling
optimize
photovoltaic generator
parameters
References
1

J. C. Muller, J3eA, 6, 3 (2007).

10.1051/j3ea:2007003
2

A. Müller, M. Ghosh, R. Sonnenschein and P. Woditsch, Mater. Sci. Eng., B, 134, 257 (2006).

10.1016/j.mseb.2006.06.054
3

A. K. Behura, A. Kumar, D. K. Rajak, C. I. Pruncu and L. Lamberti, Solar Energy, 216, 518 (2021).

10.1016/j.solener.2021.01.045
4

H. Mathieu, Physics of Semiconductors and Electronic Components, 5th ed., p.30, Dunod, Paris, France (2001).

5

M. M. Kichene and A. Helali, Master Thesis (in Algeria), p.156, Mohamed Boudiaf University, M'sila (2010).

6

World Energy Outlook, IEA. Retrieved November 7, 2006 from https://iea.blob.core.windows.net/assets/390482d0-149a-48c0-959b-d5104ea308ca/weo2006.pdf

7

M. H. El-Ahmar, A.-H. M. El-Sayed and A. M. Hemeida, in Proceedings of the 2016 Eighteenth International Middle East Power Systems Conference (MEPCON) (Cairo, December 2016), p. 741-746.

10.1109/MEPCON.2016.7836976
8

B. Lakehal, Ph. D. Thesis (in Algeria), p.120, Batna-2 University, Batna (2009).

9

A. Ahmahma and A. Silla, Master Thesis (in Algeria), p.136, Kasdi Merbah University, Ouargla (2019).

10

A. Mostfaoui, Magister Thesis (in Algeria), p.122, Echahid Hamma Lakhdar University, El-Oued (2018).

11

T. Blickle and L. Thiele, A Comparison of Selection Schemes Used in Genetic Algorithms, 2nd ed., TIK-Report 11, Zurich, Switzerland (1995), p. 11-13. https://tik-old.ee.ethz.ch/file/6c0e384dceb283cd4301339a895b72b8/TIK-Report11.pdf

12

A. Hasanovic and A. Feliachi, in Proceedings of the IEEE Power Engineering Society Summer Meeting (Chicago, July 2002), p. 1136-1141.

13

M. Setnes and H. Roubos, IEEE Trans. Fuzzy Syst., 8, 509 (2000).

10.1109/91.873575
14

S. N. Sivanandam and S. N. Deepa, Introduction to Genetic Algorithms, 1st ed., p. 119-122, Springer, Heidelberg, Germany (2008).

15

Z. Michalewicz, T. D. Logan and S. Swaminathan, in Proceedings of the 3rd Annual Conference on Evolutionary Programming (San Diego, CA, February 1994), p. 84-97.

16

Z. Michalewicz, C. Z. Janikow and J. B. Krawczyk, Comput. Math. Appl., 23, 83 (1992).

10.1016/0898-1221(92)90094-X
17

O. Gergaud, B. Multon and H. Ben Ahmed, in Proceedings of the 7th International Electrimacs Congress (Montréal, August 2002), p. 1-6.

18

R. Mendoza-Pérez, J. Sastre-Hernández, G. Contreras-Puente and O. Vigil-Galán, Sol. Energy Mater. Sol. Cells, 93, 79 (2009).

10.1016/j.solmat.2008.09.016
Information
  • Publisher :Materials Research Society of Korea
  • Publisher(Ko) :한국재료학회
  • Journal Title :Korean Journal of Materials Research
  • Journal Title(Ko) :한국재료학회지
  • Volume : 34
  • No :12
  • Pages :593-601
  • Received Date : 2024-08-07
  • Revised Date : 2024-10-03
  • Accepted Date : 2024-10-22
  • DOI :https://doi.org/10.3740/MRSK.2024.34.12.593
Journal Informaiton Korean Journal of Materials Research Korean Journal of Materials Research
Online Submission
  • scopus
  • NRF
  • KOFST
  • crossref crossmark
  • crossref cited-by
  • crosscheck
  • open access
  • ccl
Journal Informaiton Journal Informaiton - close