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Parameters estimation of photovoltaic module using Grey Wolf optimization method

Wybrane pełne teksty z tego czasopisma
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Warianty tytułu
PL
Określanie parametrów modułu fotowoltaicznego z wykorzystaniem algorytmu Grey Wolf
Języki publikacji
EN
Abstrakty
EN
Parameter prediction of a photovoltaic module is fundamental to simulate the photovoltaic generator for correctly reproduce the photovoltaic curves under natural environment. In this work, a new method for solar parameter estimation applying grey wolf algorithm is presented. This technic mimics the mechanism of the chase and the chain of command of wolves in wildlife. In the grey wolf optimizer algorithm four kinds of wolves are used for simulating the control hierarchy. This technique is tested on three different photovoltaic modules. The results showed the effectiveness and validity of the method to find with great precision the parameters of the three photovoltaic modules at various values of temperatures and illuminations.
PL
Przedstawiono nową metodę określania parametrów ogniw fotowoltaicznych wykorzystującą algorytm Grey Wolf. Wykorzystano cter modele zaworów do symulacji. Algorytm przetestowano na różnych modułach. Modele potwierdziły swoją efektywność dla różnych temperatur i naświetleń.
Rocznik
Strony
118--122
Opis fizyczny
Bibliogr. 33 poz., rys., tab.
Twórcy
  • Laboratoire Géométrie, Analyse, Contrôle Et Applications, University of Saida, Algeria
  • Laboratoire Géométrie, Analyse, Contrôle Et Applications, University of Saida, Algeria
autor
  • Laboratoire Géométrie, Analyse, Contrôle Et Applications, University of Saida, Algeria
Bibliografia
  • 1. M. G. Villalva, J. R. Gazoli, “Comprehensive approach to modeling and simulation of photovoltaic”, IEEE Transactions on Power Electronics Vol. 24 No.5 2009, pp. 1198–1208
  • 2. S. Chih-Tang, R. N. Noyce, “Carrier generation and recombination in P–N junctions and P–N junction characteristics”, Proceedings of the IRE Vol. 45, No. 9, 1957, pp. 1228–1243.
  • 3. D. Sera, R. Teodorescu and P. Rodriguez, “PV panel model based on datasheet values”, IEEE International Symposium on Industrial Electronics, Jun. 2007, pp. 2392-2396.
  • 4. T. T. Yetayew, T. R. Jyothsna, “Parameter Extraction of Photovoltaic Modules using Newton Raphson and Simulated Annealing Techniques”, 15 IEEE Power, Communication and Information Technology Conference (PCITC) Siksha ‘O’ Anusandhan University, Bhubaneswar, India.
  • 5. Partha P. Biswas, P.N. Suganthan, Guohua Wu, Gehan A.J. Amaratunga, “Parameter estimation of solar cells using datasheet information with the application of an adaptive differential evolution algorithm”, Renewable Energy, Volume 132, 2019, pp. 425-438.
  • 6. K. Sredenšek, S. Seme, “Parameter determination of a solar cell model using differential evolution algorithm” ,Przegląd Elektrotechniczny, 2019, pp. 165-168.
  • 7. D. S. H. Chan and J. C. H. Phang, “Analytical methods for the extraction of solar-cell single and double diode model parameters from I–V characteristics”, IEEE Trans. Electron Devices, vol. 34, no. 2, pp. 286–293, Feb. 1987.
  • 8. A.H.ALQahtani, M. S.Abuhamdeh, and Y. M.Alsmadi, “A Simplified and Comprehensive Approach to Characterize Photovoltaic System Performance”, Energytech, IEEE Conference Publications, 2012, pp. 1-6.
  • 9. M. Hejri, H. Mokhtari, M. R. Azizian, M. Ghandhari, and L. Soder, “On the Parameter Extraction of a Five-Parameter Double-Diode Model of Photovoltaic Cells and Modules”, IEEE J. of Photovoltaics,vol. 4, no. 3, 2014, pp. 915-923.
  • 10. Bonanno, F., Capizzi, G., Graditi, G., Napoli, C. and Tina, “A Radial Basis Function Neural Network Based Approach for the Electrical Characteristics Estimation of Aphotovoltaic Module ”, Applied Energy, 97, pp. 956-961.
  • 11. A. Ortiz-Conde, F.J. Garcia Sanchez, J. Muci, “New method to extract the model parameters of solar cells from the explicit analytic solutions of their illuminated I–V characteristics ”, Solar Energy Materials and Solar Cells, 90 (3),2006, 352–36.
  • 12. M. Tivanov, A. Patryn, N. Drozdov, A. Fedotov, A. Mazanik, “Determination of solar cell parameters from its current–voltage and spectral characteristics”, Solar Energy Materials and Solar Cells, 87 (1–4), 457-465.
  • 13. Kennedy, J. The particle swarm, “Social adaptation of knowledge”, Proc. Intl. Conf. on Evolutionary Computation, Indianapolis,1997. pp. 303-308.
  • 14. R. C. Eberhart, J. Kennedy, “A new optimizer using particle swarm theory”, Proceedings of the Sixth International 122 PRZEGLĄD ELEKTROTECHNICZNY, ISSN 0033-2097, R. 97 NR 2/2021 Symposium on Micro Machine and Human Science, Nagoya, Japan, IEEE Press, Piscataway, NJ (1995),pp. 39–43.
  • 15. Askarzadeh, Alireza., Rezazadeh, Alireza, “Parameter identification for solar cell models using harmony search-based algorithms”, Energy 86 2012, PP. 3241–3249.
  • 16. 13M.F. AlHajri et al, “Optimal extraction of solar cell parameters using pattern search”, Re A. 11Askarzadeh et al”, Extraction of maximum power point in solar cells using bird mating optimizer-based parameters identification approach”, newable Energy 44 (2012), pp. 238-245.
  • 17. 14A. Askarzadeh et al, “Artificial bee swarm optimization algorithm for parameters identification of solar cell models”,Applied Energy 102(2013), PP. 943–949.
  • 18. J A. Jervase, et al, “Solar cell parameter extraction using genetic algorithms”, Measurement.
  • 19. W. Huang, C. Jiang, L. Xue, and D. Song, “Extracting solar cell model parameters based on chaos particle swarm algorithm” ,Proceedings of the International Conference on Electric Information and Control Engineering (ICEICE '11), pp. 398– 402, April 2011.
  • 20. Azab, Mohamed, “Identification of one-diode model parameters of PV devices from nameplate information using particle swarm and least square methods”, 2015 First Workshop on Smart Grid and Renewable Energy (SGRE), 2015.
  • 21. Dali, Ali, Abderrezzak Bouharchouche, and Said Diaf. “Parameter identification of photovoltaic cell/module using genetic algorithm (GA) and particle swarm optimization (PSO)”, 2015 3rd International Conference on Control Engineering & Information Technology (CEIT), 2015.
  • 22. S. J. Jun and L. Kay-Soon, “Photovoltaic model identification using particle swarm optimization with inverse barrier constraint”, IEEE Transactions on Power Electronics, vol. 27, 2012, pp. 3975–3983,.
  • 23. M. Ye, X. Wang, and Y. Xu, “Parameter extraction of solar cells using particle swarm optimization,Journal of Applied Physics”, may. 2009 vol. 105 no. 9, pp. 094502 –094502–8.
  • 24. Satyajit Mohanty, Bidyadhar Subudhi, “A New MPPT Design Using Grey Wolf Optimization”, IEEE Transactions on Energy conversion,vol. 26, no. 3, september 2015
  • 25. C. Carrero, J. Amador, S. Arnaltes, “A single procedure for helping PV designers to select silicon PV modules and evaluate the loss resistances”, Renewable Energy, vol. 32, 2007, pp. 2579-2589
  • 26. S. Mirjalili, S. M. Mirjalili, and A. Lewis, “Grey wolf optimizer,” Adv. Eng. Software, vol. 69, 2014, pp. 46–61
  • 27. C.Muro, R. Escobedo, L. Spector, “Wolf-pack (Canis lupus) hunting strategies emerge from simple rules in computational simulations”, Behavioural processes 2011, vol. 88, pp. 192-197.
  • 28. http://www.ssb-foundation.com/
  • 29. Panasonic(VBHNV233S)datasheet
  • 30. ftp://ftp.panasonic.com/solar/specsheet/n325330-specsheet.pdf
  • 31. kaneka(U-VB110)datasheet
  • 32. http://www.kaneka-solar.com/product/thin-film/pdf/UEA.pdf
  • 33. Solar-frontier(SF-150-S)datasheet: https://www.solarfrontier.eu/fileadmin/content/downloads/modul es/en/20141030/product-overview-s-series-english.pdf
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-58c1639e-c408-435e-96a2-511d7a472533
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