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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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  4/2012 - 8

Implicit Approximation of Photovoltaic Panel Characteristics Using a Stochastic Approach

ZAPLATILEK, K. See more information about ZAPLATILEK, K. on SCOPUS See more information about ZAPLATILEK, K. on IEEExplore See more information about ZAPLATILEK, K. on Web of Science, LEUCHTER, J. See more information about LEUCHTER, J. on SCOPUS See more information about LEUCHTER, J. on SCOPUS See more information about LEUCHTER, J. on Web of Science
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Download PDF pdficon (2,177 KB) | Citation | Downloads: 419 | Views: 2,593

Author keywords
photovoltaic panel, implicit approximation, Matlab

References keywords
photovoltaic(12), modeling(11), solar(6), matlab(5), systems(4), panel(4), leuchter(4), electronics(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2012-11-30
Volume 12, Issue 4, Year 2012, On page(s): 53 - 56
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2012.04008
Web of Science Accession Number: 000312128400008
SCOPUS ID: 84872792146

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In this article, an original system is described for an implicit approximation of photovoltaic panel characteristics. Photovoltaic panels (PV panel) are considered stochastic systems. Long-term measured basic characteristics are input data of the system. Each measurement is one of the stochastic PV realizations. Basic 2-D PV characteristics are approximated using implicit circle equations. Calculated circle passes through the three chosen points of a so-called stochastic cloud and it is an quasi-average PV model. The described approximation system includes all of practice influences over the PV, e.g. solar irradiation, temperature, PV wear, random events, etc. An original 3-D implicit final approximation is also introduced. The mentioned method is unambiguous and it also enables the user to intervene. The method is strictly based on measured data and it was developed and verified in MATLAB environment.

References | Cited By  «-- Click to see who has cited this paper

[1] M. Boxwell, Solar Electricity Handbook. Greenstream Publishing, Third Edition, 2010.

[2] A. Luque, S. Hegedus, Handbook of Photovoltaic Science and Engineering. John Wiley&Sons, 2011.

[3] J. Leuchter, P. Bauer, S.J. Finney, "Modeling and experimental verification of EGS to achieve higher efficiency," in Proc. 35th Annual Conference of IEEE Industrial Electronics (IECON 2009), Porto (Portugal), 2009, pp. 3983 - 3986.
[CrossRef] [SCOPUS Times Cited 3]

[4] S. Lalouni, D. Rekioua, "Modeling and Simulation of a Photovoltaic System Using Fuzzy Logic Controller," in Proc. 2nd International Conference on Developments in Systems Engineering, Abu Dhabi, Arab Emirates, 2009, pp. 23-28.

[5] J. Leuchter, V. Rerucha, A. F. Zobaa, "Mathematical modeling of photovoltaic systems," in Proc. 14th Power Electronics and Motion Control Conference (EPE-PEMC 2010), Ohrid (Macedonia), 2010, pp. 422 - 427.
[CrossRef] [SCOPUS Times Cited 11]

[6] I. S. Hermenean, I. Visa, A. Duta, "Modeling Temperature Variation in a CPV System," in Proc. 1st International Conference of Analytical and Nano-analytical Methods for Biomedical and Environmental Sciences, Brasov, Romania, 2010, pp. 263-269.

[7] A. Shahsavar, M. Ameri, "Experimental investigation and Modeling of a Direct-coupled PV/T Air Collector," Solar Energy, 2010, vol. 84, No. 11, pp. 1938-1958.
[CrossRef] [Web of Science Times Cited 98] [SCOPUS Times Cited 115]

[8] F. Adamo, F. Attivissimo, A. Di Nisio, "Characterization and Testing of a Tool for Photovoltaic Panel Modeling," IEEE Trans. on Instrumentation and Measurement, 2011, vol. 60, No. 5, pp 1613-1622.
[CrossRef] [Web of Science Times Cited 83] [SCOPUS Times Cited 98]

[9] X, Weidong, W. G. Dubford, A. Capel, "A novel modeling method for photovoltaic cells," in Proc. 35th Power Electronics Specialists Conference (PESC 2004), Aachen (Germany), 2004, pp. 1950 - 1956.
[CrossRef] [Web of Science Times Cited 430] [SCOPUS Times Cited 350]

[10] G. Muller, J. Friedrich, "Optimization and Modeling of Photovoltaic Silicon Crystallization Processes," in Proc. 14th International Summer School on Crystal Growth, Dalian, People's Republic of China, 2010, vol. 1270, pp. 255-281.

[11] S. Z. Wei, C. C. Chang, R. H. Lin, "Modeling of the J-V Characteristics for ITO/CuPc/C-60/Al Hetero-structure Solar Cells," Journal of the Chinese Chemical Society, 2010, vol. 57, No. 5B, Special Issue Sp. Iss. SI, pp. 1185-1190.

[12] P. Bannert, "Mathematic Model of Photovoltaic Cell, "final report of project "Solar Photovoltaic Systems", 2011. [Online] Available: Temporary on-line reference link removed - see the PDF document

[13] R. W. Fischer, Mastering Essential Math Skills. Math Essentials, Second Edition, 2006.

[14] R. M. da Silva, J. L. M. Fernandes, "Hybrid Photovoltaic/Thermal (PV/T) Solar Systems Simulation with Simulink&MATLAB," Solar Energy, 2010, Vol. 84, Issue 12, pp. 1985-1996.
[CrossRef] [Web of Science Times Cited 46] [SCOPUS Times Cited 60]

[15] S. Rustemli, F. Dincer, "Modeling of Photovoltaic Panel and Examining Effects of Temperature in MATLAB&Simulink," Electronics and Electrical Engineering, Kaunas, Lithuania, 2011, Issue 3, pp. 35-40.

[16] K. Zaplatilek, J. Leuchter, "Photovoltaic Panel Modeling in MATLAB Environment," Radioengineering, 2011, vol. 20, no. 2, pp. 445-450.

[17] K. Zaplatilek, J. Leuchter, "Behavioral Model of Photovoltaic Panel in Simulink," Advances in Electrical and Computer Engineering, 2011, vol. 11, no. 4, pp. 83-88.
[CrossRef] [Full Text] [Web of Science Times Cited 4] [SCOPUS Times Cited 8]

[18] J. H. Mathews, K. K. Fink, Numerical Methods Using MATLAB. Prentice-Hall, Inc., 2004.

[19] D. Hanselman, B. Littlefield, Mastering MATLAB 7. Prentice-Hall, Inc., 2005.

References Weight

Web of Science® Citations for all references: 661 TCR
SCOPUS® Citations for all references: 645 TCR

Web of Science® Average Citations per reference: 33 ACR
SCOPUS® Average Citations per reference: 32 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2020-09-23 14:00 in 71 seconds.

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