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JCR Impact Factor: 0.595
JCR 5-Year IF: 0.661
Issues per year: 4
Current issue: Nov 2017
Next issue: Feb 2018
Avg review time: 105 days


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

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


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ABC Algorithm based Fuzzy Modeling of Optical Glucose Detection, SARACOGLU, O. G., BAGIS, A., KONAR, M., TABARU, T. E.
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LATEST NEWS

2017-Jun-14
Thomson Reuters published the Journal Citations Report for 2016. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.595, and the JCR 5-Year Impact Factor is 0.661.

2017-Apr-04
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2017-Feb-16
With new technologies, such as mobile communications, internet of things, and wide applications of social media, organizations generate a huge volume of data, much faster than several years ago. Big data, characterized by high volume, diversity and velocity, increasingly drives decision making and is changing the landscape of business intelligence, from governments to private organizations, from communities to individuals. Big data analytics that discover insights from evidences has a high demand for computing efficiency, knowledge discovery, problem solving, and event prediction. We dedicate a special section of Issue 4/2017 to Big Data. Prospective authors are asked to make the submissions for this section no later than the 31st of May 2017, placing "BigData - " before the paper title in OpenConf.

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2016-Dec-17
IoT is a new emerging technology domain which will be used to connect all objects through the Internet for remote sensing and control. IoT uses a combination of WSN (Wireless Sensor Network), M2M (Machine to Machine), robotics, wireless networking, Internet technologies, and Smart Devices. We dedicate a special section of Issue 2/2017 to IoT. Prospective authors are asked to make the submissions for this section no later than the 31st of March 2017, placing "IoT - " before the paper title in OpenConf.

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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
 
Click to see author's profile on See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (2,177 KB) | Citation | Downloads: 364 | Views: 1,923

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

Abstract
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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 8]


[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 55] [SCOPUS Times Cited 64]


[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 63] [SCOPUS Times Cited 79]


[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 112] [SCOPUS Times Cited 250]


[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 19] [SCOPUS Times Cited 35]


[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.
[CrossRef]


[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 3] [SCOPUS Times Cited 6]


[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: 252 TCR
SCOPUS® Citations for all references: 445 TCR

Web of Science® Average Citations per reference: 13 ACR
SCOPUS® Average Citations per reference: 22 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 2017-12-12 07:50 in 64 seconds.




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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania


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