Optimization of Sun-Tracker Positioning Using Takagi-Sugeno Fuzzy-Logic Method

Article Preview

Abstract:

The continuously increasing use of photovoltaic cells requires various efforts to maximize the harnessing of solar energy. This paper presents the research results of fuzzy-logic method implementation to maximize the absorption of solar energy. It is based on the optimization of solar panels position according to the sun direction. The Takagi-Sugeno method is chosen in the fuzzification stage. The control algorithm is implemented on a microcontroller ATMega-128 using BASCOM-AVR program. DC motor is used to actuate the solar panels. The results show an increase of 0.48V in the output of solar cells sensor using the fuzzy logic computation-based tracking system. The resulted tracking system proves to consume less power because the tracking process is halted while moving the DC motor continuously.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

231-235

Citation:

Online since:

August 2015

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2015 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

[1] IEA (International Energy Agency), Reneweble Energy Technologies, OECD/IEA, (2012).

Google Scholar

[2] C.Y. Lee, P.C. Chou, C.M. Chiang, and C.F. Lin, Sun Tracking Systems: A Review, Sensors, Sept 2009, pp.3875-3890.

DOI: 10.3390/s90503875

Google Scholar

[3] S. Same, Vorsic, and Joze, Maximum Efficiency Trajectories of a Two-Axis Sun Tracking System Determined Considering System Consumption, IEEE International Conference on Solar Systems, 2011, vol. 1, pp.1054-1057.

DOI: 10.1109/tpel.2011.2105506

Google Scholar

[4] Z. Yan, and S. Jiaxing, Application of Fuzzy Logic Control Approach in a Microcontroller-Based Sun Tracking System, IEEE WASE International Conference on Information Engineering (ICIE), 2010, Vol. 2, pp.161-164.

DOI: 10.1109/icie.2010.134

Google Scholar

[5] S.N. Sivanandam, S. Sumathi, and S. N. Deepa, Introduction to Fuzzy Logic using MATLAB, Springer, New York, 2007, 1st ed.

DOI: 10.1007/978-3-540-35781-0

Google Scholar

[6] W. Banks and G. Hayward, Fuzzy Logic in Embedded Microcomputers and Control Systems, Byte Craft Limited, Waterloo, (2002).

Google Scholar

[7] M.N. Cirstea, A. Dinu, J.G. Khor, and M. McCormick, Neural and Fuzzy Logic Control of Drives and Power Systems, Newnes, Oxford, (2002).

DOI: 10.1016/b978-075065558-3/50006-4

Google Scholar