MEDIUM SIZE DUAL-AXIS SOLAR TRACKING SYSTEM WITH SUNLIGHT INTENSITY COMPARISON METHOD AND FUZZY LOGIC IMPLEMENTATION

Authors

  • Azwaan Zakariah
  • Mahdi Faramarzi
  • Jasrul Jamani Jamian Resource Sustainability Research Alliance, Faculty of Electrical Engineering, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Malaysia
  • Mohd Amri Md Yunus Protom-i Research Group, Innovative Engineering Research Alliance, Control and Mechatronic Engineering Department,

DOI:

https://doi.org/10.11113/jt.v77.6468

Keywords:

Dual-axis tracker, solar renewable energy, sunlight intensity comparison method, fuzzy logic

Abstract

Nowadays, renewable energy such as solar power has become important for electricity generation, and solar power systems have been installed in homes. Furthermore, solar tracking systems are being continuously improved by researchers around the world, who focus on achieving the best design and thus maximizing the efficiency of the solar power system. In this project, a fuzzy logic controller has been integrated and implemented in a medium-scale solar tracking system to achieve the best real-time orientation of a solar PV panel toward the sun. This project utilized dual-axis solar tracking with a fuzzy logic intelligent method. The hardware system consists of an Arduino UNO microcontroller as the main controller and Light Dependent Resistor (LDR) sensors for sensing the maximum incident intensity of solar irradiance. Initially, two power window motors (one for the horizontal axis and the other for the vertical axis) coordinate and alternately rotate to scan the position of the sun. Since the sun changes its position all the time, the LDR sensors detect its position at five-minute intervals through the level of incident solar irradiance intensity measured by them. The fuzzy logic controller helps the microcontroller to give the best inference concerning the direction to which the solar PV panel should rotate and the position in which it should stay. In conclusion, the solar tracking system delivers high efficiency of output power with a low power intake while it operates.

References

Stamatescu, I., G. Stamatescu, N. Arghira, I. Fagarasan, et al. 2014. Fuzzy Decision Support System for Solar Tracking Optimization. 2014 IEEE International Conference on Development and Application Systems (DAS). Suceava, Romania. IEEE. 16-20.

Ahmad, S., Shafie, S., Kadir, M. Z. A. A. 2012. A High Power Generation, Low Power Consumption Solar Tracker. 2012 IEEE International Conference on Power and Energy (PECon). Kota Kinabalu, Sabah. IEEE. 366-371.

Alexandru, C. 2009. The Design and Optimization of a Photovoltaic Tracking Mechanism. 2009 International Conference on Power Engineering, Energy and Electrical Drives. Lisbon, Portugal. IEEE. 436-441.

Usta, M. A., Akyazi, O., Altas, I. H. 2011. Design and Performance of Solar Tracking System with Fuzzy Logic Controller Used Different Membership Functions. 2011 7th International Conference on Electrical and Electronics Engineering (ELECO). Bursa, Turkey. IEEE. 381-385.

Zhang, X., Li, X. and Lu, K. 2012. Research on an Intelligent Solar Tracking System Based On LPC2131. 2012 3rd IEEE International Conference on Network Infrastructure and Digital Content (IC-NIDC). Beijing, China. IEEE. 429-432.

Ahmad Amsyar bin Azman. 2014. A Solar Tracking System With Multiple Parameter Input For Maximize Efficiency. B. Degree Thesis. Universiti Teknologi Malaysia.

Bader, S. and Oelmann, B. 2010. Enabling BatteryLess Wireless Sensor Operation Using Solar Energy Harvesting at Locations with Limited Solar Radiation. 2010 Fourth International Conference on Sensor Technologies and Applications. Venice, Italy. IEEE. 602-608.

Huang, Y. J., Wu, B. C., Chen, C. Y., Chang, C. H., and Kuo, T. C. 2009. Solar Tracking Fuzzy Control System Design Using FPGA. In Proceedings of the World Congress on Engineering (WCE ’09), vol. 1, London, UK, July. 1-5.

Sefa, I., Demirtas, M., & Çolak, İ. 2009. Application of one-Axis Sun Tracking System. Energy Conversion and Management. 50(11): 2709-2718.

Chin, C. S., Neelakantan, P., Yoong, H. P., & Teo, K. T. K. 2011. Optimisation of Fuzzy Based Maximum Power Point Tracking In PV System For Rapidly Changing Solar Irradiance. Transaction on Solar Energy and Planning. 2: 130-137.

Shrivastava, S. M. 2013. Dual Axis Solar Tracker. B. Degree Thesis. Gautam Budh Technical University;

Clifford, M. J. and Eastwood, D. 2004. Design of a Novel Passive Solar Tracker. Solar Energy. 77(33): 269-280.

Beltran, J. A., Gonzalez Rubio, J. L. S., and Garcia-Beltran, C. D. Design, Manufacturing and Performance Test of a Solar Tracker Made by an Embedded Control. 2007 IEEE Electronics, Robotics and Automotive Mechanics Conference. Morelos, Mexico. Sept. 2007:IEEE. 2007. 129-134.

Hon, S. P., Kolte, M. T. and A, R. S. 2013. FPGA Based Sun Tracking System Using Fuzzy Logic. International Journal of Scientific and Technology Research. 2(9): 217-220.

Patcharaprakiti, N., Premrudeepreechacharn, S., and Sriuthaisiriwong, Y. 2005. Maximum Power Point Tracking Using Adaptive Fuzzy Logic Control for Grid-Connected Photovoltaic System. Renewable Energy. 30(11): 1771-1788.

Downloads

Published

2015-11-24

How to Cite

MEDIUM SIZE DUAL-AXIS SOLAR TRACKING SYSTEM WITH SUNLIGHT INTENSITY COMPARISON METHOD AND FUZZY LOGIC IMPLEMENTATION. (2015). Jurnal Teknologi, 77(17). https://doi.org/10.11113/jt.v77.6468