1. Implementation of Solar Tracker Controller Using Artificial Neural Network ECE 539 Course Project By Ray Tang

2. Motivation Alternative Energy - Solar Power Sunlight Gives Energy and the Quality of Light That No Other Source Can Replace Sunlight Varies its Direction From Day to Day and Moment to Moment Practice "On-line Training/learning" Compete with Sunflower

3. Project Details Design and Build a Solar Tracker Design and Implement the ANN Assess the Difficulty of the Ann Approach

4. Solar Tracker Two Axis for Tilt and Rotate  Two Maxon Type A Gear Motor  0.2E Resolution 15 cm x 15 cm Square Solar Panel  12VDC at 60mA Max Output Five Photo Sensors as Input  Colour Filters to Reduce Noise and Enhance Directional Sensitivity  Analogue Output

5. The ANN Multi-layer Perceptions  Using a 5 - 10 - 8 - 5 Configuration BP Training Algorithm  Off-line Training with 499 Artificial Data  On-line Training with Live Data  Learning Rate at 0.3, Momentum at 0.8  13900 Training with Tolerance at 0.1

6. The Set-up Serial Port Correction STK500 Sensor Reading Analogue to Digital for Sensor Inputs Servo Driver for Positioning Using Sensor Reading as Input Decision Sends Back to the Solar Tracker

7. Result 24th November 2001 0 1 2 3 4 5 6 7 8 9 10 0100200300400500600 Voltage (V) Neural Net Decision 0 1 0100200300400500600 Time (x30 sec.) Correction

8. Future Improvements Improve On-line Training Add Current Sensor to Solar Panel Investigate Other Approaches  Fuzzy Logic Control  Time Series Prediction

9. Conclusion "Unsuccessful" On-line Training Acceptable Performance Stable System

10. Reference Annual Report 24, Climate Monitoring & Diagnostics Laboratory CMDL, http://www.cmdl.noaa.gov/publications/annrpt24/322.htm Western Power Corporation, Kalbarri Photovoltaic System, http://www.westernpower.com.au/our_environment/renewable_energy/solar/ Gordon, M.. and Wenger, H., Central-Station Solar Photovoltaic Systems: Field Layout, Tracker, and Array Geometry Sensitivity Studies, Solar Energy, Vol. 46, No. 4, pp. 211-217, April ey., Panico, P., Garvison, P., Wenger, H., and D. Shugar, Backtracking: A Novel Strategy for Tracking PV Systems, IEEE Photovoltaic Specialists Conference, Las Vegas, NV, October 1991. Rogers, Jo (1997) Object - Oriented Neural Networks in C++, Academic Press, Inc., New York.