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Implementation of Solar Tracker Controller Using Artificial Neural Network

Implementation of Solar Tracker Controller Using Artificial Neural Network. ECE 539 Course Project. By Ray Tang. Motivation. Alternative Energy - Solar Power Sunlight Gives Energy and the Quality of Light That No Other Source Can Replace

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Implementation of Solar Tracker Controller Using Artificial Neural Network

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  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 Sensor Reading Correction Serial Port • Analogue to Digital for Sensor Inputs • Servo Driver for Positioning • Using Sensor Reading as Input • Decision Sends Back to the Solar Tracker STK500

  7. Result 24th November 2001 10 9 8 7 6 5 Voltage (V) 4 3 2 1 0 0 100 200 300 400 500 600 Neural Net Decision 1 Correction 0 0 100 200 300 400 500 600 Time (x30 sec.)

  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.

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