SCIENCE AND ENGINEERING FAIR
Research Plan and/or Abstract for 2017

Category Award:   0 (GRAND AWARD)

Research Plan:

Abstract:
BACKGROUND


The current generated by a single solar cell is highly sensitive to the angle, the intensity, and the color of the light incident upon it. In this project, I want to investigate whether this property can be utilized to design an “intelligent” solar panel that can “predict” the position of a light source incident upon it (and, possibly, also its distance and its color). A potential application of such an “intelligent” panel would be as a solar tracker, facilitating the determination of the position of the Sun and feeding positional data to the tilting control unit.


HYPOTHESIS


My hypothesis is that if an array of solar cells can be properly designed, then the electrical response of each cell in the array will be different for different locations of an incandescent source of light pointed towards it. These electrical response values can then be read by a computer using proper sensors. The latter data, along with the positional information of the light source can then be used to train a supervised Machine Learning algorithm. The generated model can then be used to predict the position of any incandescent light source pointed at the array.


EXPERIMENT:


1.Use Ruthenizer-535 synthetic dye to create dye-sensitized solar cells (DSSCs). I choose to use DSSCs because I can produce them at home. Moreover, DSSCs are environmentally friendly, cheaper to manufacture than their silicon-based counterparts, and work in low-light conditions.

2.Use 6-9 such DSSCs to create an array so that each cell responds differently to an incandescent light source pointed at the array.

3.Connect each cell to the ADC input of an Arduino.

4.Read the voltage-drop on a 560-ohm resistor connected to each cell using an Arduino program.

5.Record the position of the light source in a 2-dimensional / 3-dimensional unit.

6.Use MatLab programs to train a Machine Learning algorithm and generate a model for light-source-position prediction.

7.Test the correctness of the model by placing the light source at an arbitrary position around the panel, recording the voltage values from each cell, feeding the data to the model, and checking whether it predicts the position correctly.