Computer vision and photosensor based hybrid control strategy for a two-axis solar tracker-Daylighting application

This paper deals with a computer vision and photosensor based two-axis solar tracking system for an active daylighting system. The real-time image processing was performed by using a Raspberry Pi 4 controller, and processed data were used as an input of an ATmega128 microcontroller to track the sun's path. An active daylighting system requires a higher concentration of sunlight with an acceptable accuracy in different weather conditions. Alone, an image based or photosensor based control strategy cannot fulfill both requirements. Successful integration of two different feedback mechanisms could overcome the tracking difficulties. The system was composed of Raspberry Pi 4 and ATmega128 controllers, a camera, electronic circuits, and stepper motors. The proposed control system could distinguish the objects (e.g., sun or clouds) in front of the camera, and process the data required to run the solar tracker. The integration of extra photosensors and the camera was able to avoid the cloud disturbance. To investigate the efficacy of the solar tracker, it was integrated with the optical fiber cable to transmit harvested daylight for an indoor illumination.