Flexible Dynamic Sun Tracking System (STS) Employing Machine Vision Control Approach

In this study, a machine vision control approach for a sun tracking system (STS) is designed, implemented, and performance is evaluated. The aim is to dynamically track the sun's centroid with high flexibility under low irradiation conditions due to weather conditions such as cloud cover. The STS is designed to work independently in the absence of a manual setup of the location's spatiotemporal data. The prototype used a 180 degrees FOV high-resolution camera as the primary sensor for accurate image processing and adaptive control technique to regulate electrical signals to the two servo motors (pan and tilt). The NVIDIA Jetson AI-Computing Board is used for the autonomous deployment of the tracker. It was shown in the measurement that the sun's centroid tracking accuracy of the proposed tracker for Az (gamma) and Al (alpha) is 0.23 degrees and 0.66 degrees, respectively, with the Solar Position Algorithm (SPA) while 0.59 degrees and 0.65 degrees, respectively with the commercial solar tracker, STR-22G. The results graphically and statistically show that the prototype using machine vision can measure accurately and has the same tracking performance as compared with the two established measurements. The STS application based on machine vision control approach can meet the requirements for a dynamic and flexible control system for designed Parabolic Dish Solar Concentrators.