Machine vision as a method for characterizing solar tracker performance

This paper describes an approach to measuring the pointing error of solar trackers using a machine vision system. GreenMountain Engineering developed a device that employs this method with a custom embedded system and image processing software. The technical approach of this device (called the “Trac-Stat SL1”) is presented here with the results of extended tests on a commercially available tracker. Our conclusion is that using this method of machine vision to characterize solar trackers is useful for tracker and tracker controller research, development, end-user qualification, and other applications where calibrated, accurate information about the performance of tracking systems is needed.