Designing and modelling dual axis solar tracker system

Energy consumption of industrialized and globalized world has been increasing day by day. Vast majority of world's energy need were provided by fossil fuels. However, the tendency to alternative energy sources has increased due to the fact that such energy sources will be exhausted, and they are not meeting energy need apprehension, and also they are not-so-eco. As these renewable energy sources are clean, eco-friendly, and sustainable among the other energy sources, mankind tend to use these sources. The most important renewable energy sources such as sun, wind, biomass, geothermal, hydraulic, waves have been started to use. To be the most important source among them, solar energy has been the most used primary energy in recent years. In this way, solar panels are used to obtain electrical energy from the sun. Various methods are used to achieve optimal efficiency from the solar panels. One of these methods is solar tracking system. In this thesis study, a dual axis solar tracking and a fixed panel system has been designed for an experimental purpose. The designed tracking system has been designed that could tracking the sun in both dual axis and single axis. Each part of the experimentally designed system has been modeled in Matlab/Simulink environment. The productivity analysis has been made by comparing the measurements which was taken in two different days from the tracking and fixed panel in the experimental setup. According to the result of the comparison of the first and second day, it is determined that tracking system is more efficient than fixed system. It is determined that the tracking system %37,234 and %35,756 more efficient than fixed system on the first day and second day respectively. Therefore, it is concluded that the tracking system % 35-40 more efficient than fixed system. Tracking system and fixed system which was modeled in Matlab/Simulink, has been compared by their experimental and simulation results. As a result of the comparison, it was observed that the modeling and real data results majorly overlap with each other. The simulation model enables to make analysis under varying environmental and physical factors in the simulation environment instead of experimental setup.