An efficient concentrative photovoltaic solar system with Bayesian selection of optimal solar tracking algorithms

Abstract

As the importance of sustainable energy has been rapidly growing, a concentrative photovoltaic (CPV) solar system is drawing much attention. In order for a system to operate efficiently, a deliberate solar tracking system must be equipped because an optimal tilt of solar panel is changed as the Sun orbits its trajectory. However, many existing tracking methods did not clearly consider the effect of various weather conditions, even though the performance of tracking method is subject to them. In this paper, we propose a CPV solar system that chooses the most proper solar tracking method among the group of heterogeneous tracking algorithms, based on an inference on the current weather conditions with Bayesian network (BN). We use 13 features derived from image processing and implement four tracking algorithms which have relative performance depending on nine different weather conditions. We constructed the working CPV system and collected the 1630 image data every three minutes for five hours over a period of 16 days. The proposed BN shows 93.9% accuracy for inferencing weather conditions, and the proposed system shows 16.58% higher power productivity, compared to a pinhole system and other existing methods.