Prediction of Solar Concentration Flux Distribution for a Heliostat Based on Lunar Concentration Image and Generative Adversarial Networks

Abstract

ABSTRACT The predictive analysis of solar flux distribution on the receiver surface is critical in optimizing the concentration processes of concentrating solar power (CSP) plants. Due to the difficulties of directly measuring the solar flux distribution of the heliostat field, tracking the Moon and measuring the lunar concentration ratio distribution become a promising option. However, many factors affect the flux distribution of a heliostat field. To obtain an accurate predictive model for the solar flux distribution, we propose a deep-learning method using conditional generative adversarial networks (cGAN) and lunar concentration images. The method can take account of tracking errors of individual heliostats, defects of reflecting surfaces, as well as atmospheric attenuation effects, and has the potential to give a reliable prediction of solar flux distribution. Mathematical relations between the solar flux distribution and the solar concentration ratio distribution are discussed in the paper. Experiments have been designed and carried out with an ordinary heliostat at the Beijing Badaling solar concentrating power station. Experimental results show that the AI-generated solar concentration ratio distributions are very close to the actual solar concentration ratio distributions, demonstrating the feasibility of AI models for the prediction of solar flux distribution.