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Heliostat surface shape characterization for accurate flux prediction

Bonanos, A.M., Faka, M., Abate, D., Hermon, S., Blanco, M.J.
Renewable energy 2019 v.142 pp. 30-40
data collection, photogrammetry, prediction, renewable energy sources, solar radiation
The performance of a heliostat field is directly related to the optical quality of the mirror surfaces used to reflect solar radiation onto the receiver. In this paper, two methods are used to characterize the actual shape of the surface of a heliostat, using laser scanning and photogrammetry. The accuracy of each technique for the intended purpose is reported, while the methodology for analyzing the resulting point clouds is presented. The shape of the reflecting surface is reconstructed from the point clouds generated by each one of the methods used to characterize the actual shape of the heliostat surface. The Tonatiuh ray tracing program is used to compare the flux distributions of the heliostat obtained in this way, with the intended ideal parabolic shape of the heliostat. The flux distributions are also compared to optical images of the heliostat reflection on a plane target. Surface reconstruction from the photogrammetry point cloud results in a better agreement between the optical and simulated flux distributions on the target. Comparing results from the reconstructed surfaces and the ideal parabolic surface to optical images, a 20% improvement in prediction is achieved by using the photogrammetry point cloud as opposed to the ideal parabolic shape.