Heliostat surface shape characterization for accurate flux prediction

A.M. Bonanos, M. Faka, D. Abate, S. Hermon and M.J. Blanco

Renewable Energy, 2019, vol. 142, issue C, 30-40

Abstract: 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.

Keywords: Heliostat surface; Photogrammetry; Laser scanning; Ray-tracing; Flux prediction (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S096014811930535X
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:142:y:2019:i:c:p:30-40

DOI: 10.1016/j.renene.2019.04.051

Access Statistics for this article

Renewable Energy is currently edited by Soteris A. Kalogirou and Paul Christodoulides

More articles in Renewable Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().