Heliostat Shape and Orientation by Edge Detection

Author:

Röger Marc1,Prahl Christoph1,Ulmer Steffen1

Affiliation:

1. German Aerospace Center (DLR), Institute of Technical Thermodynamics Solar Research, Plataforma Solar de Almería (PSA), Apartado 39, E-04200 Tabernas, Spain

Abstract

The heliostats of central receiver solar power plants reach dimensions up to 150 m2 with focal lengths up to 1000 m. Their optical properties and tracking accuracy have great influence on the power plant efficiency and need to be monitored both at plant start up and during operation. Up to now, there are few efficient and fast measurement techniques that allow the heliostat properties to be measured. Flux density measurements and close-range photogrammetry are possible approaches, yet they do not fulfill the requirement to be accurate, inexpensive, and fast at the same time. In this paper, we present a noncontact measurement principle, which uses edge detection to extract the heliostat and facet vertices. This information is used to calculate the surface normals. Furthermore, the corners can replace retroreflective targets generally used in close-range photogrammetry, thus, enabling a fast and completely automatic evaluation of the three-dimensional heliostat structure. The pictures are provided by a digital camera, which is mounted on a pan tilt head on top of the central receiver tower, offering visibility to all heliostats and allowing the automated qualification of whole heliostat fields in a short period of time. It is shown that measurement uncertainties in heliostat orientation for the investigated heliostat are below 4 mrad in 80% of the relevant heliostat positions. Heliostat orientation is available within three minutes. Photogrammetric measurements based on edge detection at a 40 m2 CESA-1 heliostat at the Plataforma Solar de Almerìa exhibit an accuracy of 1.6 mrad for a single-facet normal vector with the results being available within 30 min. The reduced measurement time allows the economic characterization of entire heliostat fields. The lower accuracy compared with manual photogrammetry with retroreflective targets is still sufficient to detect facet misalignments in existing heliostat fields.

Publisher

ASME International

Subject

Energy Engineering and Power Technology,Renewable Energy, Sustainability and the Environment

Reference8 articles.

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3. Hough, P. V. C. , 1962, “Methods and Means for Recognizing Complex Patterns,” U.S. Patent No. 3,069,654.

4. Houck, C. R., Joines, J. A., and Kay, M. G., 1995, “A Genetic Algorithm for Function Optimization: A Matlab Implementation,” North Carolina State University, Raleigh, NC, Paper No. NCSU-IE-TR-95-09.

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