Light Detecting and Ranging (LIDAR) for in-situ heliostat optical error assessment
| DWPI Title: Method for facilitating optical error assessment of large-scale concentrated solar power heliostat, involves correcting canting angles of facets of heliostat from which canting angle associated with each respective facet of facets is calculated by processor |
| Abstract: A system and method for optical assessment of a heliostat includes obtaining a point cloud data representing an image of the heliostat; isolating the data; filtering and fitting the filtered heliostat data to a bounding box; translating the heliostat data to a plane to aid in segmentation; segmenting a plurality of facets of the heliostat fitting each of the segmented facets to a respective plane; generating normal vectors characterizing each of the plurality of facets; and calculating a canting angle associated with each respective facet of the plurality of facets. A heliostat with mirrored facets and a scanner are provided. The scanner captures point cloud data representing the heliostat, which is segmented for each facet. Normal vectors characterize the facets and a canting angle is calculated for the respective facet. |
| Use: Method for facilitating optical error assessment of an in-situ heliostat i.e. large-scale concentrated solar power heliostat, using a three dimensional imaging sensor i.e. surveying-quality three dimensional scanning light detecting and ranging, and two dimensional imaging sensor i.e. digital camera, by a computing apparatus. |
| Advantage: The method enables accurately determining overall tracking angles and canting errors to 0.5 milli-radians. The method enables giving a three dimensional model of a backing structure and relative angles between a camera, the heliostat under assessment and heliostat in reflection, so that features can be located on a backside of the heliostat in reflection. The method enables three-dimensional-light detecting and ranging to acquire highly accurate point cloud data across multiple heliostats. |
| Novelty: The method (100) involves generating point cloud data representing a heliostat by measuring spatial parameters of the heliostat. Heliostat data is isolated (106) in the point cloud data with a blob detection algorithm. The isolated heliostat data is filtered. The filtered heliostat data is fit (110) to a bounding box. The heliostat data is translated (112) to a plane. A set of facets of the heliostat is segmented (114). Each of the segmented facets is fit (116) to a respective plane. Normal vectors characterizing each of the set of facets are generated. A canting angle associated with each respective facet of the set of facets is calculated (118). Canting angles of facets of the heliostat from which the canting angle associated with each respective facet of the set of facets is calculated are corrected by a processor. |
| Filed: 10/8/2020 |
| Application Number: US17065661A |
| Tech ID: SD 15183.0 |
| This invention was made with Government support under Contract No. DE-NA0003525 awarded by the United States Department of Energy/National Nuclear Security Administration. The Government has certain rights in the invention. |
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