This paper summarizes findings from a small, mixed-method research study examining industry perspectives on the potential for new forms of automation to invigorate the concentrating solar power (CSP) industry. In Fall 2021, the Solar Energy Technologies Office (SETO) of the United States Department of Energy (DOE) funded Sandia National Laboratories to elicit industry stakeholder perspectives on the potential role of automated systems in CSP operations. We interviewed eleven CSP professionals from five countries, using a combination of structured and open comment response modes. Respondents indicated a preference for automated systems that support heliostat manufacturing and installation, calibration, and responsiveness to shifting weather conditions. This pilot study demonstrates the importance of engaging industry stakeholders in discussions of technology research and development, to promote adoptable, useful innovation.
This project extends Sandia's experience in Light Detection And Ranging (LiDAR) to gain an understanding of the abilities and limits of using 3D laser scanning to capture the relative canting angles between heliostat mirror surfaces in 3D space to an accuracy sufficient to measure canting errors. To the authors' knowledge, this approach has never been developed or implemented for this purpose. The goal is to be able to automatically perform a 3D scan, retrieve the data, and use computational geometry and a-priori mechanical knowledge of the heliostats (facet arrangement and size) to filter and isolate the facets, and fit planar models to the facet surfaces. FARO FocusS70 laser range scanners are used, which provide a dense data coverage of the scan area in the form of a 3D point-cloud. Each point has the 3D coordinates of the surface position illuminated by the device as it scans the laser beam over an area, both in azimuth and elevation. These scans can contain millions of points in total. The initial plan was to primarily use the back side of the heliostat to capture the mirror (the back side being opaque). It was not expected to capture high-quality data from the reflective front side. The discovery that the front side did, indeed, yield surface data was surprising. This is a function of the soiling, or collected dust, on the mirror surface. Typical point counts on the mirror facets are seen to be between 10k - 100k points per facet, depending on the facet area and the scan point density. By collecting facet surface points, the data can be used to calculate an individual planar fit per facet, the normals of which correlate directly with the facet pointing angle. Comparisons with neighboring facets yield the canting angles. The process includes software which automatically: 1) controls the LiDAR scanner and downloads the resultant scan data, 2) isolates the heliostat data from the full scan, 3) filters the points associated with each individual facet, and 4) calculates the planar fit and relative canting angles for each facet. The goal of this work has been to develop this system to measure heliostat canting errors to less than 0.25 mrad accuracy, with processing time under 5 minutes per heliostat. A future goal is to place this or a comparable sensor on an autonomous platform, along with the software system, to collect and analyze heliostats in the field for tracking and canting errors in real time. This work complements Sandia's strategic thrust in autonomy for CSP collector systems.
Borrowing from nature, neural-inspired interception algorithms were implemented onboard a vehicle. To maximize success, work was conducted in parallel within a simulated environment and on physical hardware. The intercept vehicle used only optical imaging to detect and track the target. A successful outcome is the proof-of-concept demonstration of a neural-inspired algorithm autonomously guiding a vehicle to intercept a moving target. This work tried to establish the key parameters for the intercept algorithm (sensors and vehicle) and expand the knowledge and capabilities of implementing neural-inspired algorithms in simulation and on hardware.