The heliostat collector field is the front-end of large solar power tower plants. Any negative performance impacts on the collector field will propagate down the stream of subsystems, which can negatively impact energy production and financial revenues. An underperforming collector field will provide insufficient solar flux to the receiver resulting in the receiver running at below capacity and not producing the thermal energy required for thermal storage and to run the power block at optimum efficiency. It is prudent to have an optimally operating collector field especially for future Gen3+ plants. The performance of a deployed collector field can be impacted by mirror quality (surface and shape), mirror canting errors, tracking errors, and soiling. Any of these error sources can exist during installation and further degrade over time and, if left unattended, can drastically reduce the overall performance of the plant. Concentrating solar power (CSP) plant operators require information about the collector field performance to quickly respond with corrections, if needed, and maintain optimum plant performance. This type of fast response is especially critical for future Gen3+ plants, which require high collector field performance consistently. However, power tower operators have struggled with finding or developing the right tools to assess and subsequently fix canting errors on in-field heliostats efficiently and accurately. Sandia National Laboratories National Solar Thermal Test Facility (NSTTF) is developing an aerial imaging system to evaluate facet canting quality on in-situ and offline heliostats. The imaging system is mounted on an unmanned aerial system (UAS) to collect images of targets structures in reflection. Image processing on the collected images is then performed to get estimates of the heliostat canting errors. The initial work is to develop the system definition that achieves the required measurement sensitivities, which is on the order of 0.25-0.5 mrad for canting errors. The goal of the system is to measure heliostat canting errors to <0.5 mrad accuracy and provide data on multiple heliostats within a day. In this paper, the development of the system, a sensitivity analysis, and initial measurement results on two NSTTF heliostats are provided.
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.
This report 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.
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.
This project has sought to develop new uses for surveying-quality Light Detecting and Ranging (LiDAR) 3D scanning sensors in the automatic/autonomous assessment of optical errors in largescale concentrating solar power heliostat fields. Past experiments have demonstrated the ability of a 3D-LiDAR to acquire highly accurate point cloud measurements across several Sandia NSTTF heliostats. The goal of this project is to expand upon this work to see if and how it can be used in large commercial heliostat fields.
The computer vision field has undergone a revolution of sorts in the past five years. Moore's law has driven real-time image processing from the domain of dedicated, expensive hardware, to the domain of commercial off-the-shelf computers. This thesis describes their work on the design, analysis and implementation of a Real-Time Shape from Silhouette Sensor (RT S{sup 3}). The system produces time-varying volumetric data at real-time rates (10-30Hz). The data is in the form of binary volumetric images. Until recently, using this technique in a real-time system was impractical due to the computational burden. In this thesis they review the previous work in the field, and derive the mathematics behind volumetric calibration, silhouette extraction, and shape-from-silhouette. For the sensor implementation, they use four color camera/framegrabber pairs and a single high-end Pentium III computer. The color cameras were configured to observe a common volume. This hardware uses the RT S{sup 3} software to track volumetric motion. Two types of shape-from-silhouette algorithms were implemented and their relative performance was compared. They have also explored an application of this sensor to markerless motion tracking. In his recent review of work done in motion tracking Gavrila states that results of markerless vision based 3D tracking are still limited. The method proposed in this paper not only expands upon the previous work but will also attempt to overcome these limitations.
This paper describes a system, which acquires 3D data and tracks an eleven degree of freedom human model in real-time. Using four cameras we create a time-varying volumetric image (a visual hull) of anything moving in the space observed by all four cameras. The sensor is currently operating in a volume of approximately 500,000 voxels (1.5 inch cubes) at a rate of 25 Hz. The system is able to track the upper body dynamics of a human (x,y position of the body, a torso rotation, and four rotations per arm). Both data acquisition and tracking occur on one computer at a rate of 16 Hz. We also developed a calibration procedure, which allows the system to be moved and be recalibrated quickly. Furthermore we display in real-time, either the data overlaid with the joint locations or a human avatar. Lastly our system has been implemented to perform crane gesture recognition.