Question-Based Gap Analysis of Heliostat Optical Metrology Methods
DOI:
https://doi.org/10.52825/solarpaces.v1i.708Keywords:
Heliostat Optical Metrology, Heliostat Consortium, Gap AnalysisAbstract
As part of the DOE Heliostat Consortium Roadmap study, we investigated optical metrology systems for heliostats, seeking areas where further research was needed. We began by considering optical metrology questions of interest across the heliostat development cycle and identified information types common across this spectrum. In addition to raising questions of interest, each development cycle phase implied specific operation requirements. Combining these yielded 13 core problem statements, four of which do not appear to have a readily available solution: (a) in-situ measurement of high-resolution maps of mirror surface normals, for all heliostat tilt angles and heliostats far from the tower; (b) accelerated heliostat calibration; (c) high-speed in situ measurement of heliostat surface normal maps and pointing directions; (d) ground truth methods for verifying the accuracy of surface normal map measurements. This analysis may provide input to the selection of future research goals.
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References
G. Zhu, et al. Roadmap to Advance Heliostat Technologies for Concentrating Solar-Thermal Power. https://heliocon.org/roadmap_report.html
J. W. Strachan. Revisiting the BCS, a Measurement System for Characterizing the Op-tics of Solar Collectors. Sandia Technical Report SAND92-2789C, 1992.
A. Pfahl et al. Progress in heliostat development. Solar Energy 152, pp. 3-37, 2017. https://doi.org/10.1016/j.solener.2017.03.029.
J. C. Sattler et al. Review of heliostat calibration and tracking control methods. Solar Energy 207, pp. 110-132, 2020. https://doi.org/10.1016/j.solener.2020.06.030.
CSP Services. QDec-M. https://www.cspservices.de/wp-content/uploads/CSPS-QDec.pdf
C. Andraka, et al. Rapid Reflective Facet Characterization Using Fringe Reflection Techniques. Solar Energy Engineering 136, February 2014. https://doi.org/10.1115/1.4024250.
S. Ulmer, et al. Automated high resolution measurement of heliostat slope errors.
Solar Energy 85, pp. 685-687, 2011. https://doi.org/10.1016/j.solener.2010.01.010.
N. Goldberg and A. Zisken. Heliostat surface estimation by image processing. Energy Procedia 69, pp. 1885-1894, 2015. https://doi.org/10.1016/j.egypro.2015.03.171.
M. Ayres, et al. Heliostat Aiming Corrections with Bad Data Detection. AIP Conference Proceedings 2303, 030004 (2020). https://doi.org/10.1063/5.0028603.
A. Sonn, et al. Estimating Orientations of Tracking Heliostats Using Circumsolar Radi-ance. Presented SolarPACES 2020.
W. Jessen, et al. A Two-Stage Method for Measuring the Heliostat Offset. AIP Confer-ence Proceedings 2445, 070005 (2022). https://doi.org/10.1063/5.0087036.
R. Mitchell and G. Zhu. A non-intrusive optical (NIO) approach to characterize helio-stats in utility-scale power tower plants. Solar Energy 209, pp. 431–445, 2020. https://doi.org/10.1016/j.solener.2020.09.004.
R. Brost, et al. High-Speed In-Situ Optical Scanning of Heliostat Fields. Presented in SolarPACES 2021.
K. Blume, et al. Dynamic photogrammetry applied to a real scale heliostat: Insights into the wind-induced behavior and effects on the optical performance. Solar Energy 212, 2020. https://doi.org/10.1016/j.solener.2020.10.056.
CSP Services. TraCS. https://www.cspservices.de/wp-content/uploads/CSPS-TraCS-Soiling.pdf
Bern, et al. AVUS – Automatic Soiling Rate Measurement Supporting O&M and Per-formance Prediction of Concentrating Solar Thermal Power Plants – Analysis of Soil-ing Events. Presented in SolarPACES 2022.
J. Coventry, et al. A Robotic Vision System for Inspection of Soiling at CSP Plants. AIP Conference Proceedings 2303, 100001 (2020). https://doi.org/10.1063/5.0029493.
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