In Situ Enhancement of Heliostat Calibration Using Differentiable Ray Tracing and Artificial Intelligence

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Abstract: The camera target method is the most commonly used calibration method for heliostats at solar tower power plants to minimize their sun tracking errors. In this method, individual heliostats are moved to a white surface and their deviation from the targeted position is measured. A regression is used to calculate errors in a geometry model from the tabular data obtained in this way. For modern aim point strategies, or simply heliostats in the rearmost end of the field, extremely high accuracies are needed, which can only be achieved by many degrees of freedom in the geometry model. The problem here is that the camera target method produces only a very small data set per heliostat, which limits the number of free variables and thus the accuracy. In this work, we extend existing ray tracing methods for solar towers with a differentiable description, allowing for the first time a data-driven optimization of object parameters within the ray tracing environment. Therefore, the heliostat c.... https://www.tib-op.org/ojs/index.php/solarpaces/article/view/642

Location: Deutsche Nationalbibliothek Frankfurt am Main

Extent: Online-Ressource

Language: Englisch

Bibliographic citation: In Situ Enhancement of Heliostat Calibration Using Differentiable Ray Tracing and Artificial Intelligence ; volume:1 ; year:2022
SolarPACES conference proceedings ; 1 (2022)

Creator: Pargmann, Max
Ebert, Jan
Kesselheim, Stefan
Maldonado Quinto, Daniel
Pitz-Paal, Robert

DOI: 10.52825/solarpaces.v1i.642

URN: urn:nbn:de:101:1-2024012520455805900223

Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.

Last update: 15.08.2025, 7:29 AM CEST

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Pargmann, Max
Ebert, Jan
Kesselheim, Stefan
Maldonado Quinto, Daniel
Pitz-Paal, Robert

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In Situ Enhancement of Heliostat Calibration Using Differentiable Ray Tracing and Artificial Intelligence

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