Pargmann, Max und Ebert, Jan und Maldonado Quinto, Daniel und Götz, Markus und Pitz-Paal, Robert und Kesselheim, Stefan (2024) Automatic heliostat learning for in situ concentrating solar power plant metrology with differentiable ray tracing. Nature Communications (15), Seiten 6997-1. Nature Publishing Group. doi: 10.1038/s41467-024-51019-z. ISSN 2041-1723.
![]() |
PDF
- Verlagsversion (veröffentlichte Fassung)
2MB |
Offizielle URL: https://www.nature.com/articles/s41467-024-51019-z#article-info
Kurzfassung
Concentrating solar power plants are a clean energy source capable of competitive electricity generation even during night time, as well as the production of carbon-neutral fuels, offering a complementary role alongside photovoltaic plants. In these power plants, thousands of mirrors (heliostats) redirect sunlight onto a receiver, potentially generating temperatures exceeding 1000°C. Practically, such efficient temperatures are never attained. Several unknown, yet operationally crucial parameters, e.g., misalignment in sun-tracking and surface deformations can cause dangerous temperature spikes, necessitating high safety margins. For competitive levelized cost of energy and large-scale deployment, in-situ error measurements are an essential, yet unattained factor. To tackle this, we introduce a differentiable ray tracing machine learning approach that can derive the irradiance distribution of heliostats in a data-driven manner from a small number of calibration images already collected in most solar towers. By applying gradient-based optimization and a learning non-uniform rational B-spline heliostat model, our approach is able to determine sub-millimeter imperfections in a real-world setting and predict heliostat-specific irradiance profiles, exceeding the precision of the state-of-the-art and establishing full automatization. The new optimization pipeline enables concurrent training of physical and data-driven models, representing a pioneering effort in unifying both paradigms for concentrating solar power plants and can be a blueprint for other domains.
elib-URL des Eintrags: | https://elib.dlr.de/208935/ | ||||||||||||||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Dokumentart: | Zeitschriftenbeitrag | ||||||||||||||||||||||||||||
Titel: | Automatic heliostat learning for in situ concentrating solar power plant metrology with differentiable ray tracing | ||||||||||||||||||||||||||||
Autoren: |
| ||||||||||||||||||||||||||||
Datum: | August 2024 | ||||||||||||||||||||||||||||
Erschienen in: | Nature Communications | ||||||||||||||||||||||||||||
Referierte Publikation: | Ja | ||||||||||||||||||||||||||||
Open Access: | Ja | ||||||||||||||||||||||||||||
Gold Open Access: | Ja | ||||||||||||||||||||||||||||
In SCOPUS: | Ja | ||||||||||||||||||||||||||||
In ISI Web of Science: | Ja | ||||||||||||||||||||||||||||
DOI: | 10.1038/s41467-024-51019-z | ||||||||||||||||||||||||||||
Seitenbereich: | Seiten 6997-1 | ||||||||||||||||||||||||||||
Verlag: | Nature Publishing Group | ||||||||||||||||||||||||||||
Name der Reihe: | Nature Communications | ||||||||||||||||||||||||||||
ISSN: | 2041-1723 | ||||||||||||||||||||||||||||
Status: | veröffentlicht | ||||||||||||||||||||||||||||
Stichwörter: | Differentiable Raytracing, Heliostat Calibration, Deflectometry, Beam Characterization | ||||||||||||||||||||||||||||
HGF - Forschungsbereich: | Energie | ||||||||||||||||||||||||||||
HGF - Programm: | Materialien und Technologien für die Energiewende | ||||||||||||||||||||||||||||
HGF - Programmthema: | Thermische Hochtemperaturtechnologien | ||||||||||||||||||||||||||||
DLR - Schwerpunkt: | Energie | ||||||||||||||||||||||||||||
DLR - Forschungsgebiet: | E SW - Solar- und Windenergie | ||||||||||||||||||||||||||||
DLR - Teilgebiet (Projekt, Vorhaben): | E - Intelligenter Betrieb | ||||||||||||||||||||||||||||
Standort: | Köln-Porz | ||||||||||||||||||||||||||||
Institute & Einrichtungen: | Institut für Solarforschung > Solare Kraftwerktechnik | ||||||||||||||||||||||||||||
Hinterlegt von: | Brockel, Linda | ||||||||||||||||||||||||||||
Hinterlegt am: | 22 Nov 2024 10:22 | ||||||||||||||||||||||||||||
Letzte Änderung: | 17 Feb 2025 11:07 |
Nur für Mitarbeiter des Archivs: Kontrollseite des Eintrags