TY  - JOUR
AU  - Pargmann, Max
AU  - Ebert, Jan
AU  - Götz, Markus
AU  - Maldonado Quinto, Daniel
AU  - Pitz-Paal, Robert
AU  - Kesselheim, Stefan
TI  - Automatic heliostat learning for in situ concentrating solar power plant metrology with differentiable ray tracing
JO  - Nature Communications
VL  - 15
IS  - 1
SN  - 2041-1723
CY  - London
PB  - Springer Nature
M1  - FZJ-2025-00804
SP  - 6997
PY  - 2024
AB  - 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.
LB  - PUB:(DE-HGF)16
C6  - 39143091
UR  - <Go to ISI:>//WOS:001291857100021
DO  - DOI:10.1038/s41467-024-51019-z
UR  - https://juser.fz-juelich.de/record/1037639
ER  -