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| Hauptseite > Publikationsdatenbank > Automatic heliostat learning for in situ concentrating solar power plant metrology with differentiable ray tracing > print |
| Hauptseite > Publikationsdatenbank > Automatic heliostat learning for in situ concentrating solar power plant metrology with differentiable ray tracing > print |
| 001 | 1037639 | ||
| 005 | 20250203124501.0 | ||
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| 100 | 1 | _ | |a Pargmann, Max |0 0000-0002-4705-6285 |b 0 |e Corresponding author |
| 245 | _ | _ | |a Automatic heliostat learning for in situ concentrating solar power plant metrology with differentiable ray tracing |
| 260 | _ | _ | |a London |c 2024 |b Springer Nature |
| 336 | 7 | _ | |a article |2 DRIVER |
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| 520 | _ | _ | |a 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. |
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| 700 | 1 | _ | |a Götz, Markus |0 P:(DE-Juel1)162390 |b 2 |
| 700 | 1 | _ | |a Maldonado Quinto, Daniel |0 0000-0003-2929-8667 |b 3 |
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| 773 | _ | _ | |a 10.1038/s41467-024-51019-z |g Vol. 15, no. 1, p. 6997 |0 PERI:(DE-600)2553671-0 |n 1 |p 6997 |t Nature Communications |v 15 |y 2024 |x 2041-1723 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/1037639/files/s41467-024-51019-z.pdf |y OpenAccess |
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