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001037639 1001_ $$00000-0002-4705-6285$$aPargmann, Max$$b0$$eCorresponding author
001037639 245__ $$aAutomatic heliostat learning for in situ concentrating solar power plant metrology with differentiable ray tracing
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001037639 520__ $$aConcentrating 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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001037639 7001_ $$0P:(DE-Juel1)187002$$aEbert, Jan$$b1$$ufzj
001037639 7001_ $$0P:(DE-Juel1)162390$$aGötz, Markus$$b2
001037639 7001_ $$00000-0003-2929-8667$$aMaldonado Quinto, Daniel$$b3
001037639 7001_ $$0P:(DE-HGF)0$$aPitz-Paal, Robert$$b4
001037639 7001_ $$0P:(DE-Juel1)185654$$aKesselheim, Stefan$$b5
001037639 773__ $$0PERI:(DE-600)2553671-0$$a10.1038/s41467-024-51019-z$$gVol. 15, no. 1, p. 6997$$n1$$p6997$$tNature Communications$$v15$$x2041-1723$$y2024
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