Automatic heliostat learning for in situ concentrating solar power plant metrology with differentiable ray tracing
Published in Nature Communications, 15(1), 6997, 2024
Accurate heliostat alignment is critical for the efficiency of concentrating solar power tower plants, but calibration is traditionally slow and labor-intensive. This work introduces a differentiable ray tracing approach that automatically learns heliostat geometries and alignment parameters directly from in-situ operational measurements, enabling scalable, self-calibrating metrology for solar tower plants.
Recommended citation: Pargmann, M., Ebert, J., Götz, M., Maldonado Quinto, D., Pitz-Paal, R., & Kesselheim, S. (2024). "Automatic heliostat learning for in situ concentrating solar power plant metrology with differentiable ray tracing." Nature Communications, 15(1), 6997.
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