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@INPROCEEDINGS{Pargmann:1025767,
      author       = {Pargmann, Max and Ebert, Jan and Kesselheim, Stefan and
                      Maldonado Quinto, Daniel and Pitz-Paal, Robert},
      title        = {{I}n {S}itu {E}nhancement of {H}eliostat {C}alibration
                      {U}sing {D}ifferentiable {R}ay {T}racing and {A}rtificial
                      {I}ntelligence},
      volume       = {1},
      reportid     = {FZJ-2024-03135},
      pages        = {10 p.},
      year         = {2023},
      abstract     = {The camera target method is the most commonly used
                      calibration method for heliostats at solar tower power
                      plants to minimize their sun tracking errors. In this
                      method, individual heliostats are moved to a white surface
                      and their deviation from the targeted position is measured.
                      A regression is used to calculate errors in a geometry model
                      from the tabular data obtained in this way. For modern aim
                      point strategies, or simply heliostats in the rearmost end
                      of the field, extremely high accuracies are needed, which
                      can only be achieved by many degrees of freedom in the
                      geometry model. The problem here is that the camera target
                      method produces only a very small data set per heliostat,
                      which limits the number of free variables and thus the
                      accuracy. In this work, we extend existing ray tracing
                      methods for solar towers with a differentiable description,
                      allowing for the first time a data-driven optimization of
                      object parameters within the ray tracing environment.
                      Therefore, the heliostat calibration can take place directly
                      within the ray tracing environment. Thus, the image data
                      acquired during the measurement can be processed directly
                      and more information about the orientation of the heliostat
                      can be obtained. Within a simple example we show the
                      advantages of the method, which converges faster and
                      corrects errors that could not be considered before. Without
                      any disadvantages or additional costs, the state-of-the-art
                      calibration method can be improved.},
      month         = {Sep},
      date          = {2022-09-27},
      organization  = {28th International Conference on
                       Concentrating Solar Power and Chemical
                       Energy Systemsf CSP and Hybridized
                       Systems, Albuquerque (USA), 27 Sep 2022
                       - 30 Sep 2022},
      cin          = {JSC},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
                      (SDLs) and Research Groups (POF4-511)},
      pid          = {G:(DE-HGF)POF4-5111},
      typ          = {PUB:(DE-HGF)8},
      UT           = {WOS:001324829700026},
      doi          = {10.52825/solarpaces.v1i.642},
      url          = {https://juser.fz-juelich.de/record/1025767},
}