Towards deep learning based airborne monitoring methods for heliostats in solar tower power plants

Rafal Broda; Alexander Schnerring; Julian J. Krauth; Marc Röger; Robert Pitz-Paal

doi:10.1117/12.2676821

4 October 2023 Towards deep learning based airborne monitoring methods for heliostats in solar tower power plants

Rafal Broda, Alexander Schnerring, Julian J. Krauth, Marc Röger, Robert Pitz-Paal

Proceedings Volume 12671, Advances in Solar Energy: Heliostat Systems Design, Implementation, and Operation; 1267108 (2023) https://doi.org/10.1117/12.2676821
Event: SPIE Optical Engineering + Applications, 2023, San Diego, California, United States

Abstract

While deep learning methods have proven their superiority over conventional image processing techniques in many domains, their use in airborne heliostat monitoring remains limited. Our aim is to bridge this gap by developing models to improve and extend existing image-based measurement methods in this field. We use Blender and BlenderProc to generate synthetic image data, which grants us access to vast amounts of training data essential for developing effective deep learning models. The exemplary model we train can potentially solve the following tasks related to airborne heliostat field monitoring: detection of heliostats and detection of mirror facet corners. Our promising preliminary results demonstrate the applicability of our approach to use synthetic training data for the development of the intended deep learning models.

Conference Presentation

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Rafal Broda, Alexander Schnerring, Julian J. Krauth, Marc Röger, and Robert Pitz-Paal "Towards deep learning based airborne monitoring methods for heliostats in solar tower power plants", Proc. SPIE 12671, Advances in Solar Energy: Heliostat Systems Design, Implementation, and Operation, 1267108 (4 October 2023); https://doi.org/10.1117/12.2676821

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