Inverse Deep Learning Ray Tracing for Heliostat Surface Prediction

TL;DR

This paper introduces inverse Deep Learning Ray Tracing (iDLR), a novel method to predict heliostat surface profiles from flux density images, improving safety and efficiency in CSP plants.

Contribution

The study presents a new deep learning-based inverse ray tracing approach and a comprehensive NURBS heliostat model, advancing surface prediction accuracy and operational safety.

Findings

iDLR accurately predicts heliostat surfaces from flux images.

Flux density contains sufficient information for surface reconstruction.

The NURBS model offers a new standard for heliostat surface parameterization.

Abstract

Concentrating Solar Power (CSP) plants play a crucial role in the global transition towards sustainable energy. A key factor in ensuring the safe and efficient operation of CSP plants is the distribution of concentrated flux density on the receiver. However, the non-ideal flux density generated by individual heliostats can undermine the safety and efficiency of the power plant. The flux density from each heliostat is influenced by its precise surface profile, which includes factors such as canting and mirror errors. Accurately measuring these surface profiles for a large number of heliostats in operation is a formidable challenge. Consequently, control systems often rely on the assumption of ideal surface conditions, which compromises both safety and operational efficiency. In this study, we introduce inverse Deep Learning Ray Tracing (iDLR), an innovative method designed to predict heliostat surfaces based solely on target images obtained during heliostat calibration. Our simulation-based investigation demonstrates that sufficient information regarding the heliostat surface is retained in the flux density distribution of a single heliostat, enabling deep learning models to accurately predict the underlying surface with deflectometry-like precision for the majority of heliostats. Additionally, we assess the limitations of this method, particularly in relation to surface accuracy and resultant flux density predictions. Furthermore, we are presenting a new comprehensive heliostat model using Non-Uniform Rational B-Spline (NURBS) that has the potential to become the new State of the Art for heliostat surface parameterization. Our findings reveal that iDLR has significant potential to enhance CSP plant operations, potentially increasing the overall efficiency and energy output of the power plants.