Artificial intelligence-based heliostat characterisation through synthetic data

This work proposes a fast, non-intrusive methodology for heliostat mirror surface characterisation based on Deep Learning (DL) models trained exclusively on synthetic data. The approach uses solar heat flux distribution images generated through ray-tracing simulations at multiple sun positions to predict surface deformations. Among the evaluated architectures, a U-Net model incorporating injected heliostat and sun position data achieved the best performance, with a validation mean square error (MSE) of 0.0207, representing an 8% improvement over the next-best, which was the convolutional autoencoder. A refinement step using the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is applied to further improve the prediction accuracy of local deformations, reducing the MSE by approximately 40%, albeit at an additional computational cost of around six hours per heliostat. The methodology is validated by applying a known real deformation to multiple heliostats at different positions within a representative solar tower field. For each heliostat, flux distribution images are obtained across different sun positions using ray-tracing software. These synthetic flux images then serve as the primary input for the DL model. Results show that the DL model can reconstruct the deformation pattern with high accuracy, (MSE loss of 0.015 for a real deformed heliostat) and near-instantaneous inference time. The proposed pipeline offers a scalable and efficient alternative to conventional optical calibration techniques. It is particularly suited for field-wide deployment in Concentrating Solar Power (CSP) systems, provided certain operational conditions are met, including clear-sky conditions, single-facet heliostat configurations, and minimal mirror soiling.