Real-Time Image Enhanced Data-Driven Digital Twin (Real-TImE 3DT) for Flux Density Measurements

Concentrated solar power (CSP) plants are considered one of the most attractive renewable energy producers (used for green fuel and electric power). Consequently, it is essential to enhance the different elements of their power cycles, especially the central receiver and the heliostat field. To achieve this, flux density measurement (FDM) is highly recommended. In this work, a novel methodology for FDM is presented, based on the usage of real-time data-driven simulation models in parallel with the traditional camera methods for the real-time enhancement of the results. In order to improve the output, a graph neural network is used, giving as a result a Real-Time Image-Enhanced Data-Driven Digital Twin (Real-TImE 3DT). All the data are obtained from a pre-existing data platform, where the signals from the sensors of the power plant are logged and stored. This way, it is possible to operate the model manually, or to let it work automatically with these sensors' outputs. Latencies smaller than 10 seconds are achieved and the results from the digital twin showed coherence, easy handling and great inter-operability with the neural network enhancement. On the AI-enhancement’s side, the suppression of up-to 80% of the inaccuracies can be expected under parametrically semi-controlled conditions. Further work is being performed in Solar Tower Jülich (STJ) in order to contrast these results against real experimental measurements.