Cost-Efficient Prognostics Framework for Heliostat Drive Units
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Abstract
In concentrating solar power tower plants, heliostat drive units are critical components, as they control the precise two-axis alignment of thousands of mirrors, so-called heliostats, that focus incoming solar radiation onto a central receiver. Due to the large field sizes and the corresponding long heliostat–tower distances, even small angular deviations in the milliradian range (1 mrad ≈ 0.057 degree) result in significant focal point displacements at the receiver. Consequently, the reliable operation of heliostat drive units is essential for the stable and safe operation of solar tower plants.
However, existing research on heliostat operation and maintenance (O&M) predominantly focuses on optical aspects such as mirror soiling (i.e., the accumulation of dust and sand on reflective surfaces), mirror calibration and tracking algorithms, and the influence of wind loads on heliostat performance and structural behaviour. In contrast, the operational health of heliostat drive units remains largely unexplored. To close this research gap, this study presents a cost-efficient prognostics framework for the recording and the subsequent maintenanceoriented analysis of operational data of the heliostat drive units. For this purpose, an extensive measurement campaign is conducted in the heliostat field of the DLR solar tower research facility in Juelich, Germany. In addition to the existing industrial-grade reference sensors and data loggers at the solar tower research facility in Juelich, this study develops a low-cost Arduino-based data acquisition system and performs a comparison between those conventional and cost-efficient monitoring architectures.
The results demonstrate that the recorded measurement data provide a robust foundation for monitoring the heliostat drive units. The proposed prognostics framework is experimentally validated and successfully applied to selected heliostats in the field: first, this shows that sufficiently precise measurements, adequate sampling rates, and straightforward installation and handling can be achieved for field deployment. Second, it demonstrates the capability to identify and analyse real-world operational anomalies. And third, it enables reliable and costefficient monitoring significantly reducing the barriers to scalable prognostics and health management (PHM) deployment. Although developed for heliostat drive units, the diagnostics and prognostics methodology presented in this work may be transferable to a wide range of electromechanical systems in industrial PHM applications, as it integrates sensors and instrumentation with anomaly detection, and supports conditionbased and predictive maintenance strategies.
How to Cite
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CST, Heliostat Field, Heliostat Drive Units, Prognostics Framework, heliostat failure, arduino, data analysis, outdoor measurement campaign
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