Posters

Abstract

The construction and operation of wind turbines in cold climates face significant challenges due to harsh weather conditions. One major difficulty is the icing of rotor blades, which increases mechanical loads, reduces aerodynamic performance, and can lead to shutdowns. Falling or thrown ice further presents safety risks to personnel and property around the turbines. Accurate forecasting of icing events during the planning phase is essential to determine suitable countermeasures such as ice detection systems or blade heating. Icing maps, derived from meteorological data, are crucial tools for assessing risks like power losses and ice-related hazards. However, current icing maps often rely on ground-level measurements, which do not adequately represent the conditions at the hub heights of modern wind turbines (80–200 meters). At these altitudes, temperature, humidity, and wind speed can significantly differ from ground level, resulting in a differing icing probability. This study aims to improve icing risk assessments and icing forecasts by integrating multiple data sources to enhance both detection and prediction accuracy. The developed method combines variables such as temperature, humidity, wind speed, and signal quality (e.g., carrier-to-noise ratio) from LiDAR measurements, reanalysis datasets, SCADA systems, and meteorological masts to determine the icing conditions at multiple heights above ground. We identified the most likely icing conditions at our validation sites in Germany and Poland as relative humidity above 95%, temperatures ranging from -4.4°C to 1°C, and wind speeds below 6 m/s. Based on these results, we developed a method to predict icing at hub height and estimate the corresponding uncertainties, given the quality and resolution of the underlying data. Our results enhance the safety and efficiency of wind energy production and provide more accurate energy yield assessments in cold climates. We aim to expand this work by predicting icing conditions using only reanalysis data, broadening its applicability.