Data-driven estimation of blade icing risk in wind turbines.

The formation of ice on the blades of wind turbines can severely affect their power production, lead to a degradation of the assets and even cause safety hazards. Predicting blade icing allows mitigating or preventing altogether its impact on the turbines and their performance by activating blade heating mechanisms. A novel data-driven approach is proposed which estimates a turbine-specific icing risk between 0 and 1 using only meteorological historical and forecasted data. The method is based on the creation of a repository of meteorological profiles characteristics of icing, to which all other profiles are compared in order to compute a similarity score, then converted into an icing risk. The approach is robust against icing sample imbalance in the dataset and thus performant even in locations where icing incidence is extremely low. The icing risk provides wind farm operators with a meaningful indicator, allowing for more flexibility, a better view of the onset of ice formation, and a measure of the severity of an upcoming icing event. The validation is performed on a dataset of 7 turbines belonging to the same wind farm.