wind turbine icing
PiB – Predictive, intelligent operational control for minimizing the risk of icing for wind energy turbines
Wind energy turbines and rotor blades, in particular, are not only exposed to specific structural loads, but also to extreme weather conditions. Depending on the location and, especially at lower temperatures and high humidity, there exists a danger of ice formations on the leading edge of the rotor blade. This can lead to the significant reduction of power and even to turbine damage, signifying a relevant factor for the efficiency of wind turbines as well as in the estimation of available capacities for grid operators.
Project partners research anti-icing system
In the context of the PiB research project, an intelligent operational management to reduce the risk of wind turbine icing is to be researched. The PiB project, initiated in January 2018, is coordinated by the University of Bremen and carried out together with partners energy & meteo systems, Spitzner engineers and the wpd windmanager. The ultimate goal of the cooperation project is to increase the actual availability of wind turbines and to make operations in difficult climates possible from an economic standpoint.
Development of an intelligent forecast
Aside from improved prediction models for analyzing meteorological data, the subject of the project centers on the development of data analysis methods and information acquisition based on data mining. Here, energy & meteo systems will contribute its experience in the areas of modeling and predictions of meteorological environmental conditions as well as in intelligent data analysis, particularly with regards to wind energy. As an added effect, the development of an extended predictive algorithm on the basis of meteorological data as well as mechanisms towards supporting the situational awareness of icing conditions serve the later, optimized icing prediction and recognition of ice formation risk. Our customers especially with wind farms in the USA and Scandinavia will be pleased.
The research project was completed in May 2021.