AI helps Finnish team optimise wind turbine material

Degradation of turbine blades is an industry-wide problem and their continual replacement costs the wind energy sector a sizeable chunk of its revenue. VTT’s antiAGE project used virtual testing and machine learning to cycle through the almost limitless combinations of different materials that could be used in various wind turbine components, helping to minimise the erosion of turbine blades.

“The blade material erodes due to the effect of rain, hailstones and sand dust, which significantly reduces the service life of wind turbines,” said VTT principal scientist Anssi Laukkanen. “Accelerated replacement of turbines becomes expensive: up to 2–4 per cent of the value of all wind-generated power is lost as a result of this problem.

“It is a question of a classic problem within this particular industry that costs billions of euros and brings additional costs to all wind energy. As wind turbine sizes increase and wind farms are placed out on the sea in increasingly demanding conditions, the significance of the problem becomes emphasised.”