Wind turbine ice assessment through inductive transfer learning

Wind power is one of the majority sustainable power sources in the world. However, wind turbine is facing the highly possible blade frozen event on the blade. While numerous ice detection methods have been reported, the detection with the data acquired by the SCADA (supervisory control and data acquisition) system has not been investigated yet. Thus, in this study we propose a wind turbine ice assessment framework with SCADA data by using an ITL (inductive transfer learning) approach. We applied different machine learning methods to the ice detection problem, in which the fully-connected neural networks (FNN) outperformed the others for accuracy and stability. The experimental results demonstrate that the knowledge gained from one wind turbine can be transferred to others, where insufficient labeled data are available to build a prediction model.