Pattern Mining for Learning Typical Turbine Response During Dynamic Wind Turbine Events

Maintenance costs are a main cost driver for offshore wind energy. Prediction of failure and particularly failure understanding can help to bring these costs down significantly. Since the wind turbine is subjected to a large number of dynamic events it is important to fully understand the turbine response to these events. Pattern mining has been used successfully for different applications. We believe it to have large potential for understanding turbine behavior based on turbine status logs. These logs record all turbine actions and can be used as input for pattern mining algorithms. This paper proposes the use of a multi-level pattern mining approach in order to minimize the number of uninteresting patterns and facilitate response understanding. The paper mainly focuses on the extraction of patterns and association rules linked to certain alarms and how they can be annotated for further use in the multi-level pattern mining approach. Several years of wind turbine data is used. The use of the approach is illustrated by detecting the characteristic pattern linked to turbine response to an Extremely High Wind Speed Alert.