A Data-Mining Approach to Monitoring Wind Turbines

The rapid expansion of wind farms has generated interest in operations and maintenance. An operating wind turbine undergoes various state changes, including transformation from a normal to a fault mode. Condition-based maintenance tools are needed to identify potential faults in the system. The prediction of turbine fault modes is of particular interest. In this research, data-mining algorithms are employed to construct prediction models for wind turbine faults. A three-stage prediction process is followed: 1) prediction of a fault of any kind; 2) prediction of specific faults of the system; and 3) identification on unseen faults. A comparative analysis of various data-mining algorithms is reported based on the data collected at a large wind farm. Random forest algorithm models provided the best accuracy among all algorithms tested. The robustness of the predictive model is validated for faults that have occurred at turbines with previously unseen data. The research results discussed in this paper have been derived from data collected at 17 wind turbines.