Wind turbines are the major driving force to produce renewable energy, but there is a strong need to reduce the costs of operation and maintenance. To detect anomalies of wind turbines, this paper proposes a method which uses data collected by the Supervisory Control And Data Acquisition (SCADA) system and is based upon building the normal behavior model of wind turbines. This is achieved by using supervised data and Gaussian mixture models with filtering SCADA data from the macroscopic point of view. The method is validated with SCADA data collected from actual 2-MW wind turbines. The result shows the potential of detecting anomalies and the effectiveness of filtering
conditions for building the model.
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