A Deep Learning Approach Towards Prediction of Faults in Wind Turbines

With the rising costs of conventional sources of energy, the world is moving towards sustainable energy sources including wind energy. Wind turbines consist of several electrical and mechanical components and experience an enormous amount of irregular loads, making their operational behaviour at times inconsistent. Operations and Maintenance (O&M) is a key factor in monitoring such inconsistent behaviour of the turbines in order to predict and prevent any incipient faults which may occur in the near future. Machine learning has been applied to the domain of wind energy over the last decade for analysing, diagnosing and predicting wind turbine faults. In particular, we follow the idea of modelling a turbine's performance as a power curve where any power outputs that fall off the curve can be seen as performance errors. Existing work using this idea has used data from a turbine's Supervisory Control & Acquisition (SCADA) system to filter and analyse fault & alarm data using regression techniques. In contrast to previous work, we explore how deep learning can be applied to fault prediction from open access meteorological data only.