Potential and Limitations of Anomaly Detection via Tower-Radar Monitoring of Wind Turbine Blades in Regular Operation with Convolutional Networks

Abstract

The authors have already presented a first analysis of a field study on radar-based structural health monitoring of rotor blades on two operational wind turbines. This analysis will now be extended with measurements from a third operational wind turbine used as a case study for structural monitoring of individual rotor blades over different time intervals and blade modifications. More precisely, a sensor box with a 35 GHz FMCW radar and a highspeed camera was positioned at 90m height on the turbine tower, collecting radar data for structural information and videos for automatic ground truth labelling. After two months of collecting data, each individual rotor blade of that turbine was differently modified and measured again. The radar-facing surface of one rotor blade was partially coated with a microwave absorber foil, while an equivalent foil was applied to the backside of the second rotor blade. The third rotor blade was intentionally left unchanged for the sake of comparability. This study investigates potential limitations like general dataset complexity in radar-based anomaly detection for operational wind turbines. Furthermore, the discriminative ability of a convolutional neural network for classifying moving rotor blades is discussed when encountering feature drift through changing environmental and operational conditions, i.e. metadata like nacelle orientation, rotor blade pitch and rotation speed.