Using MEMS Acceleration Sensors for Monitoring Blade Tip Movement of Wind Turbines

Monitoring blade tip movement of wind turbines is highly relevant for identifying imbalances and increased alternating loads. As sensor placement at blade tips is generally challenging, a light-weight low-profile MEMS acceleration sensor is used in this paper. A simulation of acceleration signals under wind effects such as wind shear, yaw and tower shadow was used to extract significant features, which were then applied to real world data. The dimensions of features in the simulation as well as in real world data were in alignment which proves the concept of the simulation, together with features found to correlate with the turbine's frequency. Findings strongly indicate that the approach is very promising for monitoring blade tip movement and identifying alternating loads.