Experimental validation of virtual torque sensing for wind turbine
gearboxes based on strain measurements
Abstract
In efforts to reduce the operation and maintenance cost of wind
turbines, there is an increasing interest to monitor key turbine
quantities such as the torque load on the gearbox. Monitoring the torque
paves the way for the calculation of remaining useful lifetime, leading
to cost reductions through improved reliability and maintenance
planning. In order to avoid expensive direct torque sensors,
this paper investigates the potential of virtual torque sensing, a
technique based on 3 basic components: firstly, a set of
non-intrusive sensors installed on the gearbox. Three groups of strain
gauges on the gearbox as well an angular encoder are considered in this
paper. Secondly, a physics-based model, capable of predicting the
response of aforementioned sensors. These models are constructed with a
purposeful balance between accuracy and computational cost. Model
validation and updating are performed to ensure efficient and accurate
prediction of the sensor output. Finally, an Augmented Extended Kalman
Filter (AEKF) is used to combine the measured response with predictions
from the model to infer the gearbox input torque. Since a key factor
determining the performance of the AEKF is the tuning of the AEKF
covariance matrices, multiple methods are introduced to systematically
tune the covariance matrices. Experimental validation results
show that the virtual torque sensor can detect the load torque with a
Normalized Mean Absolute Error (NMAE) between 3 .41%
to 7 .47% , depending on the sensor set. The
influence of the amount of sensors used and the tuning method are also
investigated.
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