A wind turbine digital shadow for complex inflow conditions

Abstract. We present a digital shadow Kalman filtering approach based on the direct linearization of a multibody aeroservoelastic model of a wind turbine. In contrast to approaches based on ad hoc models, the reuse of existing trusted models reduces development time and duplication of effort, leverages resources invested in tuning and validation, and eventually increases confidence in the results.

This approach has already been pursuded by others, but it is here improved with respect to several main aspects of the formulation. To extend the applicability to non-symmetric, waked, and yaw-misaligned conditions, the filter-internal model – in addition to the tower fore-aft and rotor rotational dynamics – now also includes the tower side-side and the flapwise and edgewise degrees of freedom of the rotor blades. To make the model aware of the inflow conditions at the rotor disk, inflow estimators are used to detect in real time during operation rotor-equivalent values of the wind speed, vertical shear, horizontal shear (on account of waked conditions), and yaw misalignment (in support of wake-steering control). These inflow parameters are used to schedule the filter-internal model, adapting its behavior to the current conditions experienced by the turbine. Furthermore, the filter-internal white-box model is augmented with data-driven corrections to improve its predictive accuracy. Two approaches are explored for the correction of the model: a bias correction method that attempts to improve both states and outputs, and a neural-based one that only corrects the outputs but not the states.

The proposed digital shadow is demonstrated first in a simulation environment, considering clean freestream, waked, and wake-steering conditions, and then using a field dataset collected on an instrumented turbine. To further validate its performance under complex inflow conditions, additional field data evaluations are conducted, including cases of extreme vertical shear, waked, and wake-steering conditions. Remarkably, the quality of the estimates of the damage equivalent loads for the field case is similar to the simulation case, even without any specific correction of the filter-internal model. However, after applying correction techniques, the quality of the estimates improves drastically, yielding errors in the damage equivalent load estimates of only a few percentage points.