Abstract
Abstract. This article presents an application of the Kalman filtering technique to estimate loads on a wind turbine. The approach combines a mechanical model and a set of measurements to estimate signals that are not available in the measurements, such as wind speed, thrust, tower position, and tower loads. The model is severalfold faster than real time and is intended to be run online, for instance, to evaluate real-time fatigue life consumption of a field turbine using a digital twin, perform condition monitoring, or assess loads for dedicated control strategies. The mechanical model is built using a Rayleigh–Ritz approach and a set of joint coordinates. We present a general method and illustrate it using a 2-degrees-of-freedom (DOF) model of a wind turbine and using rotor speed, generator torque, pitch, and tower-top acceleration as measurement signals. The different components of the model are tested individually. The overall method is evaluated by computing the errors in estimated tower-bottom-equivalent moment from a set of simulations. From this preliminary study, it appears that the tower-bottom-equivalent moment is obtained with about 10 % accuracy. The limitation of the model and the required steps forward are discussed.
Highlights
Wind turbines are designed and optimized for a given site or class definition using both numerical tools and a statistical assessment of the environmental conditions the turbine will experience
The methodology presented in this article uses an augmented Kalman filter (Lourens et al, 2012) to estimate loads on the wind turbine based on measurement signals commonly available in the nacelle
We presented a general approach using Kalman filtering to estimate loads on a wind turbine, combining a mechanical model and a set of readily available measurements
Summary
Wind turbines are designed and optimized for a given site or class definition using both numerical tools and a statistical assessment of the environmental conditions the turbine will experience. General approaches use Kalman filtering in combination with a model of the full wind turbine dynamics These approaches were used for wind speed estimation and load alleviation via individual pitch control (Selvam et al, 2009; Bottasso and Croce, 2009) and for online estimation of mechanical loads (Bossanyi, 2003). The methodology presented in this article uses an augmented Kalman filter (Lourens et al, 2012) to estimate loads on the wind turbine based on measurement signals commonly available in the nacelle. The generator torque, rotor speed, and tower-top accelerations are used as measurements and combined with the numerical model within an augmented Kalman filter.
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