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Wind Energy Science The interactive open-access journal of the European Academy of Wind Energy
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https://doi.org/10.5194/wes-2020-55
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-2020-55
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  13 Mar 2020

13 Mar 2020

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A revised version of this preprint was accepted for the journal WES and is expected to appear here in due course.

Augmented Kalman filter with a reduced mechanical model to estimate tower loads on an onshore wind turbine: a digital twin concept

Emmanuel Branlard1, Dylan Giardina1, and Cameron S. D. Brown2 Emmanuel Branlard et al.
  • 1National Renewable Energy Laboratory, Golden, CO, USA
  • 2Ørsted, Nesa Allé 1, 2820 Gentofte, Denmark

Abstract. The paper 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 several fold 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. The mechanical model is built using a Rayleigh-Ritz approach and a set of joint coordinates. The paper presents a general method and illustrates it using a 2 degrees of freedom 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 forwards are discussed.

Emmanuel Branlard et al.

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Emmanuel Branlard et al.

Emmanuel Branlard et al.

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Latest update: 13 Aug 2020
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Short summary
The paper 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 several fold 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.
The paper presents an application of the Kalman filtering technique to estimate loads on a wind...
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