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

Autor: Emmanuel Branlard, Dylan Giardina, Cameron S. D. Brown
Jazyk: angličtina
Rok vydání: 2020
Předmět:
ISSN: 2366-7451
Popis: 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.
Databáze: OpenAIRE