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Title: Augmented Kalman filter with a reduced mechanical model to estimate tower loads on a land-based wind turbine: a step towards digital-twin simulations

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.

Authors:
ORCiD logo [1];  [1];  [2]
  1. National Renewable Energy Lab. (NREL), Golden, CO (United States)
  2. Ørsted, Gentofte (Denmark)
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Wind Energy Technologies Office
OSTI Identifier:
1665835
Report Number(s):
NREL/JA-5000-76885
Journal ID: ISSN 2366-7451; MainId:10529;UUID:2b314b85-f3ec-474f-8977-7e9510c9c6ac;MainAdminID:17442
Grant/Contract Number:  
AC36-08GO28308
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Wind Energy Science (Online)
Additional Journal Information:
Journal Volume: 5; Journal Issue: 3; Journal ID: ISSN 2366-7451
Publisher:
European Wind Energy Association - Copernicus
Country of Publication:
United States
Language:
English
Subject:
17 WIND ENERGY; fatigue; Kalman filter; load estimation

Citation Formats

Branlard, Emmanuel, Giardina, Dylan, and Brown, Cameron D. Augmented Kalman filter with a reduced mechanical model to estimate tower loads on a land-based wind turbine: a step towards digital-twin simulations. United States: N. p., 2020. Web. doi:10.5194/wes-5-1155-2020.
Branlard, Emmanuel, Giardina, Dylan, & Brown, Cameron D. Augmented Kalman filter with a reduced mechanical model to estimate tower loads on a land-based wind turbine: a step towards digital-twin simulations. United States. https://doi.org/10.5194/wes-5-1155-2020
Branlard, Emmanuel, Giardina, Dylan, and Brown, Cameron D. 2020. "Augmented Kalman filter with a reduced mechanical model to estimate tower loads on a land-based wind turbine: a step towards digital-twin simulations". United States. https://doi.org/10.5194/wes-5-1155-2020. https://www.osti.gov/servlets/purl/1665835.
@article{osti_1665835,
title = {Augmented Kalman filter with a reduced mechanical model to estimate tower loads on a land-based wind turbine: a step towards digital-twin simulations},
author = {Branlard, Emmanuel and Giardina, Dylan and Brown, Cameron D.},
abstractNote = {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.},
doi = {10.5194/wes-5-1155-2020},
url = {https://www.osti.gov/biblio/1665835}, journal = {Wind Energy Science (Online)},
issn = {2366-7451},
number = 3,
volume = 5,
place = {United States},
year = {2020},
month = {9}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record

Figures / Tables:

Figure 1 Figure 1: Main components of the model: wind turbine measurements and a turbine model are combined to estimate tower loads. A wind speed estimator and a Kalman filter algorithm are used in the estimation. Turbine model dependencies are framed in blue.

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Figures/Tables have been extracted from DOE-funded journal article accepted manuscripts.