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

Branlard, Emmanuel; Giardina, Dylan; Brown, Cameron D.

doi:10.5194/wes-5-1155-2020

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

Journal Article · Fri Sep 04 00:00:00 EDT 2020 · Wind Energy Science (Online)

DOI:https://doi.org/10.5194/wes-5-1155-2020· OSTI ID:1665835

^[1]; Giardina, Dylan ^[1]; Brown, Cameron D. ^[2]

National Renewable Energy Lab. (NREL), Golden, CO (United States)
Ørsted, Gentofte (Denmark)

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.

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Research Organization:: National Renewable Energy Laboratory (NREL), Golden, CO (United States)

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Wind Energy Technologies Office

Grant/Contract Number:: AC36-08GO28308

OSTI ID:: 1665835

Report Number(s):: NREL/JA-5000-76885; MainId:10529; UUID:2b314b85-f3ec-474f-8977-7e9510c9c6ac; MainAdminID:17442

Journal Information:: Wind Energy Science (Online), Vol. 5, Issue 3; ISSN 2366-7451

Publisher:: European Wind Energy Association - CopernicusCopyright Statement

Country of Publication:: United States

Language:: English

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