Lio, W.H., Jones, B.L. and Rossiter, J.A. orcid.org/0000-0002-1336-0633 (2017) Analysis and design of tower motion estimator for wind turbines. In: 2016 IEEE International Conference on Renewable Energy Research and Applications (ICRERA). ICRERA 2016, 20-23 Nov 2016, Birmingham, UK. IEEE ISBN 978-1-5090-3388-1
Abstract
The use of blade individual pitch control (IPC) provides a means of alleviating the harmful turbine loads that arise from the uneven and unsteady forcing from the oncoming wind. Such IPC algorithms, which mainly target the blade loads at specific frequencies, are designed to avoid excitations of other turbine dynamics such as the tower. Nonetheless, these blade and tower interactions can be exploited to estimate the tower movement from the blade load sensors. As a consequence, the aim of this paper is to analyse the observability properties of the blade and tower model and based on these insights, an estimator design is proposed to reconstruct the tower motion from the measurements of the flap-wise blade loads, that are typically available to the IPC. The proposed estimation strategy offers many immediate benefits, for example, the estimator obviates the need for hardware sensor redundancy, and the estimated signals can be used for control or fault monitoring purposes. We further show results obtained from high-fidelity turbine simulations to demonstrate the performance of the proposed estimator.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2016 IEEE. This is an author produced version of a paper subsequently published in Renewable Energy Research and Applications (ICRERA), 2016 IEEE International Conference on. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Estimator; Observer; Kalman filter; Individual blade-pitch control. |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 21 Mar 2017 11:30 |
Last Modified: | 19 Dec 2022 13:35 |
Published Version: | https://doi.org/10.1109/ICRERA.2016.7884416 |
Status: | Published |
Publisher: | IEEE |
Refereed: | Yes |
Identification Number: | 10.1109/ICRERA.2016.7884416 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:113960 |