Articles | Volume 10, issue 1
https://doi.org/10.5194/wes-10-193-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-10-193-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
22 Jan 2025
Research article |
| 22 Jan 2025
| 22 Jan 2025
Dynamic displacement measurement of a wind turbine tower using accelerometers: tilt error compensation and validation
Clemens Jonscher
CORRESPONDING AUTHOR
ForWind, Institute of Structural Analysis, Leibniz University Hannover, Appelstraße 9A, 30167 Hanover, Germany
Paula Helming
Bremen Institute for Metrology, Automation and Quality Science, University of Bremen, Linzer Str. 13, 28359 Bremen, Germany
David Märtins
ForWind, Institute of Structural Analysis, Leibniz University Hannover, Appelstraße 9A, 30167 Hanover, Germany
Andreas Fischer
Bremen Institute for Metrology, Automation and Quality Science, University of Bremen, Linzer Str. 13, 28359 Bremen, Germany
David Bonilla
ForWind, Institute of Structural Analysis, Leibniz University Hannover, Appelstraße 9A, 30167 Hanover, Germany
Benedikt Hofmeister
ForWind, Institute of Structural Analysis, Leibniz University Hannover, Appelstraße 9A, 30167 Hanover, Germany
Tanja Grießmann
ForWind, Institute of Structural Analysis, Leibniz University Hannover, Appelstraße 9A, 30167 Hanover, Germany
Raimund Rolfes
ForWind, Institute of Structural Analysis, Leibniz University Hannover, Appelstraße 9A, 30167 Hanover, Germany
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Cited articles
Artese, S. and Nico, G.: TLS and GB-RAR Measurements of Vibration Frequencies and Oscillation Amplitudes of Tall Structures: An Application to Wind Towers, Appl. Sci., 10, 2237, https://doi.org/10.3390/app10072237, 2020. a, b
Baqersad, J., Poozesh, P., Niezrecki, C., and Avitabile, P.: Photogrammetry and optical methods in structural dynamics–A review, Mech. Syst. Sig. Process., 86, 17–34, https://doi.org/10.1016/j.ymssp.2016.02.011, 2017. a, b
Berg, G. V. and Housner, G. W.: Integrated velocity and displacement of strong earthquake ground motion, Bull. Seismol. Soc. Am., 51, 175–189, https://doi.org/10.1785/BSSA0510020175, 1961. a
Boroschek, R. L. and Legrand, D.: Tilt motion effects on the double-time integration of linear accelerometers: an experimental approach, Bull. Seismol. Soc. Am., 96, 2072–2089, https://doi.org/10.1785/0120050167, 2006. a, b
Brandt, A. and Brincker, R.: Integrating time signals in frequency domain – Comparison with time domain integration, Measurement, 58, 511–519, https://doi.org/10.1016/j.measurement.2014.09.004, 2014. a
Camargo, E. A., Ulfkjaer, J. P., Brincker, R., Nøergaard, J., and Gadegaard, S. S.: Operational Modal Analysis and Finite-Element Model Updating of Pilot Concrete Wind Turbine Tower, J. Struct. Eng., 145, 05018003, https://doi.org/10.1061/(ASCE)ST.1943-541X.0002242, 2019. a
Civera, M. and Surace, C.: Non-destructive techniques for the condition and structural health monitoring of wind turbines: A literature review of the last 20 years, Sensors, 22, 1627, https://doi.org/10.3390/s22041627, 2022. a
Devriendt, C., Magalhães, F., Weijtjens, W., de Sitter, G., Cunha, Á., and Guillaume, P.: Structural health monitoring of offshore wind turbines using automated operational modal analysis, Struct. Health Monit., 13, 644–659, https://doi.org/10.1177/1475921714556568, 2014. a
Dilek, A. U., Oguz, A. D., Satis, F., Gokdel, Y. D., and Ozbek, M.: Condition monitoring of wind turbine blades and tower via an automated laser scanning system, Eng. Struct., 189, 25–34, https://doi.org/10.1016/j.engstruct.2019.03.065, 2019. a, b
EN 1993-1-9:2005+AC:2009: Eurocode 3: Design of steel structures - Part 1–9: Fatigue, DIN Media, https://doi.org/10.31030/1722660, 2005. a
Gebhardt, C. G., Hofmeister, B., Hente, C., and Rolfes, R.: Nonlinear dynamics of slender structures: a new object-oriented framework, Comput. Mech., 63, 219–252, https://doi.org/10.1007/s00466-018-1592-7, 2019. a
Gebhardt, C. G., Romero, I., and Rolfes, R.: A new conservative/dissipative time integration scheme for nonlinear mechanical systems, Comput. Mech., 65, 405–427, https://doi.org/10.1007/s00466-019-01775-3, 2020. a
Gustafsson, F.: Determining the initial states in forward-backward filtering, IEEE T. Signal Process., 44, 988–992, https://doi.org/10.1109/78.492552, 1996. a
Häckell, M. W. and Rolfes, R.: Monitoring a 5 MW offshore wind energy converter – Condition parameters and triangulation based extraction of modal parameters, Mech. Syst. Sig. Process., 40, 322–343, https://doi.org/10.1016/j.ymssp.2013.04.004, 2013. a
Helming, P., Intemann, A., Webersinke, K.-P., von Freyberg, A., Sorg, M., and Fischer, A.: Assessing the rotor blade deformation and tower–blade tip clearance of a 3.4 MW wind turbine with terrestrial laser scanning, Wind Energ. Sci., 8, 421–431, https://doi.org/10.5194/wes-8-421-2023, 2023. a, b, c
Jonscher, C., Hofmeister, B., Grießmann, T., and Rolfes, R.: Optimierte Messkette zur Erfassung tieffrequenter Strukturdynamik, in: Baudynamik 2022, VDI Verlag, 439–450, ISBN 9783181023792, https://doi.org/10.51202/9783181023792-439, 2022a. a, b, c
Jonscher, C., Möller, S., Liesecke, L., Schuster, D., Hofmeister, B., Grießmann, T., and Rolfes, R.: Identification Uncertainties of Bending Modes of an Onshore Wind Turbine for Vibration-Based Monitoring, Struct. Control Health Monit., 2024, 3280697, https://doi.org/10.1155/2024/3280697, 2024. a, b, c
Łuczak, S.: Guidelines for tilt measurements realized by MEMS accelerometers, Int. J. Precis. Eng. Manufact., 15, 489–496, https://doi.org/10.1007/s12541-014-0362-5, 2014. a
Maes, K., Iliopoulos, A., Weijtjens, W., Devriendt, C., and Lombaert, G.: Dynamic strain estimation for fatigue assessment of an offshore monopile wind turbine using filtering and modal expansion algorithms, Mech. Syst. Sig. Process., 76-77, 592–611, https://doi.org/10.1016/j.ymssp.2016.01.004, 2016. a, b
Oliveira, G., Magalhães, F., Cunha, Á., and Caetano, E.: Vibration-based damage detection in a wind turbine using 1 year of data, Struct. Control Health Monit., 25, e2238, https://doi.org/10.1002/stc.2238, 2018. a
Ozbek, M., Meng, F., and Rixen, D. J.: Challenges in testing and monitoring the in-operation vibration characteristics of wind turbines, Mech. Syst. Sig. Process., 41, 649–666, https://doi.org/10.1016/j.ymssp.2013.07.023, 2013. a, b
Park, K.-T., Kim, S.-H., Park, H.-S., and Lee, K.-W.: The determination of bridge displacement using measured acceleration, Eng. Struct., 27, 371–378, https://doi.org/10.1016/j.engstruct.2004.10.013, 2005. a
Stiros, S. C.: Errors in velocities and displacements deduced from accelerographs: An approach based on the theory of error propagation, Soil Dynam. Earthq. Eng., 28, 415–420, https://doi.org/10.1016/j.soildyn.2007.07.004, 2008. a
Tarpø, M., Nabuco, B., Boroschek, R., and Brincker, R.: Tilt errors of translational accelerometers attached to dynamic systems with tilt motion caused by the system response, J. Sound Vibrat., 498, 115967, https://doi.org/10.1016/j.jsv.2021.115967, 2021. a, b, c
Tarpø, M., Amador, S., Katsanos, E., Skog, M., Gjødvad, J., and Brincker, R.: Data–driven virtual sensing and dynamic strain estimation for fatigue analysis of offshore wind turbine using principal component analysis, Wind Energy, 25, 505–516, https://doi.org/10.1002/we.2683, 2022. a
VDI 3834: Measurement and Evaluation of the Mechanical Vibration of Wind Turbines and Their Components – Wind Turbines with Gearbox, https://www.vdi.de/en/home/vdi-standards/details/vdi-3834-blatt-1-measurement-and-evaluation-of-the-mechanical (last access: 7 October 2024), 2015. a
Zieger, T., Nagel, S., Lutzmann, P., Kaufmann, I., Ritter, J., Ummenhofer, T., Knödel, P., and Fischer, P.: Simultaneous identification of wind turbine vibrations by using seismic data, elastic modeling and laser Doppler vibrometry, Wind Energy, 23, 1145–1153, https://doi.org/10.1002/we.2479, 2020. a, b
Short summary
This study investigates dynamic displacement estimation using double-time-integrated acceleration signals for future application in load monitoring based on accelerometers. To estimate displacements without amplitude distortion, a tilt error compensation method for low-frequency vibrations of tower structures using the static bending line without the need for additional sensors is presented. The method is validated using a full-scale onshore wind turbine tower and a terrestrial laser scanner.
This study investigates dynamic displacement estimation using double-time-integrated...
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