Articles | Volume 5, issue 3
https://doi.org/10.5194/wes-5-1155-2020
© Author(s) 2020. 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-5-1155-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
04 Sep 2020
Research article |
| 04 Sep 2020
| 04 Sep 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
Emmanuel Branlard
CORRESPONDING AUTHOR
National Renewable Energy Laboratory, Golden, CO 80401, USA
Dylan Giardina
National Renewable Energy Laboratory, Golden, CO 80401, USA
Cameron S. D. Brown
Ørsted, Nesa Allé 1, 2820 Gentofte, Denmark
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Cited
26 citations as recorded by crossref.
- Joint parameter-input estimation for digital twinning of the Block Island wind turbine using output-only measurements M. Song et al. 10.1016/j.ymssp.2023.110425
- Virtual sensing of wind turbine hub loads and drivetrain fatigue damage F. Mehlan et al. 10.1007/s10010-023-00627-0
- A wind turbine digital shadow with tower and blade degrees of freedom - Preliminary results and comparison with a simple tower fore-aft model H. Hoghooghi et al. 10.1088/1742-6596/2767/3/032026
- Architecting a digital twin for wind turbine rotor blade aerodynamic monitoring Y. Marykovskiy et al. 10.3389/fenrg.2024.1428387
- Multiple Model Adaptive Estimation of the Aerodynamic Torque for the Control of Variable Speed Wind Turbines D. Bourlis 10.1109/TEC.2021.3090101
- An Overview of Digital Twin Concept for Key Components of Renewable Energy Systems Q. Li & Y. He 10.31875/2409-9694.2021.08.4
- Digital Twin Based Virtual Sensor for Online Fatigue Damage Monitoring in Offshore Wind Turbine Drivetrains F. Mehlan et al. 10.1115/1.4055551
- Strain Virtual Sensing for Structural Health Monitoring under Variable Loads B. Mora et al. 10.3390/s23104706
- Analysis and design of an adaptive turbulence-based controller for wind turbines L. Dong et al. 10.1016/j.renene.2021.06.080
- Digital twin technology for wind turbine towers based on joint load–response estimation: A laboratory experimental study Z. Zhu et al. 10.1016/j.apenergy.2023.121953
- Wind turbine load optimization control and verification based on wind speed estimator with time series broad learning system method D. Fu et al. 10.1049/cth2.12635
- Recursive Bayesian estimation of wind load on a monopile-supported offshore wind turbine using output-only measurements A. Mehrjoo et al. 10.1016/j.ymssp.2024.112183
- Data-driven virtual sensor for online loads estimation of drivetrain of wind turbines O. Kamel et al. 10.1007/s10010-023-00615-4
- Digital Twins for the Future Power System: An Overview and a Future Perspective Z. Song et al. 10.3390/su15065259
- Identification of Vibration Modes in Floating Offshore Wind Turbines M. Serrano-Antoñanazas et al. 10.3390/jmse11101893
- A digital twin solution for floating offshore wind turbines validated using a full-scale prototype E. Branlard et al. 10.5194/wes-9-1-2024
- A symbolic framework to obtain mid-fidelity models of flexible multibody systems with application to horizontal-axis wind turbines E. Branlard & J. Geisler 10.5194/wes-7-2351-2022
- Grand challenges in the digitalisation of wind energy A. Clifton et al. 10.5194/wes-8-947-2023
- Enabling self calibration for the wind turbine digital twin: a blending-function learning algorithm Y. Liu et al. 10.1088/1742-6596/2767/3/032035
- Digital Twin Technologies for Turbomachinery in a Life Cycle Perspective: A Review R. Xie et al. 10.3390/su13052495
- Modelling of wind turbine gear stages for Digital Twin and real-time virtual sensing using bond graphs F. Mehlan et al. 10.1088/1742-6596/2265/3/032065
- Virtual sensing in an onshore wind turbine tower using a Gaussian process latent force model J. Bilbao et al. 10.1017/dce.2022.38
- Industrial digital twins in offshore wind farms E. Ambarita et al. 10.1186/s42162-024-00306-6
- Bridge weigh-in-motion using augmented Kalman filter and model updating X. Lai et al. 10.1007/s13349-022-00559-3
- Precision blade deflection measurement system using wireless inertial sensor nodes E. Grundkötter & J. Melbert 10.1002/we.2680
- Estimation of fatigue crack initiation and growth in engineering product development using a digital twin approach W. Kim et al. 10.1016/j.ress.2022.108721
26 citations as recorded by crossref.
- Joint parameter-input estimation for digital twinning of the Block Island wind turbine using output-only measurements M. Song et al. 10.1016/j.ymssp.2023.110425
- Virtual sensing of wind turbine hub loads and drivetrain fatigue damage F. Mehlan et al. 10.1007/s10010-023-00627-0
- A wind turbine digital shadow with tower and blade degrees of freedom - Preliminary results and comparison with a simple tower fore-aft model H. Hoghooghi et al. 10.1088/1742-6596/2767/3/032026
- Architecting a digital twin for wind turbine rotor blade aerodynamic monitoring Y. Marykovskiy et al. 10.3389/fenrg.2024.1428387
- Multiple Model Adaptive Estimation of the Aerodynamic Torque for the Control of Variable Speed Wind Turbines D. Bourlis 10.1109/TEC.2021.3090101
- An Overview of Digital Twin Concept for Key Components of Renewable Energy Systems Q. Li & Y. He 10.31875/2409-9694.2021.08.4
- Digital Twin Based Virtual Sensor for Online Fatigue Damage Monitoring in Offshore Wind Turbine Drivetrains F. Mehlan et al. 10.1115/1.4055551
- Strain Virtual Sensing for Structural Health Monitoring under Variable Loads B. Mora et al. 10.3390/s23104706
- Analysis and design of an adaptive turbulence-based controller for wind turbines L. Dong et al. 10.1016/j.renene.2021.06.080
- Digital twin technology for wind turbine towers based on joint load–response estimation: A laboratory experimental study Z. Zhu et al. 10.1016/j.apenergy.2023.121953
- Wind turbine load optimization control and verification based on wind speed estimator with time series broad learning system method D. Fu et al. 10.1049/cth2.12635
- Recursive Bayesian estimation of wind load on a monopile-supported offshore wind turbine using output-only measurements A. Mehrjoo et al. 10.1016/j.ymssp.2024.112183
- Data-driven virtual sensor for online loads estimation of drivetrain of wind turbines O. Kamel et al. 10.1007/s10010-023-00615-4
- Digital Twins for the Future Power System: An Overview and a Future Perspective Z. Song et al. 10.3390/su15065259
- Identification of Vibration Modes in Floating Offshore Wind Turbines M. Serrano-Antoñanazas et al. 10.3390/jmse11101893
- A digital twin solution for floating offshore wind turbines validated using a full-scale prototype E. Branlard et al. 10.5194/wes-9-1-2024
- A symbolic framework to obtain mid-fidelity models of flexible multibody systems with application to horizontal-axis wind turbines E. Branlard & J. Geisler 10.5194/wes-7-2351-2022
- Grand challenges in the digitalisation of wind energy A. Clifton et al. 10.5194/wes-8-947-2023
- Enabling self calibration for the wind turbine digital twin: a blending-function learning algorithm Y. Liu et al. 10.1088/1742-6596/2767/3/032035
- Digital Twin Technologies for Turbomachinery in a Life Cycle Perspective: A Review R. Xie et al. 10.3390/su13052495
- Modelling of wind turbine gear stages for Digital Twin and real-time virtual sensing using bond graphs F. Mehlan et al. 10.1088/1742-6596/2265/3/032065
- Virtual sensing in an onshore wind turbine tower using a Gaussian process latent force model J. Bilbao et al. 10.1017/dce.2022.38
- Industrial digital twins in offshore wind farms E. Ambarita et al. 10.1186/s42162-024-00306-6
- Bridge weigh-in-motion using augmented Kalman filter and model updating X. Lai et al. 10.1007/s13349-022-00559-3
- Precision blade deflection measurement system using wireless inertial sensor nodes E. Grundkötter & J. Melbert 10.1002/we.2680
- Estimation of fatigue crack initiation and growth in engineering product development using a digital twin approach W. Kim et al. 10.1016/j.ress.2022.108721
Latest update: 13 Dec 2024
Short summary
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 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.
The paper presents an application of the Kalman filtering technique to estimate loads on a wind...
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