POD-Based Sparse Stochastic Estimation of Wind Turbine Blade Vibrations
Fuente:
arXiv
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866916685713244160 |
|---|---|
| author | Schena, Lorenzo Munters, Wim Helsen, Jan Mendez, Miguel A. |
| author_facet | Schena, Lorenzo Munters, Wim Helsen, Jan Mendez, Miguel A. |
| contents | This study presents a framework for estimating the full vibrational state of wind turbine blades from sparse deflection measurements. The identification is performed in a reduced-order space obtained from a Proper Orthogonal Decomposition (POD) of high-fidelity aeroelastic simulations based on Geometrically Exact Beam Theory (GEBT). In this space, a Reduced Order Model (ROM) is constructed using a linear stochastic estimator, and further enhanced through Kalman fusion with a quasi-steady model of azimuthal dynamics driven by measured wind speed. The performance of the proposed estimator is assessed in a synthetic environment replicating turbulent inflow and measurement noise over a wide range of operating conditions. Results demonstrate the method's ability to accurately reconstruct three-dimensional deformations and accelerations using noisy displacement and acceleration measurements at only four spatial locations. These findings highlight the potential of the proposed framework for real-time blade monitoring, optimal sensor placement, and active load control in wind turbine systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_08505 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | POD-Based Sparse Stochastic Estimation of Wind Turbine Blade Vibrations Schena, Lorenzo Munters, Wim Helsen, Jan Mendez, Miguel A. Systems and Control Classical Physics This study presents a framework for estimating the full vibrational state of wind turbine blades from sparse deflection measurements. The identification is performed in a reduced-order space obtained from a Proper Orthogonal Decomposition (POD) of high-fidelity aeroelastic simulations based on Geometrically Exact Beam Theory (GEBT). In this space, a Reduced Order Model (ROM) is constructed using a linear stochastic estimator, and further enhanced through Kalman fusion with a quasi-steady model of azimuthal dynamics driven by measured wind speed. The performance of the proposed estimator is assessed in a synthetic environment replicating turbulent inflow and measurement noise over a wide range of operating conditions. Results demonstrate the method's ability to accurately reconstruct three-dimensional deformations and accelerations using noisy displacement and acceleration measurements at only four spatial locations. These findings highlight the potential of the proposed framework for real-time blade monitoring, optimal sensor placement, and active load control in wind turbine systems. |
| title | POD-Based Sparse Stochastic Estimation of Wind Turbine Blade Vibrations |
| topic | Systems and Control Classical Physics |
| url | https://arxiv.org/abs/2504.08505 |