Fast-varying Natural Frequencies and Damping Ratio Identification for Linear Time-Varying System
Physics-enhanced LSTM-EKF method identifies fast-varying natural frequencies and damping ratios in offshore wind turbines with 0.0012 Hz RMSE.
The work combines a long short-term memory network with an Extended Kalman Filter for physics-enhanced system identification of linear time-varying systems. It targets fast-varying natural frequencies and damping ratios in a 2-blade offshore wind turbine under wind and wave loading, validated on synthetic data from a finite element model. The identified FA-1 fore-aft mode frequency achieves a maximum RMSE of 0.0012 Hz, and damping ratio estimation via grid search improves on covariance-driven stochastic subspace identification.
- Combines LSTM with Extended Kalman Filter for time-varying modal identification
- Validated on finite-element synthetic data from a 2-blade offshore wind turbine
- FA-1 natural frequency identified with maximum RMSE of 0.0012 Hz
- Physics-based training data gave robustness to incorrect damping assumptions
From the original article
1 min read at arxiv.orgThis work proposes a physics-enhanced machine learning approach for the system identification of Linear Time-Varying (LTV) systems under time-varying operating conditions in terms of fast-varying natural frequencies and damping ratios by combining a long short-term memory network with an Extended Kalman Filter (EKF).
By Melisa Bozaci · arxiv.org
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