Application of OMA for Low-Frequency Modes Detection in FOWT: Numerical Study on OC4-DeepCwind Semi-Submersible

Abstract The dynamic interaction between mooring system, floating platform, and wind turbines is complex, leading to greater uncertainties in design and higher operational and maintenance costs (OPEX). A potential solution to mitigate these uncertainties and reduce OPEX is the application of remote Structural Health Monitoring (SHM) systems. Among SHM techniques, Operational Modal Analysis (OMA) is particularly valuable for assessing the dynamic properties of structures under actual operating conditions. This research explores the reliability of detecting low-frequency modes of Floating Offshore Wind Turbines (FOWTs) using OMA. The analysis employs the Least Squares Complex Frequency (LSCF) algorithm and numerical sensor data. The NREL 5MW reference wind turbine mounted on the OC4 semi-submersible platform was used. Acceleration time-series signals were generated using the time-domain software OpenFAST at various points on the FOWT, simulating accelerometer placements. The pre-processed signals were then analyzed using the LSCF algorithm to estimate the natural frequencies and damping ratios of the low-frequency modes, including the first tower mode, via stabilization diagrams. Results showed that the LSCF algorithm successfully detected all low-frequency modes of the FOWT, up to the first tower bending modes. The study on window length sensitivity indicated that a window length above 600s is required for consistent modal parameter estimation. Additionally, the analysis of sensor placement revealed that placing translational and rotational accelerometers close to the platform provides good estimates of the platform low-frequency motions, particularly yaw.