Quantifiable Scour Detection for Offshore Wind Turbines Using Resonance Frequency Monitoring and a Digital Twin

Abstract

This paper presents a methodology for enhancing frequency-based Structural Health Monitoring (SHM) of Offshore Wind Turbines (OWTs) by incorporating digital twin models. The proposed methodology utilizes an automated operational modal analysis to extract modal frequencies from acceleration measurements. The environmental and operational variability is removed through tree-based regression models, which can adequately recognize the different operational states of the OWT. The proposed data-driven approach relies on the detection of the modal frequency changes caused by a damaging scenario in the OWT (e.g. scouring). The digital twin model accounts for the actual properties of the substructure, considering the specific foundation geometry and soil conditions at the monitored OWT location. A statistical control chart is used to identify the possible scour depth by comparing the changes in the normalized modal frequency and the digital-twin calculations. The methodology is demonstrated on 4 months of measurements from a full-scale monitored OWT. The proposed methodology advances the SHM capabilities from damage detection to damage quantification, enabling improved operations & maintenance strategies for OWTs.