Applying Machine Learning to SENTINEL Satellite Images to Predict the Operational State of Floating Offshore Wind Turbines

This work explores the use of satellite imagery and open-source data to predict the operational status of floating offshore wind turbines, using the WindFloat Atlantic project as a case study. By integrating Sentinel-2 satellite images, metocean data, and offshore wind production data from ENTSO-E, a machine learning model was developed to estimate the operational state of offshore wind turbines. The approach relies on using already available platform offsets from satellite imagery and correlating them with wind intensity and direction. Results show a clear dependency between turbine movement and oper-ational status, particularly under higher wind conditions. Two classification models-Logistic Regression and Support Vector Machines-were evaluated, with Logistic Regression providing more stable performance across cross-validation scenarios. This study demonstrates the potential of remote sensing and open data to complement traditional monitoring systems, offering a scalable and cost-effective solution for offshore wind farm operation and maintenance strategies.