Offshore wind energy is crucial for achieving net zero emissions, with a goal of reaching 1,000 GW capacity. Automated remote monitoring can transform the offshore renewable energy market, but current technologies struggle with interpreting complex structural dynamics. This research aims to develop a new framework for monitoring offshore wind farms by integrating an efficient motion estimator into a real-time structural health monitoring (SHM) system, reducing the need for human intervention. The proposed model combines physical principles with advanced machine learning techniques to predict dynamic responses accurately. By addressing challenges like harsh conditions and complex load dynamics, this research will enhance the reliability and efficiency of SHM systems, leading to better management of offshore wind farm operations and advancing scientific understanding in this field.

2025

2029