A New Data-Driven Method for Structural Reliability-Based Lifetime Extension of Offshore Wind Support Structures

Abstract With wind turbines approaching or surpassing their designed operational lifespan of 20-25 years, the exploration of lifetime extension potentials becomes crucial. This paper presents a reliability-based assessment of steel support structures for offshore wind turbines, utilizing measured data from various sources to evaluate structural integrity. The proposed methodology adheres to IEC Standards and DNV guidelines, introducing a structural reliability format and identifying uncertainties associated with extending turbine life based on measured data. Traditionally, the assessment of a lifetime extension potential has relied on aero-elastic simulations, which are updated to represent actual structural, environmental and operational conditions. However, this paper proposes a novel data-driven approach that utilizes measurement data to predict the lifetime extension potential. A significant aspect of this methodology is virtual sensing, which is known as a cost efficient method to obtain the complete structural response based on a limited number of accelerometers in the wind turbine tower. The cost efficiency enables structural response monitoring of all wind turbines in a wind farm, providing the essential input to the data-driven reliability format suggested in this paper. The proposed data-driven approach integrates three types of data: measured load-response data (virtual sensing), operational data (SCADA), and environmental data (Metocean). By establishing a robust framework for data collection, integration, and validation, it provides a novel method to predict the structural reliability of offshore wind turbine support structures based on measurements. The methodology is validated using data from a wind farm in the Baltic Sea, showcasing a highly reliable and cost-efficient approach for assessing lifetime extension. By comparing virtual sensing predictions with actual strain gauge measurements, the accuracy of this data-driven method is confirmed, ensuring reliable assessments of structural integrity and lifetime extension potentials.