EFFICIENT LIFECYCLE RELIABILITY ASSESSMENT OF OFFSHORE WIND TURBINES USING DIGITAL TWIN

The objective of this study is to develop a reliability assessment framework for the maintenance optimization of offshore wind turbines (OWTs) using an uncertainty-aware digital twin framework. OWTs are typically located far from the coastline to optimize wind utilization efficiency and minimize disruption of human activities. However, the greater distance between OWTs and the coast can increase maintenance costs due to accessibility and exposure to harsh weather conditions. Effective planning is crucial in managing these costs. Therefore, employing digital twin models for OWTs can provide potential benefits. A digital twin framework creates a virtual replica of the turbine and leverages multi-source data for real-time simulations, enabling assessment of the turbine’s performance under various loading conditions, which can substantially enhance the maintenance decision-making process. The proposed framework has two main contributions: (1) uncertainty quantification in the long-term performance of OWTs at both the component and system levels, and (2) digital twin decision support leveraging OWT failure probabilities under various scenarios, which is used to provide maintenance recommendations aimed at optimizing system profitability and structural integrity. Additionally, the digital twin model provides clear and concise warnings regarding potential OWT failures.