Replication Data for: Damage detection in chain and synthetic mooring lines of Floating Offshore Wind Turbines

About 65 GW of onshore wind turbine installations in Europe will reach end-of-design-life by 2028. It is time for the operators to decide on one of the three end-of-life scenarios, namely, decommissioning, lifetime extension, or repowering. The last two options will increase the operating life and thus reduce lifecycle costs. These end-of-life decisions require careful consideration of the accumulated fatigue life of each turbine in a wind farm to minimize monetary risk for the wind farm operators. Today, this decision is primarily based on a single point assessment by the certification authority.

AIMWind (Analytics for asset Integrity Management of Windfarms) project (https://www.aimwind.no/) proposes a continuous evaluation of wind farm health based on big data analytics using multimodal data such as wind, operational data, weather, condition monitoring, and inspection logs across a wind farm. Conventional approaches to fatigue estimation are slow and inadequate to achieve these goals, especially in large wind farms. Such a continuous health assessment will facilitate not only accurate life predictions but also continuous improvement of wind turbine operations to ensure long life and high availability.

In the context of the AIMWind project, simulated acceleration data representing the healthy and damaged chain or synthetic mooring lines of Floating Offshore Wind Turbines have been generated. The data have been used for the validation of various machine learning methods used for damage detection in the considered chain and synthetic mooring lines.