TP III.3 Site-specific assessment of the remaining service life of rotor blades using data-based finite element modeling

A sub-project in Innovation Area III – Life-Cycle Management of offshore wind turbines and wind farms

Understanding the real load and environmental conditions is crucial for analyzing the actual performance (service life) of the blade.

One challenge in this context is the limited availability of local stress data for re-evaluating the service life of the blade under real operating conditions.

Here, the Living Lab Offshore offers the unique opportunity to use data from the SHM system for a real and locally resolved fatigue analysis using a generic FE model of the rotor blade in order to determine the site-specific causes of damage development.

TP III.2 Data-driven methods for determining the remaining service life of load-bearing structures

A sub-project in Innovation Area III – Life-Cycle Management of offshore wind turbines and wind farms

Based on the SHM concept from SP III.1, data-driven methods for determining the remaining service life of load-bearing structures are being developed and validated, thus laying the foundation for their acceptance in the certification process.

The main objective of this sub-project is the (further) development of data-driven methods in the co-production phase, so that the remaining service life of load-bearing structures can be estimated even if the data situation is incomplete.

The aim of further development is firstly to use the available data as comprehensively as possible, secondly to investigate the benefits of additional measurement data and thirdly to be able to quantify the uncertainty in the extrapolations.

TP III.1 Data-based structural health monitoring (SHM)

A sub-project in Innovation Area III – Life-Cycle Management of offshore wind turbines and wind farms

In this sub-project, a suitable SHM concept for the continuous monitoring of the support structure, tower and rotor blades is applied to existing sensors and measurement technology in the offshore wind farm and supplemented as required.

The extent to which existing sensor and monitoring infrastructure and previously recorded data can be used must be agreed with the operator. The innovation lies in the comprehensive analysis of structural and SCADA data already available in the OWP using probabilistic and deterministic system identification methods and AI-based methods.

The aim is to identify all relevant operating states and system parameters and to identify critical operating states and damage scenarios which, if known, can be eliminated in order to significantly extend the remaining service life of a wind farm.