Structural health monitoring for asset management of offshore wind farms
Creators: name: Tewolde, Simon
name: Hoeffer, Ruediger
name: Haardt, Holger
Publisher: name: Zenodo
Year: 2019
Subjects: Structural Health Monitoring
Offshore wind turbines
data analysis
life cycle management
Vibration based SHM

description: Offshore wind energy industry is playing an important role in the transition from fossil based energy<br> sources to the renewable energy sources. But for rapid transition, being only clean and environmental<br> friendly is not enough. Renewable energy sources also need to be competitive with the other energy sources<br> in terms of cost and reliability. As for every new industry, wind energy started off expensive. But with<br> technological advancement in design and construction, significant cost reductions have been achieved. But<br> still, as suggested by several studies1, 2, there is high potential for further cost reduction by optimization of<br> the Operation and Maintenance (O&amp;M) during the service life time of the wind turbines. <br> For effective optimization of the O&amp;M, it is important to know the ongoing performance of the wind<br> turbines, so that an action can be taken in good time. Almost all offshore wind farms across the world are<br> equipped with Structural Health Monitoring (SHM) system. For example, in Germany until 2015, BSH -<br> Bundesamt für Seeschifffahrt und Hydrographie (German Federal Maritime and Hydrographic Agency)<br> recommended that 10% of the wind turbines in an offshore wind farm to be equipped with foundation<br> monitoring system. But in 2015, the 10% recommendation was replaced with “representative offshore wind<br> turbine sites”. This gave the offshore wind farm operators more flexibility, but also at the same time more<br> responsibility to come up with a concept to assure the safe operation of their structures. In this situation,<br> almost all wind farms are still following the 10% recommendation. <br> This paper deals with vibration based structural health monitoring and illustrates with practical examples<br> of the SHM data analysis results from offshore wind farms in the North Sea. The contents include:<br> development of a global monitoring concept which employs robust and highly sensitive sensors fit for the<br> offshore environment. Moreover, the monitoring concept makes best use of the available similar structures<br> to be monitored by implementing identical sensor positions on all monitored turbines. <br> The data analysis approach, which classifies the data analysis in three stages based on complexity level.<br> This is important considering the huge amount of data being continuously collected. The first stage comprises<br> of 10 minute statistical data evaluation, which is applied for detection of changes in structural integrity or<br> broken sensors in real time. This stage makes use of the responses from identically positioned sensors at the<br> monitored turbines. The second stage data analysis involves modal and fatigue analyses, which are also done<br> with some lag in real time. The third stage makes use of a validated finite element model (FEM) for threshold<br> limit initiation and updating after damage, significant degradation or maintenance. Environmental and<br> operational conditions are also used for compensation of their effects on structure behaviour.<br> In this paper, the data analysis results are further post processed to identify dangerous operation<br> conditions and propose a plan on how to optimize them for effective asset management. Furthermore, the<br> effect of special scenarios on the overall fatigue life time of the structures is quantified. <br> For the near future, the final goal is to develop a Risk Based Inspection (RBI) planning using the SHM data<br> analysis results. This can then help for the SHM data analysis results to be presented in a more compacted<br> and ready for use form, so that they can directly be used as an input for O&amp;M decision making by wind farm<br> operators.