Session: Structural Design-2

Paper Number: 165440

165440 - A New Data-Driven Method for Structural Reliability-Based Lifetime Extension of Offshore Wind Support Structures 

Abstract: 

As wind turbines near or surpass their designed operational lifespan of 20-25 years, extending their operational life becomes essential. To assess whether these turbines can continue to operate safely and efficiently, two main methods can be used: deterministic methods, which use predefined safety factors, or structural reliability-based methods, as specified in e.g. the IEC 61400-1:2019 standard.

This paper focuses on the reliability-based assessment of steel support structures for offshore wind turbines. It uses data from various sources, including measured responses, operational data, and environmental conditions, to evaluate the turbines' structural integrity.

The paper introduces a new methodology that aligns with IEC Standards and DNV guidelines and proposes a structural reliability format. Furthermore, the paper identifies the uncertainties that need to be considered when calculating the potential for extending the turbines' operational life based on measured data. This approach not only supports extending the turbines' life beyond their original design but also improves the ability to predict future performance and potential failure points.

A significant aspect of this methodology is virtual sensing, which uses data from a limited number of accelerometers to predict the structural response throughout the turbine. This approach is more economical and allows for comprehensive monitoring of all turbines in a wind farm.

To ensure the accuracy of virtual sensing, its predictions are compared with actual strain gauge measurements taken at selected points on a limited number of support structures in the wind farm. This validation process confirms that virtual sensing provides reliable data for assessing the turbines' remaining operational life.

The methodology is validated by using data from wind farms in the North Sea, and the first results show that the methodology provides a highly reliable and cost-efficient lifetime extension assessment method. The results indicate that this data-driven approach, validated against strain gauge measurements, can reliably assess the structural integrity of wind turbine support structures based solely on measured data.

This methodology leverages three types of data:

- Measured load-response data (Virtual Sensing): This involves using a few accelerometers placed on the turbine tower to estimate the structural response at different locations.

- Operational data (SCADA): Supervisory Control and Data Acquisition (SCADA) systems collect real-time data on the turbine's performance and operational conditions.

- Environmental data (Metocean): This includes information about the marine and atmospheric conditions affecting the turbines.

By offering a robust framework for data collection, integration, and validation, this methodology provides a novel way to predict the structural reliability of wind turbine support structures, which is essential for assessing any lifetime extension potential. This contributes to the long-term sustainability of wind energy as a clean energy source.

Presenting Author: John D. Sørensen Aalborg University

Presenting Author Biography: John Dalsgaard Sørensen is professor in structural reliability at Aalborg University, Denmark. His main research fields are stochastic modelling, reliability assessment and risk-based decision making for planning of inspections and O&M. Application areas include offshore structures and wind turbines. Professor Sørensen has published more than 100 journal papers and 200 international conference papers. He is leader of a research group ‘Risk, Resilience, Safety and Sustainability of systems’. Further, he is active in standardization activities in the Eurocodes and as convenor for three standards within IEC-61400 for wind turbines, especially related to reliability.

Authors:

John D. Sørensen Aalborg University
Ulf T. Tygesen Vattenfall
Johan F. Toftekær Vattenfall
Michael S. Jepsen Vattenfall