Presentations

Abstract

As the global wind turbine fleet matures, a growing number of wind farms are approaching or exceeding their original design lifetime. Lifetime reassessment and potential lifetime extension have therefore become critical topics for asset owners seeking to maximize value while maintaining safety and regulatory compliance. Reliable, data-driven methods are required to move beyond conservative design assumptions and provide turbine-specific estimates of remaining support structure life. This paper presents a novel and practical methodology for lifetime reassessment of on- and offshore wind turbines based on operational data of already existing sensors in the turbine with a particular focus on SCADA-derived tower top acceleration measurements combined with tower bottom load effects from the integrated load analysis (ILA) model used in the design phase. The proposed approach leverages tower top acceleration 10-min statistic signals routinely available from the turbine SCADA systems. These measurements are processed to characterize the dynamic response of the turbine support structure system under real operational and environmental conditions. Using the ILA model from either the design phase, or a calibrated model, the measured tower-top acceleration statistics are translated into corresponding tower bottom Damage Equivalent Load (DEL). This is done though a correlation between the 10-min standard deviation of the tower top accelerations and the tower bottom 1 Hz DEL. The method enables a representative in-service behaviour without additional measurements other than those already available for almost all turbines, rather than relying solely on pre-construction design load cases. By comparing the DEL from the measurements against design DEL, the methodology produces updated lifetime consumption estimates and projections of remaining useful life. Importantly, the approach accounts for site-specific factors (environmental conditions, operational strategies, and control system behaviour) which may differ significantly from assumptions made during the original design phase. Since the method relies on tower top 10-min acceleration statistics, it can easily be applied for all assets in the wind farm. It allows for reassessment of individual turbines as well as fleet-wide analyses. It supports both retrospective lifetime evaluation and forward-looking lifetime extension scenarios. This also includes operational scenarios where the turbine does not operate as expected, where response of the turbine can quickly be turned into an impact on the lifetime of the support structure. The proposed method has already been applied in multiple offshore wind projects, demonstrating its technical feasibility and practical value. Results from these applications show that measured operational data can significantly refine fatigue life estimates, in some cases indicating additional remaining lifetime beyond the original design assumption, while in others identifying turbines or components requiring closer attention. The use of existing SCADA and monitoring data minimizes the need for additional instrumentation, making the approach cost-effective and suitable for near-term deployment.  The audience will be provided with readily useable techniques for data-driven lifetime reassessment using SCADA statistics and ILA information already available from the design phase. The methodology provides a robust technical basis for asset management strategies, regulatory and certification discussions, as well as investment planning as the offshore wind industry enters its next phase of operation.