Presentations

Abstract

"This study addresses the lifetime assessment of an existing wind farm composed of early‑generation turbine designs, where both the availability and the reliability of SCADA data are notably limited. It illustrates how the integration of operational information with a dedicated mechanical loads measurement campaign and a targeted lidar deployment can yield a credible estimation of lifetime usage and residual service life. The proposed approach proves effective even under the severe data constraints commonly encountered in legacy wind farms. A ground‑based lidar system was installed during the loads measurement campaign to obtain high‑resolution wind field data. The lidar fulfilled two essential roles. First, it provided detailed wind condition measurements during the loads testing phase, strengthening the link between observed mechanical responses and the prevailing environmental conditions, and thereby supporting accurate modeling of the reference turbine behavior. Second, the lidar campaign enabled the extraction of long‑term wind characteristics at the site that could not be reconstructed from historical SCADA records alone. Key parameters such as turbulence intensity, vertical shear profiles, and their sector‑dependent variability were quantified and extrapolated to long‑term operating conditions. These lidar‑derived parameters are particularly important for older wind farms, as turbulence and shear—beyond mean wind speed—are dominant contributors to fatigue loading but are rarely documented in historical datasets. By combining SCADA‑derived operational statistics, lidar‑based wind characterization, and aeroelastic models validated through direct measurements, fatigue loading over the full operational history of the turbines is estimated. Damage‑equivalent loads and cumulative fatigue damage are calculated for critical components, allowing direct comparison with original design assumptions and supporting the identification of possible life‑extension scenarios. For aging assets with incomplete or low‑quality operational data, this methodology offers a realistic and technically robust means of evaluating structural condition. Overall, the results confirm that the joint use of SCADA information, dedicated lidar observations, and experimentally validated aeroelastic simulations constitutes a reliable framework for lifetime evaluation in data‑poor, legacy wind farms. The improved wind field description enabled by lidar measurements significantly enhances prediction accuracy and lowers uncertainty, contributing to safer operation and more economically sound lifetime management of aging wind turbines."