Presentations

Abstract

This work presents the lifetime evaluation of a real, aging wind farm equipped with very old turbine models and significant limitations in SCADA data availability and quality. The study demonstrates how a combined use of operational data, a mechanical loads measurement campaign, and a dedicated lidar deployment enables a reliable assessment of actual lifetime consumption and remaining useful life, even under strong data constraints typical of legacy assets.  A ground base lidar system was deployed during the loads campaign, providing high‑resolution wind measurements. The lidar served two key purposes. First, it enabled precise wind characterization during the mechanical loads testing, improving the correlation between measured loads and environmental conditions, and ensuring that the reference turbine’s response could be accurately modelled. Second, and equally important, the lidar campaign allowed the characterization of long‑term wind‑field properties at the site that were not available from the historical SCADA data. Parameters such as turbulence intensity, vertical wind shear, and their variability across operating sectors were derived and scaled to long‑term wind conditions. These lidar‑based insights were critical, because on top of the wind speed, turbulence and shear are major drivers of fatigue loading, yet are typically unknown in older wind farms. The combination of SCADA‑based statistics, lidar monitoring, and measurement‑validated aeroelastic modeling is used to estimate fatigue loads over the turbine’s operational life. Damage equivalent loads and cumulative fatigue damage are computed for key components, enabling comparison with design expectations and identification of potential life‑extension opportunities. For older assets with incomplete operational records, this integrated approach provides a practical and reliable pathway to evaluate structural health. The results demonstrate that the combined use of SCADA data, lidar measurements, and validated aeroelastic simulations forms a robust methodology for lifetime evaluation, even in legacy wind farms with limited data availability. The enhanced wind characterization provided by lidar substantially improves accuracy and reduces uncertainty, supporting safer and more cost‑effective operation of aging wind turbines.