Wind farm dynamic wake modeling analysis using engineering scale wake modeling simulations against flow field monitoring data

Wind turbine wake interactions in a wind farm play a fundamental role in power loss. Accurately characterizing wake dynamics through experimental validation of FAST (fatigue, aerodynamics, structures, and turbulence).Farm models under complex real-world conditions remain challenging. In this study, a rigorous validation of the FAST.Farm wake model against scanning light detection and ranging (LiDAR) measurements was performed to analyze multi-turbine wake interactions in operational wind farms under realistic atmospheric conditions. The methodology involves generating a synthetic inflow field and performing detailed simulations of an entire multi-turbine wind farm. Rigorous validation was performed by comparing the centerline and cross-sectional wake profiles, including overlapping and meandering, at various downstream distances. An evident relationship was found between the simulation and field measurements, effectively capturing critical wake dynamics, such as wind speed reduction, wake recovery patterns, and spatial wake meandering characteristics. The benchmarking results indicate that wake interactions significantly affect downstream turbine performance owing to wake overlap and meandering. The study concluded that integrating FAST.Farm simulations with LiDAR data provide an efficient tool for wind farm wake analysis, enabling improved management and operations.