Search

Wind turbines in a wind plant do not always experience free-stream flow fields. The flow fields inside a wind plant or wind farm, waked by upwind turbines, exhibit different dynamic characteristics. The International Electrotechnical Commission (IEC) standard 61400-1 for the design of wind turbines only considers a deterministic wake model for the design of a wind plant. This study is focused on the stochastic modeling of waked wind fields for assessing turbine loads using a regression-based approach. The waked wind velocity field is generated using Large-Eddy Simulation (LES). Stochastic characteristics of the generated waked wind velocity field, including the mean and turbulence components, are analyzed. Proper orthogonal decomposition (POD) and spectral methods are proposed to develop reduced-order engineering model-based wind velocity fields as alternatives to the LES-generated waked fields. The reduced-order model-based simulation employs either a subset of the POD eigenmodes or Fourier-based spectral simulation with parameters derived from LES in illustrations with a wake-generating turbine. With the spectral model, wake-related spectral parameters are estimated using Multivariate Multiple Linear Regression (MMLR). To validate the simulated wind fields based on the reduced-order models, wind turbine tower and blade loads are generated using aeroelastic simulation for a utility-scale wind turbine model and compared with those based on LES. This study also discusses the construction of a stochastic expanded-wake model for wind turbines experiencing fully and partially waked situations. The study's overall objective is to offer efficient stochastic approaches that are computationally tractable, when assessing the performance and loads of turbines operating in wakes. Validation studies are carried out by comparion with loads computed directly from LES wake fields.