Quantification of parameter uncertainty in wind farm wake modeling

Reliable prediction of wind turbine wakes is essential for the optimal design and operation of wind farms. In order to achieve this, the parameter uncertainty of analytical wake model is investigated for the first time. Specifically, large eddy simulations (LES) of wind farms are carried out with different turbine yaw angles, based on SOWFA (Simulator fOr Wind Farm Applications) platform. The generated high-fidelity flow field data is used to infer the low-fidelity model’s parameters within the Bayesian uncertainty quantification framework. After model calibration, the posterior model check shows that the predicted mean velocity profile with the quantified uncertainty matches well with the high-fidelity CFD data. The prediction of other quantities, such as wind farm flow field and turbine power generation, is also carried out. The results show that the wake model with the model parameters specified by their posterior distributions can be seen as the stochastic extension of the original wake model. As most of the existing wake models are static, the resulting stochastic model shows a great advantage over the original model, as it can give not only the static wind farm properties but also their statistical distributions.