A non-intrusive reduced-order model for wind farm wake analysis based on SPOD-DNN

Abstract

Wind farm wake modeling is of great significance for wind turbine layout optimization design and yaw control strategy. In this work, we combine deep neural network (DNN) with spectral proper orthogonal decomposition (SPOD) to discover dynamic characteristics of wake under different inflow conditions. Then an assessment of the proposed SPOD-DNN surrogate modeling method of parameterized fluid is performed by comparing the predicted results. Meanwhile, we demonstrate the robustness of the SPOD-DNN through a comparison with POD-DNN, where SPOD produces fewer modes than POD but can achieve the same cumulative contribution rate and wake prediction accuracy. In the end, the method is developed to predict the wake of single wind turbine in untrained inflow condition and Wake of six wind turbines with different yaw angles. The results reveals that the model has good generalization performance and can robustly reconstruct the wake of multiple wind turbines in different directions.