Generalization of single wake surrogates for multiple and farm-farm wake analysis

Abstract

Abstract Wind farm efficiency is influenced by atmospheric turbulence and wake interactions from preceding turbines. Optimal performance necessitates effective control strategies, encompassing collective/individual pitch (and/or torque) control, yaw control, and innovative techniques, significantly boosting energy capture. Wake effects are crucial, impacting downstream turbines and reducing overall energy extraction. For this purpose, the study of wind farm flow control (WFFC) holds significant relevance in this context. In this study, a Deep Neural Network predicts downstream flow features of a wind turbine under diverse control scenarios, including varying thrust coefficient and yaw control. A feed-forward neural network models the deficit and added turbulent intensity of a single wake, trained using Computational Fluid Dynamics (CFD) simulations as reference data. Linear superposition establishes the wind farm flow field, allowing examination of downstream effects. Another feedforward neural network encompasses wind farm physics such as blockage and wake recovery in and around wind turbine arrays which are overlooked by the single wake model. The methodology employed in this study yields results that are more time-efficient compared to traditional CFD models while maintaining a higher level of accuracy (ideally) than the engineering models, especially when implemented for WFFC without calibration.