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Summary Wake steering is very effective in optimizing the power production of an array of turbines aligned with the wind direction. However, the wind farm behaves as a porous obstacle for the incoming flow, inducing a secondary flow in the lateral direction and a reduction of the upstream wind speed. This is normally referred to as blockage effect. Little is known on how the blockage and the secondary flow influence the loads on the turbines when an intentional yaw misalignment is applied to steer the wake. In this work, we assess the variation of the loads on a virtual 4 by 4 array of turbines with intentional yaw misalignment under different levels of turbulence intensity. We estimate the upstream distance at which the incoming wind is influenced by the wind farm, and we determine the wind farm blockage effect on the loads. In presence of low turbulence intensity in the incoming flow, the application of yaw misalignment was found to induce a significant increase of damage equivalent load (DEL) mainly in the most downstream row of turbines. We also found that the sign (positive or negative) of the yaw misalignment affects differently the dynamic loads and the DEL on the turbines. Thus, it is important to consider both the power production and the blade fatigue loads to evaluate the benefits of intentional yaw misalignment control especially in conditions with low turbulence intensity upstream of the wind farm. 

Low-fidelity engineering wake models are often combined with linear superposition laws to predict wake velocities across wind farms under steady atmospheric conditions. While convenient for wind farm planning and long-term performance evaluation, such models are unable to capture the time-varying nature of the waked velocity field, as they are agnostic to the complex aerodynamic interactions among wind turbines and the effects of atmospheric boundary layer turbulence. To account for such effects while remaining amenable to conventional system-theoretic tools for flow estimation and control, we propose a new class of data-enhanced physics-based models for the dynamics of wind farm flow fluctuations. Our approach relies on the predictive capability of the stochastically forced linearized Navier–Stokes equations around static base flow profiles provided by conventional engineering wake models. We identify the stochastic forcing into the linearized dynamics via convex optimization to ensure statistical consistency with higher-fidelity models or experimental measurements while preserving model parsimony. We demonstrate the utility of our approach in completing the statistical signature of wake turbulence in accordance with large-eddy simulations of turbulent flow over a cascade of yawed wind turbines. Our numerical experiments provide insight into the significance of spatially distributed field measurements in recovering the statistical signature of wind farm turbulence and training stochastic linear models for short-term wind forecasting. 

An efficient strategy for maximizing the power production of a power plant is to control in a coordinated way only turbines that are aerodynamically coupled through wake effects. The implementation of such control strategy requires the knowledge of which clusters of turbines are coupled through wake interaction. In a previous study, we identified turbine clusters in real-time by evaluating the correlation among the power production signals of the turbines in the farm. In this study we reproduce the more challenging scenario with large scale variation of the wind direction. Different time windows of data needed to compute the correlation coefficients are tested and characterized in term of accuracy and promptness of the identification. 

This paper presents regression and classification methods to estimate wind direction in a wind farm from operational data. Two neural network models are trained using supervised learning. The data are generated using high-fidelity large eddy simulations (LES) of a virtual wind farm with 16 turbines, which are representative of the data available in actual SCADA systems. The simulations include the high-fidelity flow physics and turbine dynamics. The LES data used for training and testing the neural network models are the rotor angular speeds of each turbine. Our neural network models use sixteen angular speeds as inputs to produce an estimate of the wind direction at each point in time. Training and testing of the neural network models are done for seven discrete wind directions, which span the most interesting cases due to symmetry of the wind farm layout. The results of this paper are indicative of the potential that existing neural network models have to obtain estimates of wind direction in real time.