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Wind tunnel experiments were performed to quantify the coupling mechanisms between incoming wind flows, power output fluctuations, and unsteady tower aerodynamic loads of a model wind turbine under periodically oscillating wind environments across various yaw misalignment angles. A high-resolution load cell and a data logger at high temporal resolution were applied to quantify the aerodynamic loads and power output, and time-resolved particle image velocimetry system was used to characterize incoming and wake flow statistics. Results showed that due to the inertia of the turbine rotor, the time series of power output exhibits a distinctive phase lag compared to the incoming periodically oscillating wind flow, whereas the phase lag between unsteady aerodynamic loads and incoming winds was negligible. Reduced-order models based on the coupling between turbine properties and incoming periodic flow characteristics were derived to predict the fluctuation intensity of turbine power output and the associated phase lag, which exhibited reasonable agreement with experiments. Flow statistics demonstrated that under periodically oscillating wind environments, the growth of yaw misalignment could effectively mitigate the overall flow fluctuation in the wake region and significantly enhance the stream-wise wake velocity cross correlation intensities downstream of the turbine hub location. 

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. 

Abstract This paper presents results from wind tunnel experiments to evaluate power gains from wake steering via yaw control. An experimental scaled wind farm with 12 turbines in an aligned rectangular array is used. Wake steering is performed by yawing turbines using a closed-loop algorithm termed the Log-of-Power Proportional Integral Extremum Seeking Control (LP-PIESC). Two configurations are considered. In the first configuration, the turbines in the first two upstream rows are controlled. In the second case, yaw control is applied to the turbines in the first upstream row and the third row. For both cases, uncontrolled turbines have no yaw misalignment. The results show that by independent parallel maximization of the power sum of a reduced number of turbines, it is possible to obtain a close approximation of the true maximum power. The data shows that the LP-PIESC algorithm can converge relatively fast compared to traditional ESC algorithms. 

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. 

This paper describes a multi-objective ESC strategy that determines Pareto-optimal control parameters to jointly optimize wind turbine loads and power capture. The method uses two optimization objectives calculated in real time: (a) the logarithm of the average power and (b) the logarithm of the standard deviation of a measurable blade load, tower load or the combination of these loads. These two objectives are weighted in real-time to obtain a solution that is Pareto optimal with respect to the power average and the standard deviation of chosen load metric. The method is evaluated using NREL FAST simulations of the 5-MW reference turbine. The results are then evaluated using energy capture over the duration of the simulation and damage equivalent loads (DEL) calculated with MLife. 

Abstract This work describes the results from wind tunnel experiments performed to maximize wind plant total power output using wake steering via closed loop yaw angle control. The experimental wind plant consists of nine turbines arranged in two different layouts; both are two dimensional arrays and differ in the positioning of the individual turbines. Two algorithms are implemented to maximize wind plant power: Log‐of‐Power Extremum Seeking Control (LP‐ESC) and Log‐of‐Power Proportional Integral Extremum Seeking Control (LP‐PIESC). These algorithms command the yaw angles of the turbines in the upstream row. The results demonstrate that the algorithms can find the optimal yaw angles that maximize total power output. The LP‐PIESC reached the optimal yaw angles much faster than the LP‐ESC. The sensitivity of the LP‐PIESC to variations in free stream wind speed and initial yaw angles is studied to demonstrate robustness to variations in wind speed and unknown yaw misalignment.