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Method for evaluating wind turbine wake effects on wind farm performance
A method of testing the performance of a cluster of wind turbine units an data analysis equations are presented which together form a simple and direct procedure for determining the reduction in energy output caused by the wake of an upwind turbine. This method appears to solve the problems presented by data scatter and wind variability. Test data from the three-unit Mod-2 wind turbine cluster at Goldendale, Washington, are analyzed to illustrate the application of the proposed method. In this sample case the reduction in energy was found to be about 10 percent when the Mod-2 units were separated a distance equal to seven diameters and winds were below rated.
How Does Inow Veer Affect the Veer of a Wind-Turbine Wake?
Stably stratified flow conditions often exhibit wind veer, or a change of wind direction with height. When wind turbines experience this veered flow, the resulting wake structure tends to exhibit a stretching into an ellipsoid, rather than a symmetric shape or a curled shape. Observational studies suggest that the magnitude of wake veer is less than the veer of the inflow, whereas large-eddy simulations with actuator disk models and actuator line models suggest a range of relationships between inflow veer and wake veer. Here we present a series of large-eddy simulations with a range of veer shapes, a range of magnitudes of veer, a range of wind speeds, and both rotational directions of the wind-turbine rotor investigating the effect on the wake deflection angle. These results can guide the application of wake steering in stably stratified flow.
A Three-Dimensional, Analytical Wind Turbine Wake Model: Flow Acceleration, Empirical Correlations, and Continuity
A new, three-dimensional, analytical, steady-state wake model is presented that includes local flow acceleration near the rotor, improving the wake description compared to existing models. Wake structures such as the momentum deficit and regions of accelerated flow are concisely described with compound and normal Gaussian functions. Large-eddy simulations (LES) are used as training data to develop the model using two in-line turbines under various inflow conditions parameterized by hub-height wind speed and turbulence intensity. Mass conservation is considered by fixing two components of the wake velocity model and optimizing the third to best satisfy continuity; after which, the model performs comparably if not better than existing work with regards to both relative error and mass consistency. The final model demonstrates a high degree of flexibility, making use of empirical correlations to scale across different inflow conditions. The inclusion of these effects is capable of revealing unused opportunities for enhanced power generation by aligning wake trajectories with these regions of accelerated flow.
On the interaction of a wind turbine wake with a conventionally neutral atmospheric boundary layer
In this work, we investigate the dynamics of wind turbine tip-vortex breakdown in a conventionally neutral atmospheric boundary layer (ABL). To this end, high-resolution data are collected from large-eddy simulations of a wind turbine operating within a neutral ABL and studied by means of proper orthogonal decomposition (POD) and Fourier analysis. The high resolution of the generated data in both space and time allows us to gain insight into the tip-vortex breakdown mechanisms by (i) capturing the energy modes of the coherent structures, (ii) studying their contribution to the tip-vortex breakdown through their power spectra functions and mean kinetic energy (MKE) flux, and (iii) analysing the growth rate of each contributing perturbation frequency along tip vortices. Our analysis shows that under a fully turbulent scenario, the growth rate of perturbations along the tip vortices is largest for low wave numbers, i.e. long-wave perturbations. Additionally, the MKE flux reaches its highest value at two diameters downstream of the rotor plane, a behaviour that can be attributed to the coexistence of multiple interacting POD modes, with the streamwise vortex roller mode being the primary contributor to the total MKE flux budget, contributing approximately . Finally, comparisons with a laminar, uniform flow scenario subject to a single-frequency perturbation highlight the differences between the two ambient flow conditions. In the non-turbulent, uniform flow scenario, the growth rate attains its maximum value at a wave number corresponding to the out-of-phase mutual-inductance mechanism, whereas the MKE flux exhibits local minima and maxima along the wake and at different downstream locations depending on the perturbation frequency. Our analyses suggest that the breakdown of the wind turbine tip vortices under a fully turbulent neutral ABL inflow is due to complex interactions across a range of excitation frequencies, in which the mutual-inductance instability may not be the dominant one.
Long-range Doppler lidar measurements of wind turbine wakes and their interaction with turbulent atmospheric boundary-layer flow at Perdigao 2017
As part of the Perdigão 2017 campaign, vertical RHI (range-height indicator) scans with long-range pulsed Doppler wind lidars were performed aligned with the main wind direction and a wind turbine (WT) located on a mountain ridge. The measurements are used to not only retrieve flow velocities, but also their variance and - by using the turbulent broadening of the Doppler spectrum - also turbulent kinetic energy (TKE) dissipation rate. The study shows that turbulence in the WT wake is dependent on the turbulence of the inflow, but also on atmospheric stability. In stable atmospheric conditions, wakes could be analyzed up to five rotor diameters downstream (D) and showed the maximum turbulence in the wake at 2-3 D, whereas in unstable conditions, the maximum was found at 2 D and the wake could not be detected further than 3 D. A clear dependency of wake turbulence enhancement on inflow turbulence intensity is found, which levels out to no further enhancement at turbulence intensities of 30%.
Towards multi-fidelity deep learning of wind turbine wakes
We report engineering wake models that accurately predict wake in a computationally efficient manner are very important for tasks such as layout optimization and control of wind farms. In this paper, we explore an application of deep learning (DL) to learn the wake model from hierarchies of physics-based approaches ranging from analytical models to an approximate form of the Reynolds-averaged Navier-Stokes equations. We first illustrate the application of principal component analysis to obtain a lower-dimensional representation that allows a computationally tractable training and deployment of DL models. Then, the DL model is trained to learn the mapping from input parameter space to the principal components, which are then used to reconstruct the three-dimensional flow field. Additionally, we investigate a composite framework consisting of two neural networks to learn the correlation between low- and high-fidelity data with Gauss and curl models treated as proxies for low- and high-fidelity models, respectively. The prediction from both DL models matches well with the high-fidelity data with a maximum relative percentage error for the kinetic energy flux of <1%. This work opens up possibilities for data-efficient construction of surrogate models for wake prediction that can be used to study the influence of wind speed and yaw angles on wind farm power production.
Wind turbine wake characterization using the SpinnerLidar measurements.
Abstract not provided.
Rapidly Recovering Wind Turbine Wakes with Dynamic Pitch and Rotor Speed Control.
Abstract not provided.
Novel, Nacelle-mounted Spire for Accelerated Wind Turbine Wake Decay.
Abstract not provided.
Application of parallel-flow linear stability theory to wind turbine wakes with implications for active wake control.
Abstract not provided.
Rapidly Recovering Wind Turbine Wakes with Dynamic Pitch and Rotor Speed Control.
Abstract not provided.
Ensemble neural networks for post-processing contaminated Doppler LiDAR spectra to improve wind turbine wake diagnostics.
Abstract not provided.
Novel, Nacelle-mounted Spire for Accelerated Wind Turbine Wake Decay.
Abstract not provided.
Analytical solution for the cumulative wake of wind turbines in wind farms
This paper solves an approximate form of conservation of mass and momentum for a turbine in a wind farm array. The solution is a fairly simple explicit relationship that predicts the streamwise velocity distribution within a wind farm with an arbitrary layout. As this model is obtained by solving flow-governing equations directly for a turbine that is subject to upwind turbine wakes, no ad hoc superposition technique is needed to predict wind farm flows. A suite of large-eddy simulations (LES) of wind farm arrays is used to examine self-similarity as well as validity of the so-called conservation of momentum deficit for turbine wakes in wind farms. The simulations are performed with and without the presence of some specific turbines in the wind farm. This allows us to systematically study some of the assumptions made to develop the analytical model. A modified version of the conservation of momentum deficit is also proposed to provide slightly better results at short downwind distances, as well as in the far wake of turbines deep inside a wind farm. Model predictions are validated against the LES data for turbines in both full-wake and partial-wake conditions. While our results highlight the limitation in capturing the flow speed-up between adjacent turbine columns, the model is overall able to acceptably predict flow distributions for a moderately sized wind farm. Finally, the paper employs the new model to provide insights on the accuracy of common wake superposition methods.
Evolution of eddy viscosity in the wake of a wind turbine
The eddy viscosity hypothesis is a popular method in wind turbine wake modeling for estimating turbulent Reynolds stresses. We document the downstream evolution of eddy viscosity in the wake of a wind turbine from experimental and large-eddy-simulation data. Wake eddy viscosity is isolated from its surroundings by subtracting the inflow profile, and the driving forces are identified in each wake region. Eddy viscosity varies in response to changes in turbine geometry and nacelle misalignment with larger turbines generating stronger velocity gradients and shear stresses. We propose a model for eddy viscosity based on a Rayleigh distribution. Model parameters are obtained from scaling the eddy viscosity hypothesis and demonstrate satisfactory agreement with the reference data. The model is implemented in the curled wake formulation in the FLOw Redirection and Induction in Steady State (FLORIS) framework and assessed through comparisons with the previous formulation. Our approach produced more accurate flow field estimates with lower total error for the majority of cases.
Highlighting the Impact of Yaw Control by Parsing Atmospheric Conditions Based on Total Variation: Preprint
Identification of atmospheric conditions within a multivariate atmospheric dataset is a necessary step in the validation of wind plant control strategies. Most often, operating conditions are characterized in terms of aggregated observations and assume that the atmosphere is 'quasi-steady'. Aggregation of observations without regard to covariance between time series discounts the dynamical nature of the atmosphere and is not sufficiently representative of wind plant operating conditions. Identification and characterization of continuous time periods with atmospheric conditions that have a high value for analysis or simulation sets the stage for more advanced model validation and the development of real-time control and operation strategies. Controlling observational data for statistical stationarity highlights significant enhancements to the power production of waked turbines under wake steering wind plant control. Considering the combined energy ratio of the turbine to which yaw offsets are prescribed as well as the waked turbine show only moderate improvements when filtering for total validation. The same quality control that highlights wake steering also serves to underpin the off-nominal operation of the controlled turbine.