Wind–Wave Interaction Effects on a Wind Farm Power Production

2017 ◽  
Vol 139 (5) ◽  
Author(s):  
A. AlSam ◽  
R. Szasz ◽  
J. Revstedt
Keyword(s):  
Wind Farm ◽  
Wave Interaction ◽  
Power Production ◽  
External Source ◽  
Sea Waves ◽  
Atmospheric Boundary Layers ◽  
Power Extraction ◽  
Large Eddy ◽  
Sea Surface Waves ◽  
Wave Induced

In the current study, the effects of the nonlocally generated long sea surface waves (swells) on the power production of a 2 × 2 wind farm are investigated by using large-eddy simulations (LES) and actuator-line method (ALM). The short sea waves are modeled as a roughness height, while the wave-induced stress accounting for swell effects is added as an external source term to the momentum equations. The results show that the marine atmospheric boundary layers (MABLs) obtained in this study have similar characteristics as the MABLs observed during the swell conditions by many other studies. The current results indicate also that swells have significant impacts on the MABL. As a consequence of these changes in the MABL, swells moving faster than the wind and aligned with the local wind direction increase the power extraction rate.

scholarly journals Dynamic Balances in a Wavy Boundary Layer

2014 ◽  
Vol 44 (12) ◽  
pp. 3185-3194 ◽  
Author(s):  
Tihomir Hristov ◽  
Jesus Ruiz-Plancarte
Keyword(s):  
Kinetic Energy ◽  
Wind Wave ◽  
Empirical Studies ◽  
Wave Interaction ◽  
Mean Flow ◽  
Atmospheric Flow ◽  
Wave Effects ◽  
Sea Surface Waves ◽  
Horizontal Inhomogeneity ◽  
Wave Induced

Abstract The authors analyze the influence of waves on the budgets of momentum flux and kinetic energy in the atmospheric flow over sea surface waves and use the findings to reinterpret the results from the earlier empirical studies on the subject. This analysis employs the framework of wave–mean flow interaction and experimental data collected recently over the open ocean. From a minimal set of plausible assumptions, limited to small-slope waves and uncorrelated turbulent and wave-induced motions in the wind, this study demonstrates that the budgets apply separately to the turbulent and the wave-induced flows. The explicit forms of the wave-supported fluxes of momentum and kinetic energy favor wave spectra ∝ ω−β, 4 ≤ β ≤ 5 for wind–wave equilibrium. These explicit forms also show that in common conditions at heights above one significant wave height from the unperturbed surface, the wave-supported fluxes are a small fraction of the total, of the order of 5%. The wave influence on the kinetic energy budget and on the shape of the wind profile is therefore also small at these heights and thus difficult to identify experimentally next to influences from nonstationarity or horizontal inhomogeneity. Consequently, the predictions of Monin–Obukhov phenomenology show little sensitivity to wave effects. This makes the phenomenology as valid over the ocean as it is over land, but a poor instrument for studying wind–wave interaction. Describing the wind–wave interaction through the dynamics and statistics of the wave-induced motion remains a viable and productive alternative.

scholarly journals Large-Eddy Simulations of Stratified Atmospheric Boundary Layers: Comparison of Different Subgrid Models

2020 ◽  
Author(s):  
Srinidhi N. Gadde ◽  
Anja Stieren ◽  
Richard J. A. M. Stevens
Keyword(s):  
Boundary Layer ◽  
Boundary Layers ◽  
Large Scale ◽  
Wind Farm ◽  
Research Area ◽  
Large Eddy Simulations ◽  
Turbulence Energy ◽  
Atmospheric Boundary Layers ◽  
Smagorinsky Model ◽  
Large Eddy

Abstract The development and assessment of subgrid-scale (SGS) models for large-eddy simulations of the atmospheric boundary layer is an active research area. In this study, we compare the performance of the classical Smagorinsky model, the Lagrangian-averaged scale-dependent (LASD) model, and the anisotropic minimum dissipation (AMD) model. The LASD model has been widely used in the literature for 15 years, while the AMD model was recently developed. Both the AMD and the LASD models allow three-dimensional variation of SGS coefficients and are therefore suitable to model heterogeneous flows over complex terrain or around a wind farm. We perform a one-to-one comparison of these SGS models for neutral, stable, and unstable atmospheric boundary layers. We find that the LASD and the AMD models capture the logarithmic velocity profile and the turbulence energy spectra better than the Smagorinsky model. In stable and unstable boundary-layer simulations, the AMD and LASD model results agree equally well with results from a high-resolution reference simulation. The performance analysis of the models reveals that the computational overhead of the AMD model and the LASD model compared to the Smagorinsky model is approximately 10% and 30% respectively. The LASD model has a higher computational and memory overhead because of the global filtering operations and Lagrangian tracking procedure, which can result in bottlenecks when the model is used in extensive simulations. These bottlenecks are absent in the AMD model, which makes it an attractive SGS model for large-scale simulations of turbulent boundary layers.

Large-Eddy Simulation of Offshore Wind Farms for Power Prediction

2018 ◽  
Author(s):  
Micah Sandusky ◽  
Rey DeLeon ◽  
Inanc Senocak
Keyword(s):  
Boundary Layer ◽  
Large Eddy Simulation ◽  
Atmospheric Boundary Layer ◽  
Wind Field ◽  
Wind Farm ◽  
Power Production ◽  
Offshore Wind ◽  
Detailed Knowledge ◽  
Eddy Simulation ◽  
Large Eddy

Offshore wind turbines are mega structures that span a critical section of the lowest part of atmospheric boundary layer while experiencing significant wind shear. A detailed knowledge of the wind field through a wind farm as part of the atmospheric boundary layer is essential to design efficient farm layouts and estimate power production for grid integration. To address these needs, we present a micro-scale wind prediction model based on a large-eddy simulation paradigm. We consider actuator disk models with and without rotation to simulate the influence of turbines on the wind field and apply our computational capability to the well-known Horns Rev offshore wind farm in Denmark to estimate power production. Instead of using manufacturers power curve to estimate power production, we propose an alternative approach based on the control volume analysis of kinetic energy conservation around turbines.

scholarly journals Large Eddy Simuation of an Onshore Wind Farm with the Actuator Line Model Including Wind Turbine’s Control Below and Above Rated Wind Speed

Energies ◽  
2019 ◽  
Vol 12 (18) ◽  
pp. 3508 ◽  
Author(s):  
Andrés Guggeri ◽  
Martín Draper
Keyword(s):  
Wind Speed ◽  
Wind Turbines ◽  
Wind Farm ◽  
Fluid Dynamic ◽  
Power Production ◽  
Model Framework ◽  
Line Model ◽  
Onshore Wind ◽  
Large Eddy ◽  
Actuator Line Model

As the size of wind turbines increases and their hub heights become higher, which partially explains the vertiginous increase of wind power worldwide in the last decade, the interaction of wind turbines with the atmospheric boundary layer (ABL) and between each other is becoming more complex. There are different approaches to model and compute the aerodynamic loads, and hence the power production, on wind turbines subject to ABL inflow conditions ranging from the classical Blade Element Momentum (BEM) method to Computational Fluid Dynamic (CFD) approaches. Also, modern multi-MW wind turbines have a torque controller and a collective pitch controller to manage power output, particularly in maximizing power production or when it is required to down-regulate their production. In this work the results of a validated numerical method, based on a Large Eddy Simulation-Actuator Line Model framework, was applied to simulate a real 7.7 MNW onshore wind farm on Uruguay under different wind conditions, and hence operational situations are shown with the aim to assess the capability of this approach to model actual wind farm dynamics. A description of the implementation of these controllers in the CFD solver Caffa3d, presenting the methodology applied to obtain the controller parameters, is included. For validation, the simulation results were compared with 1 Hz data obtained from the Supervisory Control and Data Acquisition System of the wind farm, focusing on the temporal evolution of the following variables: Wind velocity, rotor angular speed, pitch angle, and electric power. In addition to this, simulations applying active power control at the wind turbine level are presented under different de-rate signals, both constant and time-varying, and were subject to different wind speed profiles and wind directions where there was interaction between wind turbines and their wakes.

An Investigation of Wind Farm Power Production for Various Atmospheric Boundary Layer Heights

2017 ◽  
Vol 139 (5) ◽  
Author(s):  
A. Al Sam ◽  
R. Szasz ◽  
J. Revstedt
Keyword(s):  
Boundary Layer ◽  
Atmospheric Boundary Layer ◽  
Wind Shear ◽  
Wind Farm ◽  
Power Production ◽  
Large Eddy Simulations ◽  
Power Coefficient ◽  
Higher Power ◽  
Large Eddy ◽  
Aerodynamic Roughness

The dependency of the atmospheric boundary layer (ABL) characteristics on the ABL’s height is investigated by using large eddy simulations (LES). The impacts of ABL’s height on the wind turbine (WT) power production are also investigated by simulating two subsequent wind turbines using the actuator line method (ALM). The results show that, for the same driving pressure forces and aerodynamic roughness height, the wind velocity is higher at deeper ABL, while the wind shear and the wind veer are not affected by the depth. Moreover, the turbulence intensity, kinetic energy, and kinematic shear stress increase with the ABL’s height. Higher power production and power coefficient are obtained from turbines operating at deeper ABL.

The Influence of Sea Waves on Offshore Wind Turbine Aerodynamics

2015 ◽  
Vol 137 (5) ◽  
Author(s):  
A. AlSam ◽  
R. Szasz ◽  
J. Revstedt
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Offshore Wind ◽  
Speed Ratio ◽  
Offshore Wind Turbine ◽  
Intensity Level ◽  
Sea Waves ◽  
Power Extraction ◽  
Wind Turbine Aerodynamics ◽  
Large Eddy

The impacts of swells on the atmospheric boundary layer (ABL) flows and by this on the standalone offshore wind turbine (WT) performance are investigated by using large eddy simulations (LES) and actuator-line techniques. At high swell to wind speed ratio, the swell-induced stress reduces the total wind stress resulting in higher wind velocity, less wind shear, and lower turbulence intensity level. These effects increase by increasing swell to wind speed ratio (C/U) and/or swell steepness. Moreover, for the same hub-height wind speed (Uhub), the presence of swells increases the turbine power extraction rate by about 3% and 8.4% for C/Uhub = 1.53 and 2.17, respectively.

scholarly journals Time-Averaged Wind Turbine Wake Flow Field Prediction Using Autoencoder Convolutional Neural Networks

Energies ◽  
2021 ◽  
Vol 15 (1) ◽  
pp. 41
Author(s):  
Zexia Zhang ◽  
Christian Santoni ◽  
Thomas Herges ◽  
Fotis Sotiropoulos ◽  
Ali Khosronejad
Keyword(s):  
Wind Turbines ◽  
Wind Farm ◽  
Power Production ◽  
Velocity Fields ◽  
Large Eddy Simulations ◽  
Wake Flow ◽  
Surface Model ◽  
Flow Conditions ◽  
Large Eddy ◽  
Turbine Wake

A convolutional neural network (CNN) autoencoder model has been developed to generate 3D realizations of time-averaged velocity in the wake of the wind turbines at the Sandia National Laboratories Scaled Wind Farm Technology (SWiFT) facility. Large-eddy simulations (LES) of the SWiFT site are conducted using an actuator surface model to simulate the turbine structures to produce training and validation datasets of the CNN. The simulations are validated using the SpinnerLidar measurements of turbine wakes at the SWiFT site and the instantaneous and time-averaged velocity fields from the training LES are used to train the CNN. The trained CNN is then applied to predict 3D realizations of time-averaged velocity in the wake of the SWiFT turbines under flow conditions different than those for which the CNN was trained. LES results for the validation cases are used to evaluate the performance of the CNN predictions. Comparing the validation LES results and CNN predictions, we show that the developed CNN autoencoder model holds great potential for predicting time-averaged flow fields and the power production of wind turbines while being several orders of magnitude computationally more efficient than LES.

scholarly journals Influence of Wake Model Superposition and Secondary Steering on Model-Based Wake Steering Control with SCADA Data Assimilation

Energies ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 52
Author(s):  
Michael F. Howland ◽  
John O. Dabiri
Keyword(s):  
Wind Farm ◽  
Closed Loop ◽  
Power Production ◽  
Model Parameters ◽  
Closed Loop Control ◽  
Steering Control ◽  
Loop Control ◽  
Large Eddy ◽  
Wake Model ◽  
The Impact

Methods for wind farm power optimization through the use of wake steering often rely on engineering wake models due to the computational complexity associated with resolving wind farm dynamics numerically. Within the transient, turbulent atmospheric boundary layer, closed-loop control is required to dynamically adjust to evolving wind conditions, wherein the optimal wake model parameters are estimated as a function of time in a hybrid physics- and data-driven approach using supervisory control and data acquisition (SCADA) data. Analytic wake models rely on wake velocity deficit superposition methods to generalize the individual wake deficit to collective wind farm flow. In this study, the impact of the wake model superposition methodologies on closed-loop control are tested in large eddy simulations of the conventionally neutral atmospheric boundary layer with full Coriolis effects. A model for the non-vanishing lateral velocity trailing a yaw misaligned turbine, termed secondary steering, is also presented, validated, and tested in the closed-loop control framework. Modified linear and momentum conserving wake superposition methodologies increase the power production in closed-loop wake steering control statistically significantly more than linear superposition. While the secondary steering model increases the power production and reduces the predictive error associated with the wake model, the impact is not statistically significant. Modified linear and momentum conserving superposition using the proposed secondary steering model increase a six turbine array power production, compared to baseline control, in large eddy simulations by 7.5% and 7.7%, respectively, with wake model predictive mean absolute errors of 0.03P1 and 0.04P1, respectively, where P1 is the baseline power production of the leading turbine in the array. Ensemble Kalman filter parameter estimation significantly reduces the wake model predictive error for all wake deficit superposition and secondary steering cases compared to predefined model parameters.

Impact of ocean waves on offshore wind farm power production

2021 ◽  
Author(s):  
Porchetta Sara ◽  
Muñoz-Esparza Domingo ◽  
Munters Wim ◽  
van Beeck Jeroen ◽  
van Lipzig Nicole
Keyword(s):  
Wind Farm ◽  
Power Production ◽  
Ocean Waves ◽  
Offshore Wind ◽  
Offshore Wind Farm
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