To what extent turbulence characteristics of wind in the atmosphere transfer to wind turbines in terms of power, loads, etc. is of great interest throughout the research community. An experimental approach using a model wind turbine at laboratory scale was used in a wind tunnel study. It is shown that the gustiness of the wind remains present in the wind turbine data, stressing the importance of including those wind characteristics in industry standards and when designing wind turbines.

The certification process of a wind turbine requires simulations of a coupled structural and aerodynamic wind turbine model in many different external conditions. Due to the large number of load cases, the complexity of the aerodynamics models has to be limited. In this paper, a simplified vortex method based aerodynamics model is described. It is shown that this model, which is fast enough for use in a certification context, can produce results similar to those of a more complex vortex model.

The series of GABLS model intercomparison benchmarks is revisited in the context of wind energy atmospheric boundary layer (ABL) models. GABLS 1 and 2 are used for verification purposes. Then GABLS 3 is used to develop a methodology for using realistic mesoscale forcing for microscale ABL models. The method also uses profile nudging to dynamically reduce the bias. Different data assimilation strategies are discussed based on typical instrumentation setups of wind energy campaigns.

As wind turbines extract energy from the wind, a wind field of reduced wind speed and increased turbulence is left behind for the downstream turbines. For the exact calculation of the annual energy production and lifetime of wind turbines, it is therefore of great importance to be able to accurately calculate this turbulent wake flow for different wind conditions. This paper compares different computational modeling approaches with flow measurements on model turbines in a wind tunnel.

Remote-sensing devices such as lidars are often used for wind energy studies. Lidars measure mean wind speeds accurately but measure different values of turbulence than an instrument on a tower. In this paper, a model is described that improves lidar turbulence estimates. The model can be applied to commercially available lidars in real time or post-processing. Results indicate that the model performs well under most atmospheric conditions but retains some errors under daytime conditions.

In this study we aimed to use the spinner anemometer calibration and nacelle transfer function determined on a reference wind turbine to assess the power performance of a second, identical turbine. An experiment was set up with a met mast and spinner anemometer on each turbine. For each wind turbine, the nacelle power curve agreed with the corresponding power curve within 0.10 % of AEP for the reference wind turbine and within 0.38 % for the second wind turbine, for a mean wind speed of 8 m s⁻¹.

This paper demonstrates optimization of wind turbine locations within a utility-scale wind plant using a nonlinear flow model and gradient-based optimization techniques made possible through the use of adjoints. This represents a groundbreaking improvement in model fidelity and optimization efficiency for wind energy applications. The optimized wind farms demonstrate significant improvements in annual energy production with turbine layouts that take advantage of nonlinear flow curvature effects.

Nacelle lidars are nowadays extensively used to scan the turbine inflow. Thus, it is important to characterize turbulence from their measurements. We present two methods to perform turbulence estimation and demonstrate them using two types of lidars. With one method we can estimate the along-wind unfiltered variance accurately. With the other we can estimate the filtered radial velocity variance accurately and velocity-tensor parameters under neutral and high wind-speed conditions.

The aim of this paper is to show that with an automated tuning strategy, wind turbine control performance can be significantly increased. To this end, iterative feedback tuning (IFT) is applied to two different turbine controllers. The results obtained by high-fidelity simulations indicate significant performance improvements over baseline controllers. It is concluded that IFT of turbine controllers has the potential to become a valuable tool for improving wind turbine performance.

Efficient detection of wind turbines operating below their expected power output and immediate corrections help maximize asset value. The method presented estimates the environmental conditions from turbine states and uses pre-calculated power lookup tables from a numeric wake model to predict the expected power output. Deviations between the expected and the measured power output are an indication of underperformance. A demonstration of the method's ability to detect underperformance is given.

Here we give a basic form for uncertainty in mean wind speed predicted at one site via measurements taken at another site due to uncertainty in surface roughness when using industry-standard European Wind Atlas (e.g., WAsP) method. We also provide an approximate power-curve form and method to further estimate uncertainty in turbine energy production; this is also useful in AEP estimates. Some implications are also discussed, e.g., prediction over forest or with mesoscale model output.

Understanding uncertainties in wind resource assessment associated with the use of the output from numerical weather prediction (NWP) models is important for wind energy applications. A better understanding of the sources of error reduces risk and lowers costs. Here, an intercomparison of the output from 25 NWP models is presented. The study shows that model errors are larger and agreement between models smaller at inland sites and near the surface.

In this paper, a field test of wake-steering control is presented. In the campaign, an array of turbines within an operating commercial offshore wind farm have the normal yaw controller modified to implement wake steering according to a yaw control strategy. Results indicate that, within the certainty afforded by the data, the wake-steering controller was successful in increasing power capture.

Trailing edge flaps applied to the outer part of a wind turbine rotor blade are a very promising concept to reduce fatigue loads as they are able to increase or decrease the airfoil lift for a given angle of attack. They have been widely researched on 2-D airfoils, but only little is known about their aerodynamic characteristics on 3-D wind turbine rotor blades. The present article addresses this issue.

This work provides a possible solution to closed-loop flow control in a wind farm. The remote sensing technology, lidar, which is a laser-based measurement system, is used to obtain wind speed information behind a wind turbine. The measurements are processed using a model-based approach to estimate position information of the wake. The information is then used in a controller to redirect the wake to the desired position. Altogether, the concept aims to increase the power output of a wind farm.

The paper describes an innovative methodology to extract useful wind field estimates from remote sensing instruments – called lidars – mounted on the nacelle of a wind turbine. We used lidar measurements at multiple distances upstream and close to the wind turbine rotor in order to retrieve free-stream wind characteristics. The methodology and the obtained results open new paths to assess the power performance of wind turbines, which is essential to ensure financially viable wind farm projects.

In recent years, wind farms have grown in size and are more frequently placed in wind farm clusters. This means that large-scale effects such as the interaction of the Coriolis force and wind farm wakes are becoming more important for designing energy efficient wind farms. The literature disagrees on the turning direction of a wind farm wake due to the Coriolis force. In this article, we explain why the Coriolis force turns a wind farm wake clockwise in the Northern Hemisphere.

We use upwind and nacelle-based measurements from a wind turbine and investigate the influence of atmospheric stability and turbulence regimes on nacelle transfer functions (NTFs) used to correct nacelle-mounted anemometer measurements. This work shows that correcting nacelle winds using NTFs results in similar energy production estimates to those obtained using upwind tower-based wind speeds. Further, stability and turbulence metrics have been found to have an effect on NTFs below rated speed.

Actuator disc theory is the basis of most rotor design methods. However, the off-design condition of a very low rotational speed Ω is still a topic for scientific discussions. A small modification in the momentum theory for Joukowsky discs with uniform circulation results in the efficiency going to 0 instead of ∞ when Ω goes to 0. For high values of Ω, the classical momentum theory is reproduced. The new theory is confirmed by a very good match with numerically obtained results.

Vertical axis wind turbines (VAWTs) operate through inherently unsteady aerodynamics, unlike their horizontal axis counterparts (HAWTs). This greatly affects the structure of the wake, i.e., the region of velocity deficit and increased turbulence downstream of the machine. In this work, we use an advanced vortex method to identify the flow structures and instabilities at work in the decay of a VAWT wake, a crucial step if one wishes to optimize this decay or perform the design of VAWT farms.

We conducted measurements in a wind tunnel with the remote sensing technique lidar to map the flow around a row of three model wind turbines. Two lidars were positioned near the wind tunnel walls to measure the two-dimensional wind vector over a defined scanning line or area without influencing the flow itself. A comparison of the lidar measurements with a hot-wire probe and a thorough uncertainty analysis confirmed the usefulness of lidar technology for such flow measurements in a wind tunnel.

Coupling between bending and twist has a significant influence on the aeroelastic response of wind turbine blades. The coupling can arise from the blade geometry or from the anisotropic properties of the blade material. Bend–twist coupling can be utilised to reduce the fatigue loads of wind turbine blades. In this study the effects of material-based coupling on the aeroelastic modal properties and stability limits of the DTU 10 MW Reference Wind Turbine are investigated.