Giacomo Valerio Iungo

Field Measurements of Wind Turbine Wakes with Lidars

AbstractField measurements of the wake flow produced from a 2-MW Enercon E-70 wind turbine were performed using three scanning Doppler wind lidars. A GPS-based technique was used to determine the position of the wind turbine and the wind lidar locations, as well as the direction of the laser beams. The lidars used in this study are characterized by a high spatial resolution of 18 m, which allows the detailed characterization of the wind turbine wake. Two-dimensional measurements of wind speed were carried out by scanning a single lidar over the vertical symmetry plane of the wake. The mean axial velocity field was then retrieved by averaging 2D scans performed consecutively. To investigate wake turbulence, single lidar measurements were performed by staring the laser beam at fixed directions and using the maximum sampling frequency. From these tests, peaks in the velocity variance are detected within the wake in correspondence of the turbine top tip height; this enhanced turbulence could represent a sourc...

Volumetric Lidar Scanning of Wind Turbine Wakes under Convective and Neutral Atmospheric Stability Regimes

AbstractOptimization of a wind farm’s layout is a strategic task to reduce wake effects on downstream turbines, thus maximizing wind power harvesting. However, downstream evolution and recovery of each wind turbine wake are strongly affected by the characteristics of the incoming atmospheric boundary layer (ABL) flow, such as the vertical profiles of the mean wind velocity and the turbulence intensity, which are in turn affected by the ABL thermal stability. Therefore, the characterization of the variability of wind turbine wakes under different ABL stability regimes becomes fundamental to better predict wind power harvesting and to improve wind farm efficiency. To this aim, wind velocity measurements of the wake produced by a 2-MW Enercon E-70 wind turbine were performed with three scanning Doppler wind lidars. One lidar was devoted to the characterization of the incoming wind—in particular, wind velocity, shear, and turbulence intensity at the height of the rotor disc. The other two lidars performed vol...

3D Turbulence Measurements Using Three Synchronous Wind Lidars: Validation against Sonic Anemometry

AbstractThis paper presents a technique to measure the time series of the three components of the wind vector at a point in space from synchronous measurements of three scanning Doppler wind lidars. Knowing the position of each lidar on the ground and the orientation of each laser beam allows for reconstructing the three components of the wind velocity vector. The laser beams must intersect at the desired point in space and their directions must be noncoplanar, so that trigonometric relationships allow the reconstruction of the velocity vector in any coordinate system.This technique has been tested during a measurement campaign carried out at Cabauw’s Experimental Site for Atmospheric Research (CESAR) in the Netherlands and compared against measurements from sonic anemometers installed in a meteorological mast. The spatial resolutions of both measurement techniques differ by one order of magnitude. Therefore, in order to properly compare the results, a pseudospatial filter that mimics the attenuation indu...

Assessing State-of-the-Art Capabilities for Probing the Atmospheric Boundary Layer: The XPIA Field Campaign

AbstractTo assess current capabilities for measuring flow within the atmospheric boundary layer, including within wind farms, the U.S. Department of Energy sponsored the eXperimental Planetary boundary layer Instrumentation Assessment (XPIA) campaign at the Boulder Atmospheric Observatory (BAO) in spring 2015. Herein, we summarize the XPIA field experiment, highlight novel measurement approaches, and quantify uncertainties associated with these measurement methods. Line-of-sight velocities measured by scanning lidars and radars exhibit close agreement with tower measurements, despite differences in measurement volumes. Virtual towers of wind measurements, from multiple lidars or radars, also agree well with tower and profiling lidar measurements. Estimates of winds over volumes from scanning lidars and radars are in close agreement, enabling the assessment of spatial variability. Strengths of the radar systems used here include high scan rates, large domain coverage, and availability during most precipita...

Data-driven Reduced Order Model for prediction of wind turbine wakes

In this paper a new paradigm for prediction of wind turbine wakes is proposed, which is based on a reduced order model (ROM) embedded in a Kalman filter. The ROM is evaluated by means of dynamic mode decomposition performed on high fidelity LES numerical simulations of wind turbines operating under different operational regimes. The ROM enables to capture the main physical processes underpinning the downstream evolution and dynamics of wind turbine wakes. The ROM is then embedded within a Kalman filter in order to produce a time-marching algorithm for prediction of wind turbine wake flows. This data-driven algorithm enables data assimilation of new measurements simultaneously to the wake prediction, which leads to an improved accuracy and a dynamic update of the ROM in presence of emerging coherent wake dynamics observed from new available data. Thanks to its low computational cost, this numerical tool is particularly suitable for real-time applications, control and optimization of large wind farms.

Volumetric scans of wind turbine wakes performed with three simultaneous wind LiDARs under different atmospheric stability regimes

Aerodynamic optimization of wind farm layout is a crucial task to reduce wake effects on downstream wind turbines, thus to maximize wind power harvesting. However, downstream evolution and recovery of wind turbine wakes are strongly affected by the characteristics of the incoming atmospheric boundary layer (ABL) flow, such as wind shear and turbulence intensity, which are in turn affected by the ABL thermal stability. In order to characterize the downstream evolution of wakes produced by full-scale wind turbines under different atmospheric conditions, wind velocity measurements were performed with three wind LiDARs. The volumetric scans are performed by continuously sweeping azimuthal and elevation angles of the LiDARs in order to cover a 3D volume that includes the wind turbine wake. The minimum wake velocity deficit is then evaluated as a function of the downstream location for different atmospheric conditions. It is observed that the ABL thermal stability has a significant effect on the wake evolution, and the wake recovers faster under convective conditions.

Data-driven RANS for simulations of large wind farms

In the wind energy industry there is a growing need for real-time predictions of wind turbine wake flows in order to optimize power plant control and inhibit detrimental wake interactions. To this aim, a data-driven RANS approach is proposed in order to achieve very low computational costs and adequate accuracy through the data assimilation procedure. The RANS simulations are implemented with a classical Boussinesq hypothesis and a mixing length turbulence closure model, which is calibrated through the available data. High-fidelity LES simulations of a utility-scale wind turbine operating with different tip speed ratios are used as database. It is shown that the mixing length model for the RANS simulations can be calibrated accurately through the Reynolds stress of the axial and radial velocity components, and the gradient of the axial velocity in the radial direction. It is found that the mixing length is roughly invariant in the very near wake, then it increases linearly with the downstream distance in the diffusive region. The variation rate of the mixing length in the downstream direction is proposed as a criterion to detect the transition between near wake and transition region of a wind turbine wake. Finally, RANS simulations were performed with the calibrated mixing length model, and a good agreement with the LES simulations is observed.

Towards reduced order modelling for predicting the dynamics of coherent vorticity structures within wind turbine wakes

The dynamics of the velocity field resulting from the interaction between the atmospheric boundary layer and a wind turbine array can affect significantly the performance of a wind power plant and the durability of wind turbines. In this work, dynamics in wind turbine wakes and instabilities of helicoidal tip vortices are detected and characterized through modal decomposition techniques. The dataset under examination consists of snapshots of the velocity field obtained from large-eddy simulations (LES) of an isolated wind turbine, for which aerodynamic forcing exerted by the turbine blades on the atmospheric boundary layer is mimicked through the actuator line model. Particular attention is paid to the interaction between the downstream evolution of the helicoidal tip vortices and the alternate vortex shedding from the turbine tower. The LES dataset is interrogated through different modal decomposition techniques, such as proper orthogonal decomposition and dynamic mode decomposition. The dominant wake dynamics are selected for the formulation of a reduced order model, which consists in a linear time-marching algorithm where temporal evolution of flow dynamics is obtained from the previous temporal realization multiplied by a time-invariant operator. This article is part of the themed issue ‘Wind energy in complex terrains’.

Reduced order model for optimization of power production from a wind farm

This paper focuses on the optimization of power production from a column of wind turbines aligned with the mean wind direction. Optimal settings of the wind turbines are estimated through dynamic programming. The optimization is conducted using a reduced order model that estimates the downstream evolution of wind turbine wakes with high level of accuracy and low computational costs. The model, which is the main contribution of this paper, consists in a data-driven Reynolds-averaged Navier-Stokes (RANS) algorithm calibrated through Large Eddy Simulation (LES) data. The RANS model is first assessed against LES data for a single turbine operating with different tip speed ratios. Then, the optimal settings of a column of four wind turbines are estimated from this model through dynamic programming. Finally, the cumulative power curve of the wind turbine column is obtained.

Effects of Incoming Wind Condition and Wind Turbine Aerodynamics on the Hub Vortex Instability

Dynamics and instabilities occurring in the near-wake of wind turbines have a crucial role for the wake downstream evolution, and for the onset of far-wake instabilities. Furthermore, wake dynamics significantly affect the intra-wind farm wake flow, wake interactions and potential power losses. Therefore, the physical understanding and predictability of wind turbine wake instabilities become a nodal point for prediction of wind power harvesting and optimization of wind farm layout. This study is focused on the prediction of the hub vortex instability encountered within wind turbine wakes under different operational conditions of the wind turbine. Linear stability analysis of the wake flow is performed by means of a novel approach that enables to take effects of turbulence on wake instabilities into account. Stability analysis is performed by using as base flow the time-averaged wake velocity field at a specific downstream location. The latter is modeled through Carton-McWilliams velocity profiles by mimicking the presence of the hub vortex and helicoidal tip vortices, and matching the wind turbine thrust coefficient predicted through the actuator disc model. The results show that hub vortex instability is promoted by increasing the turbine thrust coefficient. Indeed, a larger aerodynamic load produces an enhanced wake velocity deficit and axial shear, which are considered the main sources for the wake instability. Nonetheless, wake swirl also promotes hub vortex instability, and it can also affect the azimuthal wavenumber of the most unstable mode.