Nacelle-mounted Lidar Research Articles

A method to correct for the effect of blockage and wakes on power performance measurements

Abstract. Wind turbine power performance measurements often occur at the perimeter of a wind farm, where the wind flow is subject to blockage effects, which might impact the measured power performance. We perform Reynolds-averaged Navier–Stokes simulations of a wind farm with five rows of 20 turbines each, operating in a conventionally neutral boundary layer, to evaluate whether the power performances measured for turbines in the upstream row would differ from that of a turbine operating in isolation under the same inflow conditions. We simulate the power performance measurements with both meteorological masts and nacelle-mounted lidars. Results show that blockage effects have an impact on the measured power performance of the wind farm turbines, with measured power coefficient varying more than 1 % relative to what is measured for the isolated turbine. In this work, we propose a method to correct for the effect of blockage on power performance measurements, yielding a curve that is more consistent with how power curves in energy yield analyses are defined and used, and thereby allowing for more useful comparisons between these curves. Our numerical results indicate that the correction method greatly reduces blockage-related variance and bias in the measured power curves. While flow modeling can be used to calculate the correction factors for actual power performance measurements in the field, we additionally show how some of the correction factors can be derived from lidar measurements. Finally, the numerical results suggest that the method could also be used to correct for the effect of wakes on power performance measurements conducted on turbines located downstream of the leading row.

Characterization of wind turbine flow through nacelle-mounted lidars: a review

This article provides a comprehensive review of the most recent advances in the planning, execution, and analysis of inflow and wake measurements from nacelle-mounted wind Doppler lidars. Lidars installed on top of wind turbines provide a holistic view of the inflow and wake characteristics required to characterize and optimize wind turbine performance, carry out model validation and calibration, and aid in real-time control. The need to balance the enhanced capabilities and limitations of lidars compared to traditional anemometers inspired a broad variety of approaches for scan design and wind reconstruction, which we discuss in this review. We give particular emphasis to identifying common guidelines and gaps in the available literature with the aim of providing an exhaustive picture of the state-of-the-art techniques for reconstructing wind plant flow using nacelle-mounted lidars.

Revealing inflow and wake conditions of a 6 MW floating turbine

Abstract. We investigate the characteristics of the inflow and the wake of a 6 MW floating wind turbine from the Hywind Scotland offshore wind farm, the world's first floating wind farm. We use two commercial nacelle-mounted lidars to measure the up- and downwind conditions with a fixed and a scanning measuring geometry, respectively. In the analysis, the effect of the pitch and roll angles of the nacelle on the lidar measuring location is taken into account. The upwind conditions are parameterized in terms of the mean horizontal wind vector at hub height, the shear and veer of the wind profile along the upper part of the rotor, and the induction of the wind turbine rotor. The wake characteristics are studied in two narrow wind speed intervals between 8.5–9.5 and 12.5–13.5 m s−1, corresponding to below and above rotor rated speeds, respectively, and for turbulence intensity values between 3.3 %–6.4 %. The wake flow is measured along a horizontal plane by a wind lidar scanning in a plan position indicator mode, which reaches 10 D downwind. This study focuses on the downstream area between 3 and 8 D. In this region, our observations show that the transverse profile of the wake can be adequately described by a self-similar wind speed deficit that follows a Gaussian distribution. We find that even small variations (∼1 %–2 %) in the ambient turbulence intensity can result in an up to 10 % faster wake recovery. Furthermore, we do not observe any additional spread of the wake due to the motion of the floating wind turbine examined in this study.

A Simple Model for Wake-Induced Aerodynamic Interaction of Wind Turbines

Wind turbine aerodynamic interactions within wind farms lead to significant energy losses. Optimizing the flow between turbines presents a promising solution to mitigate these losses. While analytical models offer a fundamental approach to understanding aerodynamic interactions, further development and refinement of these models are imperative. We propose a simplified analytical model that combines the Gaussian wake model and the cylindrical vortex induction model to evaluate the interaction between wake and induction zones in 3.5 MW wind turbines with 328 m spacing. The model’s validation is conducted using field data from a nacelle-mounted LiDAR system on the downstream turbine. The ‘Direction to Hub’ parameter facilitates a comparison between the model predictions and LiDAR measurements at distances ranging from 50 m to 300 m along the rotor axis. Overall, the results exhibit reasonable agreement in flow trends, albeit with discrepancies of up to 15° in predicting peak interactions. These deviations are attributed to the single-hat Gaussian shape of the wake model and the absence of wake expansion consideration, which can be revisited to improve model fidelity. The ‘Direction to Hub’ parameter proves valuable for model validation and LiDAR calibration, enabling a detailed flow analysis between turbines. This analytical modeling approach holds promise for enhancing wind farm efficiency by advancing our understanding of turbine interactions.

Holistic scan optimization of nacelle-mounted lidars for inflow and wake characterization at the RAAW and AWAKEN field campaigns

In this article, we provide a methodological framework for designing the scanning strategies of nacelle-mounted scanning lidars for wind energy field experiments, and apply it at two major experimental field campaigns. For the Rotor Aerodynamics, Aeroelastics, and Wake project (RAAW), we leverage two scanning lidars on one turbine to characterize the incoming turbulence and the turbine wake. For the American WAKE experimeNt (AWAKEN), we use four scanning lidars on top of four turbines in a large wind power plant to investigate both individual wakes and wind-plant-scale flow features.

High-fidelity retrieval from instantaneous line-of-sight returns of nacelle-mounted lidar including supervised machine learning

Abstract. Wind turbine applications that leverage nacelle-mounted Doppler lidar are hampered by several sources of uncertainty in the lidar measurement, affecting both bias and random errors. Two problems encountered especially for nacelle-mounted lidar are solid interference due to intersection of the line of sight with solid objects behind, within, or in front of the measurement volume and spectral noise due primarily to limited photon capture. These two uncertainties, especially that due to solid interference, can be reduced with high-fidelity retrieval techniques (i.e., including both quality assurance/quality control and subsequent parameter estimation). Our work compares three such techniques, including conventional thresholding, advanced filtering, and a novel application of supervised machine learning with ensemble neural networks, based on their ability to reduce uncertainty introduced by the two observed nonideal spectral features while keeping data availability high. The approach leverages data from a field experiment involving a continuous-wave (CW) SpinnerLidar from the Technical University of Denmark (DTU) that provided scans of a wide range of flows both unwaked and waked by a field turbine. Independent measurements from an adjacent meteorological tower within the sampling volume permit experimental validation of the instantaneous velocity uncertainty remaining after retrieval that stems from solid interference and strong spectral noise, which is a validation that has not been performed previously. All three methods perform similarly for non-interfered returns, but the advanced filtering and machine learning techniques perform better when solid interference is present, which allows them to produce overall standard deviations of error between 0.2 and 0.3 m s−1, or a 1 %–22 % improvement versus the conventional thresholding technique, over the rotor height for the unwaked cases. Between the two improved techniques, the advanced filtering produces 3.5 % higher overall data availability, while the machine learning offers a faster runtime (i.e., ∼ 1 s to evaluate) that is therefore more commensurate with the requirements of real-time turbine control. The retrieval techniques are described in terms of application to CW lidar, though they are also relevant to pulsed lidar. Previous work by the authors (Brown and Herges, 2020) explored a novel attempt to quantify uncertainty in the output of a high-fidelity lidar retrieval technique using simulated lidar returns; this article provides true uncertainty quantification versus independent measurement and does so for three techniques rather than one.

Impact of the wave/wind induced oscillations on the power performance of the WindFloat wind turbine

The main objective of this work is the characterization of the wave/wind induced oscillations on the power performance of the wind turbine operating on a WindFloat floating system. To assess the potential impact on the wind turbine power performance induced by these oscillations, the nacelle movements of the WindFloat wind turbine were monitored using accelerometer sensors synchronized with : 1) metocean data measured with a buoy; 2) wind turbine power data installed in the WindFloat floating system; and 3) wind speed data gathered from a nacelle-mounted LiDAR. Based on this data, a clustering analysis approach is proposed. No meaningful relationship between the ocean parameters and the nacelle movements or the wind power production could be established. The obtained results suggest that the dynamic adaptation of the drive train (mainly due to wind turbine torque control) to a fast oscillating (primary energy) moving force is the source of the largest oscillations in the nacelle of the WindFloat wind turbine. Nevertheless, results suggest that the wind/wave induced oscillations and their impact on the power performance of the WindFloat wind turbine is low considering its nominal capacity. Outcomes of this work were extremely relevant to demonstrate the stability of the WindFloat system, and, consequently, also important for the development of the floating wind offshore industry (and other technologies).

Lower Order Description and Reconstruction of Sparse Scanning Lidar Measurements of Wind Turbine Inflow Using Proper Orthogonal Decomposition

Preview measurements of the inflow by turbine-mounted lidar systems can be used to optimise wind turbine performance or alleviate structural loads. However, nacelle-mounted lidars suffer data losses due to unfavourable environmental conditions and laser beam obstruction by the rotating blades. Here, we apply proper orthogonal decomposition (POD) to the simulated line-of-sight wind speed measurements of a turbine-mounted scanning lidar obtained from two large eddy simulations. This work aimed at identifying the dominant POD modes that can be used to subsequently derive a reduced-order representation of the turbine inflow. Secondly, we reconstructed the data points lost due to blade passage by using Gappy-POD. We found that only a few modes are required to capture the dynamics of the wind field parameters commonly used for lidar-assisted wind turbine control, such as the effective wind speed, vertical shear and directional misalignment. By evaluating turbine-relevant metrics in the time and frequency domain, we found that a ten-mode reconstruction could accurately describe most spatio-temporal variations in the inflow. Furthermore, a modal interpretation is presented by direct comparison with these wind field parameters. We found that the Gappy-POD method performs substantially better than spatial interpolation techniques, accurately reconstructing up to even 50% of missing data. A POD-based wind field reconstruction offers a trade-off between wind field reconstruction techniques requiring flow assumptions and more complex physics-based representations, offers dimensional reduction and can overcome the blade passage limitation of nacelle-mounted lidar systems.

An improved data-driven methodology and field-test verification of yaw misalignment calibration on wind turbines

Yaw misalignment is being gradually recognized as a critical aspect for repowering aged wind turbines. For large-scale wind farms, the detection and calibration of yaw misalignment need to balance the relationship among accuracy, efficiency, and cost. This paper presents a data-driven yaw misalignment calibration method, and implements the field test with slight hardware modification. The specific procedures, including data preprocessing, regionalization and inference are listed step by step, which sustain the calibration direction and value of yaw misalignment calibration. Then, the historical data are extracted from system database of six adjacent commercial 2 MW wind turbines, and the proposed data-driven calibration method is applied, where the nacelle-mounted LiDAR is also introduced for auxiliary calibration and comparison. Field verification is implemented in these six wind turbines, through implementing pre-defined yaw offsets. The performance evaluations are conducted from different aspects, and compared with the same period of previous year. Consistent results show that both power production and yaw control accuracy are improved significantly, while the data-driven calibration method is with the same direction and acceptable deviation as the LiDAR. Hence, the proposed yaw misalignment calibration could be considered a propagable repowering technique with advantages of low cost, high efficiency, and acceptable accuracy.

Field Study of Turbulence Intensity measurement by Nacelle Mounted Lidar (NML)

In this paper, Nacelle Mounted LiDAR (NML) was used to study Turbulence Intensity (TI) by dataset from three measurement campaigns, using two approaches: the white box test and the black box test. The white box test was to compare TI LOS using each Line Of Sight, while the black box test was to compare TI Hub using all LOS by relative reconstruction algorithm at wind turbine hub height. The result of white box test showed that the correlation coefficient of TI LOS varied from 0.923 to 0.955 and the slope varied from 0.978 to 1.030. The result of black box test showed that the correlation coefficient of TI Hub is 0.8337∼0.8924 and the slope is 0.904∼0.935. This study provided some promising results to wind energy community, which could be the evidence base for the further investigation for the temporal-spatial turbulence structure in low Atmospheric Boundary Layer.

Evaluation of the “fan scan” based on three combined nacelle lidars for advanced wind field characterisation

Nacelle-mounted lidars are finding more and more applications in the wind industry. Their use cases range from power performance testing to lidar-assisted control. Depending on the application, different wind field parameters are reconstructed from the lidar’s line-of-sight wind measurements. Hereby, the respective scan geometry of the lidar determines the capabilities and limitations for the reconstruction of wind field parameters. In this study, we introduce the fan scan, a scanning pattern made of multiple nacelle-mounted lidars with the aim of an advanced wind field assessment to reconstruct more than three wind field parameters, which has been implemented at the Testfeld BHV site in Bremerhaven, Germany. Analytically derived uncertainties as well as calibration results for both, line-of-sight speeds and reconstructed wind field parameters, are presented indicating a good agreement with reference measurements. Additionally, a method for the reconstruction of the horizontal wind shear is introduced and applied to the fan scan data, highlighting the extended utilization possibilities of nacelle lidars.

Overcoming Blade Interference: A Gappy-POD Data Reconstruction Method for Nacelle-Mounted Lidar Measurements

Nacelle-mounted lidar systems suffer data loss due to unfavourable atmospheric conditions such as rain or fog and most importantly the rotation of the blades that obstruct the laser beam from measuring upstream of the turbine. In this paper, we apply Gappy Proper Orthogonal Decomposition (Gappy-POD) to reconstruct incomplete flow fields from nacelle-mounted lidar measurements. For this purpose, two scanning nacelle-based SpinnerLidar simulations are performed inside a Large Eddy Simulation, one measuring the undisturbed wind inflow and the other in the wake of a reference turbine. Data loss of up to 90 % is simulated by artificially removing measurement points. The performance of Gappy-POD in reconstructing the wind fields is evaluated by comparing metrics such as effective wind speeds, vertical shear, yaw misalignment, wake deficit, wake meandering and the turbulent spectra in fixed and rotating frames of reference. We see that Gappy-POD is capable of accurately reconstructing missing data in comparison to normally used spatial interpolation techniques even in cases where 90 % of the data was missing. As a result, the dynamics of the reconstructed wind fields can be investigated based on highly accurate lidar-based wind field retrievals. The methodology can be used as a tool to develop effective wind field reconstruction techniques from sparse data.

Evaluation of the global-blockage effect on power performance through simulations and measurements

Abstract. Blockage effects due to the interaction of five wind turbines in a row are investigated through both Reynolds-averaged Navier–Stokes simulations and site measurements. Since power performance tests are often carried out at sites consisting of several turbines in a row, the objective of this study is to evaluate whether the power performance of the five turbines differs from that of an isolated turbine. A number of simulations are performed, in which we vary the turbine inter-spacing (1.8, 2 and 3 rotor diameters) and the inflow angle between the incoming wind and the orthogonal line to the row (from 0 to 45∘). Different values of the free-stream velocity are considered to cover a broad wind speed range of the power curve. Numerical results show consistent power deviations for all five turbines when compared to the isolated case. The amplitude of these deviations depends on the location of the turbine within the row, the inflow angle, the inter-spacing and the power curve region of operation. We show that the power variations do not cancel out when averaging over a large inflow sector (from −45 to +45∘) and find an increase in the power output of up to +1 % when compared to the isolated case under idealised conditions (neutral atmospheric conditions, no vertical wind shear or ground effects). We simulate power performance “measurements” with both a virtual mast and nacelle-mounted lidar and find a combination of power output increase and upstream velocity reduction, which causes an increase of +4 % in the power coefficient under idealised conditions. We also use measurements from a real site consisting of a row of five wind turbines to validate the numerical results. From the analysis of the measurements, we also show that the power performance is impacted by the neighbouring turbines. Compared to when the inflow is perpendicular to the row, the power output varies by +1.8 % and −1.8 % when the turbine is the most downwind and upwind of the line, respectively.

Four-dimensional wind field generation for the aeroelastic simulation of wind turbines with lidars

Abstract. Lidar-assisted control (LAC) of wind turbines is a control concept that takes advantage of a nacelle-mounted lidar (a remote sensing device) to measure upstream wind speeds of a turbine to allow a preview of the incoming turbulence. Because the turbine will not be exposed to the identical turbulence as that measured by the lidar in advance, the simulation of a LAC system will be more realistic if wind evolution can be modeled in the wind field generation. Since the commonly used 3D stochastic wind field generation method does not include wind evolution, the main goal of this research is to extend the 3D method to 4D to enable the modeling of wind evolution along the wind direction. The most novel part of this research is that we propose a two-step Cholesky decomposition approach for the factorization of the coherence matrices in the wind field generation. With this approach, 4D wind fields can be generated by combining multiple statistically independent 3D wind fields. To enable better integration of the 4D method into the common workflow of wind turbine simulations, we implement the 4D method as the open-access tool evoTurb in combination with TurbSim and Mann turbulence generator. Moreover, since 4D wind field generation is supposed to be coupled with lidar simulations, and considering the range weighting effect of lidars and eventually multiple range gates, a 4D wind field will contain many more simulation points than a 3D one. To avoid excessive computational effort, we further investigate the impacts of the spatial discretization in 4D wind fields on lidar simulations to provide some insights to optimize the application of 4D wind field generation.

Experimental study on application of nacelle-mounted LiDAR for analyzing wind turbine wake effects by distance

The blade rotation of wind turbines causes the wake effect, which affects energy production of downstream wind turbines, and its magnitude and intensity vary with distance. To optimize the wind turbine layout for maximum efficiency, it is necessary to quantitatively evaluate the wake effect by distance. Using a nacelle LiDAR, it is possible to concurrently measure wind conditions at multiple positions. This study presents the results of analyzing the wake effects by distance, behind a 3 MW wind turbine, using a nacelle LiDAR. Wake wind data measured at 10 points by a nacelle LiDAR, mounted on the nacelle of a 1.5 MW downstream wind turbine, were analyzed against freestream wind data obtained from a 70-m-high met mast installed near the turbine. Variations in wind conditions in the wake region were analyzed as a function of distance and wind direction, and the influence of these factors on the power production of the downstream wind turbine was estimated using SCADA. The variations in wind speeds and turbulence intensities at multiple distances between 0.9D and 4.8D were quantitatively evaluated. Additionally, it was confirmed that the wake effects reduced the power performance by 23% at 5.7D under a wind speed of 8.5 m/s.

Feedforward control of a floating offshore wind turbine-generator system based on wind speed preview using a nacelle-mounted lidar

Feedforward control of a floating offshore wind turbine-generator system based on wind speed preview using a nacelle-mounted lidar

Parameterization of wind evolution using lidar

Abstract. Wind evolution, i.e., the evolution of turbulence structures over time, has become an increasingly interesting topic in recent years, mainly due to the development of lidar-assisted wind turbine control, which requires accurate prediction of wind evolution to avoid unnecessary or even harmful control actions. Moreover, 4D stochastic wind field simulations can be made possible by integrating wind evolution into standard 3D simulations to provide a more realistic simulation environment for this control concept. Motivated by these factors, this research aims to investigate the potential of Gaussian process regression in the parameterization of wind evolution. Wind evolution is commonly quantified using magnitude-squared coherence of wind speed and is estimated with lidar data measured by two nacelle-mounted lidars in this research. A two-parameter wind evolution model modified from a previous study is used to model the estimated coherence. A statistical analysis is done for the wind evolution model parameters determined from the estimated coherence to provide some insights into the characteristics of wind evolution. Gaussian process regression models are trained with the wind evolution model parameters and different combinations of wind-field-related variables acquired from the lidars and a meteorological mast. The automatic relevance determination squared exponential kernel function is applied to select suitable variables for the models. The performance of the Gaussian process regression models is analyzed with respect to different variable combinations, and the selected variables are discussed to shed light on the correlation between wind evolution and these variables.

Residual uncertainty in processed line-of-sight returns from nacelle-mounted lidar due to spectral artifacts

An uncertainty quantification technique for nacelle-mounted lidar is developed that extends conventional error analyses to precisely account for residual uncertainty due to observed non-ideal features in processed Doppler lidar spectra. The technique is applied after quality assurance/quality control (QAQC) processing to quantify residual error, both bias and random, from solid-body interference, shot noise, and any additional uncertainty introduced to the data from the QAQC process itself. The approach follows from the one-time construction of a high-dimensional parametric database of synthetic lidar spectra and subsequent processing with an existing QAQC technique. A model of the correspondence between the spectral shape and the associated residual errors due to non-ideal features is then developed for quantities of interest (QOIs) including the geometric median and spectral standard deviation of line-of-sight velocity. The model is preliminarily implemented within a neural network framework that is then applied in post-processing to sample returns from a DTU SpinnerLidar. The initial analysis uncovers the effects of specific sources of uncertainty in the context of both individual spectra and full-field maps of the measurement domain. The technique is described in terms of application to continuous wave (CW) lidar, though it is also relevant to pulsed lidar.

Comparison of different machine learning algorithms for prediction of wind evolution

Wind evolution, i.e. the evolution of turbulence structures over time, has become an increasingly interesting topic in recent years. Part of this increasing interest is resulted from the development of lidar-assisted wind turbine control, which requires accurate prediction of wind evolution to avoid unnecessary or even harmful control actions. Moreover, study of wind evolution can deepen our understanding of turbulence characteristics and, for example, could make 4D stochastic wind field simulations possible by integrating wind evolution into 3D simulations. This study compares different machine learning algorithms for the prediction of wind evolution, including three variants of Gaussian process regression, regression tree, support vector machine regression, and shallow neural network. The comparison is done using lidar data measured by a nacelle-mounted lidar, and it focuses on the prediction accuracy and the computational time of models, to provide some insights of the trade-off between these factors. In terms of computational time, both the training time of the final model and the time for hyperparameter optimization are taken into account. Moreover, up-scaling of the computational time with data size is also investigated. It is found that Gaussian process regression provides high prediction accuracy but needs long computational time, increasing significantly with data size. For cases where long computational time might be a critical problem, shallow neural network could be a good alternative thanks to its high training efficiency, although its prediction accuracy is lower than Gaussian process regression. The other methods have no advantages in wind evolution prediction compared to these two methods.

Dynamic wake meandering model calibration using nacelle-mounted lidar systems

Abstract. Light detection and ranging (lidar) systems have gained a great importance in today's wake characteristic measurements. The aim of this measurement campaign is to track the wake meandering and in a further step to validate the wind speed deficit in the meandering frame of reference (MFR) and in the fixed frame of reference using nacelle-mounted lidar measurements. Additionally, a comparison of the measured and the modeled wake degradation in the MFR was conducted. The simulations were done with two different versions of the dynamic wake meandering (DWM) model. These versions differ only in the description of the quasi-steady wake deficit. Based on the findings from the lidar measurements, the impact of the ambient turbulence intensity on the eddy viscosity definition in the quasi-steady deficit has been investigated and, subsequently, an improved correlation function has been determined, resulting in very good conformity between the new model and the measurements.