Nacelle Lidar Research Articles

Flow observations using nacelle lidars: A study on the University of Stavanger campus

Abstract This study deals with wind measurements on the campus of the University of Stavanger, using two continuous-wave nacelle lidars and a vertical continuous-wave profiler. Wind data is further acquired by 2-D sonic anemometers fixed to the lidars. The emphasis of the analysis is on the data gathered by the nacelle lidars. The horizontal wind speeds reconstructed from the radial velocities are compared to the recordings of the sonic anemometers and analysed in terms of mean wind profiles over the extent of the scanning circle. Standard logarithmic and power laws are fitted to the profiles to estimate site-specific parameters such as wind shear exponent and surface roughness. Turbulence characteristics in the mean wind direction, such as spectra and variances, are estimated and compared to those from the sonic anemometer. The study demonstrates the overall potential of remote sensing for wind monitoring in an urban environment. The wind velocities acquired in the individual measurement points along the nacelle lidars’ scanning circles are found to capture the flow in the monitored regions around the buildings in a realistic way. Together with the information from the vertically pointing wind profiler, the measurement data represents a valuable source for validation of related numerical models for flow in an urban area.

Impact of motions on floating wind turbine power production

Floating offshore wind opens new possibilities for harnessing wind energy in deeper waters where it is not feasible to install traditional fixed-bottom turbines. Accessing deeper waters enables the utilization of stronger and more consistent wind resources, potentially leading to higher energy production. However, one of the challenges of floating offshore wind is the impact of increased motions on floating turbine’s power. This paper addresses this challenge by investigating wind field reconstruction and motion compensation algorithms when using a nacelle lidar to characterize floating turbine inflow wind speed. The fully instrumented TetraSpar floating demonstrator with a 3.6 MW wind turbine and a nacelle lidar, offers a unique opportunity to investigate the effect of motions on power production. Observations from real measurements are complemented with numerical simulations, highlighting that motion-compensating for mean tilt angle is an effective correction for 10 min average power performance measurements. Results showed that mean tilt angles causing the lidar beams to shift upwards, result in overestimation of the estimated hub height wind speed if no motion compensation is applied. The paper also assesses the impact of motions induced by different sea states on power production.

Detecting abnormal small-scale events by nacelle lidar at the AD8-180 prototype turbine

Identifying anomalous wind events that lead to high aerodynamic loads is essential for mitigating extreme loading on wind turbines. Building upon a recently published method, we herein introduce an extended methodology for early small-scale abnormal wind event detection employing a pulsed nacelle-lidar, potentially triggering preemptive control actions within the wind turbine system. The extended method integrates the consideration of multiple measurement ranges within the induction zone and the evaluation of a horizontally asymmetric wind detection parametrized through a difference in lidar line-of-sight (LoS) measurements. A comparative assessment of various detection algorithms is conducted, leading to the identification of optimal parameter configurations and an evaluation of their reliability. With the proposed methodology, the false positive rate can be reduced with respect to the original methodology, although not all extreme loading events are detected.

Impact of floating turbine motion on nacelle lidar turbulence measurements

We determine the impact of floating turbine motion on turbulence measurements from a four-beam lidar by emulating its scanning configuration and retrievals within atmospheric turbulence boxes. Since the elevation angle of the lidar beams is small and the two bottom lidar beams point closely to the horizontal plane, we also evaluate the turbulence estimation abilities of a two-beam lidar. For the two-beam nacelle lidar, the variance of the individual beams is close to the target u-variance and closer than that we compute by reconstructing the u-velocity component with the two lidar beams radial velocities. By using floating turbine motion measurements from Hywind, we show that the floating turbine motion impacts turbulence estimations of the nacelle lidar. Roll does not have a clear impact on nacelle-lidar turbulence, whereas both the beam and the u-reconstructed variances increase with pitch amplitude.

Feedforward pitch control for a 15 MW wind turbine using a spinner-mounted single-beam lidar

Abstract. Feedforward blade pitch control is one of the most promising lidar-assisted control strategies due to its significant improvement in rotor speed regulation and fatigue load reduction. A high-quality preview of the rotor-effective wind speed is a key element of control benefits. In this work, a single-beam lidar is simulated in the spinner of a bottom-fixed IEA 15 MW wind turbine. Both continuous-wave (CW) and pulsed lidar systems are considered. The single-beam lidar can rotate with the wind turbine rotor and scan the inflow with a circular pattern, which mimics a multiple-beam nacelle lidar at a lower cost. Also, the spinner-based lidar has an unimpeded view of the inflow without intermittent blockage from the rotating blade. The focus distance and the cone angle of the spinner-based single-beam lidar are optimized for the best wind preview quality based on a rotor-effective wind speed coherence model. Then, the control benefits of using the optimized spinner-based lidar are evaluated for an above-rated wind speed in OpenFAST with an embedded lidar simulator and virtual four-dimensional Mann turbulence fields considering the wind evolution. Results are compared against those using a single-beam nacelle-based lidar. We found that the optimum scanning configurations of both CW and pulsed spinner-based single-beam lidars lead to a lidar scan radius of 0.6 of the rotor radius. Also, results show that a single-beam lidar mounted in the spinner provides many more control benefits (i.e. better rotor speed regulations and higher reductions in the damage equivalent loads on the tower base and blade roots) than the one based on the nacelle. The spinner-based single-beam lidar has a similar performance to a four-beam nacelle lidar when used for feedforward control.

Dependence of turbulence estimations on nacelle lidar scanning strategies

Abstract. Through numerical simulations and the analysis of field measurements, we investigate the dependence of the accuracy and uncertainty of turbulence estimations on the main features of the nacelle lidars' scanning strategy, i.e., the number of measurement points, the half-cone opening angle, the focus distance and the type of the lidar system. We assume homogeneous turbulence over the lidar scanning area in front of a Vestas V52 wind turbine. The Reynolds stresses are computed via a least-squares procedure that uses the radial velocity variances of each lidar beam without the need to reconstruct the wind components. The lidar-retrieved Reynolds stresses are compared with those from a sonic anemometer at turbine hub height. Our findings from the analysis of both simulations and measurements demonstrate that to estimate the six Reynolds stresses accurately, a nacelle lidar system with at least six beams is required. Further, one of the beams of this system should have a different opening angle. Adding one central beam improves the estimations of the velocity components' variances. Assuming the relations of the velocity components' variances as suggested in the IEC standard, all considered lidars can estimate the along-wind variance accurately using the least-squares procedure and the Doppler radial velocity spectra. Increasing the opening angle increases the accuracy and reduces the uncertainty on the transverse components, while enlarging the measurement distance has opposite effects. All in all, a six-beam continuous-wave lidar measuring at a close distance with a large opening angle provides the best estimations of all Reynolds stresses. This work gives insights on designing and utilizing nacelle lidars for inflow turbulence characterization.

Numerical evaluation of multivariate power curves for wind turbines in wakes using nacelle lidars

The IEC standards describe how to measure the power performance of an isolated wake-free wind turbine. However, most wind turbines operate under waked conditions for a substantial amount of time, calling for the need of a new methodology for power performance evaluation. We define multivariate power curves in the form of multivariate polynomial regressions, whose input variables are several wind speed and turbulence measurements retrieved with nacelle lidars. We use a dataset of synthetic power performance tests including both waked and wake-free conditions. The dataset is generated through aeroelastic simulations combined with both virtual nacelle lidars and the dynamic wake meandering model. A feature-selection algorithm is used to select the input variables among the available measurements, showing that the optimal model includes four input variables: three correspondent to wind speed and one to turbulence measures. Additionally, we give insights on the optimal nacelle-lidar scanning geometry needed to implement the multivariate power curve. Results show that the multivariate power curves predict the power output with accuracy of the same order under both waked and wake-free operation. For the in-wake cases, the accuracy is much higher than that of the IEC standard power curve, with an error reduction of up to 88%.

The space-time structure of turbulence for lidar-assisted wind turbine control

Lidar-assisted wind turbine control has been proven to be beneficial for turbine load reduction. It relies on the preview of incoming turbulence provided by a nacelle lidar, which allows the turbine controller to react to the flow disturbances before their impact on the turbine. When assessing its benefits, previous studies mainly use the standard turbulence parameters suggested by the IEC 61400-1 standard and assume Taylor's frozen hypothesis. Based on atmospheric turbulence observations, the parameters of the Mann spectral turbulence model differ significantly from the values recommended in the standard, and they vary, e.g., with atmospheric stability. Also, turbulence evolution from upstream to the rotor position conflicts with the frozen turbulence hypothesis, and its effect on the preview quality by a nacelle lidar still needs quantification. This work presents a simple method to extend the Mann model to account for the temporal evolution of turbulence using a space-time spectral velocity tensor. Under various atmospheric stability conditions, we derive the space-time tensor parameters and evaluate the space-time tensor using data from a five-beam lidar and a meteorological mast, we found good agreement between the space-time tensor and the measurement data. In addition, a numerical method for simulating a four-dimensional (4D) space-time velocity field based on the space-time tensor is presented. In the end, we analyze the importance of including the temporal evolution of turbulence for assessing lidar wind preview quality.

Influence of nacelle-lidar scanning patterns on inflow turbulence characterization

Nacelle lidars with different number of beams, scanning configurations and focus distances are simulated for characterizing the inflow turbulence. Lidar measurements are simulated within 100 turbulence wind fields described by the Mann model. The reference wind turbine has a rotor diameter of 52 m. We assume homogeneous frozen turbulence over the lidar scanning area. The lidar-derived Reynolds stresses are computed from a least-square procedure that uses radial velocity variances of each of the beams and compared with those from a simulated sonic anemometer at turbine hub height. Results show that at least six beams, including one beam with a different opening angle, are needed to estimate all Reynolds stresses. Enlarging the beam opening angle improves the accuracy and uncertainty in turbulence estimation more than increasing the number of beams. All simulated lidars can estimate the along-wind variance accurately. This work provides guidance on designing and utilizing nacelle lidars for inflow turbulence characterization.

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.

Wind turbine power performance characterization through aeroelastic simulations and virtual nacelle lidar measurements

The power performance of a wind turbine depends on several characteristics of the inflow, such as wind speed, turbulence and wind shear. Additionally, wind turbine control strategies affect the power performance of the wind turbines; under certain conditions one might want to, e.g., intentionally misalign the turbine with respect to the main wind direction. Here, we evaluate whether the accuracy in power performance evaluation can be improved by using multi-dimensional power curves in the form of multivariate polynomial regressions, which define the power output as function of wind speed, turbulence and yaw misalignment. The analysis is conducted on a dataset of virtual power performance measurements, which is generated through aeroelastic simulations combined with a simulator of nacelle lidar measurements. Results show that the multi-dimensional power curves can provide higher accuracy than those derived using the IEC standard for power curve measurements; the error in power prediction is nearly halved compared to that using the IEC standard power curve method. Additionally, we show that nacelle lidar measurements increase the accuracy of the multi-dimensional power curves when compared to using mast-based anemometer measurements.

Power curve measurement of a floating offshore wind turbine with a nacelle-based lidar

Understanding the power performance of floating offshore wind turbines is essential for the economics of floating wind, which requires reliable wind speed measurements. This work investigates the use of nacelle-based lidar technology for power curve measurement of a floating offshore wind turbine. In a five-month long measurement campaign, a nacelle-based lidar was installed on top of a 2MW floating offshore wind turbine. It was used to reconstruct the wind field including a carrier-to-noise ratio threshold filter, an availability filter and a hard target filter for the one hertz line of sight measurements. The quality of lidar wind speed estimations were compared to nacelle-based sonic anemometer measurements in terms of their linear correlation and root mean squared error of ten minute averages. The correlation of rotor averaged wind speed estimation of lidar and sonic wind speed measurements showed a 0.97 coefficient of determination. Variability of correlation is investigated in terms of nacelle excitations to identify the uncertainty related to the use of nacelle lidars for floating wind applications. Findings show that the quality of correlation decreased with higher nacelle excitations, which are caused by both wind and waves. Consequently, wind speed estimations were used for power curve measurement to investigate the qualification of using a nacelle-based lidar for such an application. Although lidar measurements showed higher scatter, they provided a reasonable wind speed estimate and a similar power curve to sonic measurements.

Turbulence statistics from three different nacelle lidars

Turbulence statistics from three different nacelle lidars

Turbulence statistics from three different nacelle lidars

Abstract. Atmospheric turbulence can be characterized by the Reynolds stress tensor, which consists of the second-order moments of the wind field components. Most of the commercial nacelle lidars cannot estimate all components of the Reynolds stress tensor due to their limited number of beams; most can estimate the along-wind velocity variance relatively well. Other components are however also important to understand the behavior of, e.g., the vertical wind profile and meandering of wakes. The SpinnerLidar, a research lidar with multiple beams and a very high sampling frequency, was deployed together with two commercial lidars in a forward-looking mode on the nacelle of a Vestas V52 turbine to scan the inflow. Here, we compare the lidar-derived turbulence estimates with those from a sonic anemometer using both numerical simulations and measurements from a nearby mast. We show that from these lidars, the SpinnerLidar is the only one able to retrieve all Reynolds stress components. For the two- and four-beam lidars, we study different methods to compute the along-wind velocity variance. By using the SpinnerLidar's Doppler spectra of the radial velocity, we can partly compensate for the lidar's probe volume averaging effect and thus reduce the systematic error of turbulence estimates. We find that the variances of the radial velocities estimated from the maximum of the Doppler spectrum are less affected by the lidar probe volume compared to those estimated from the median or the centroid of the Doppler spectrum.

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.

Results from a wake-steering experiment at a commercial wind plant: investigating the wind speed dependence of wake-steering performance

Abstract. Wake steering is a wind farm control strategy in which upstream wind turbines are misaligned with the wind to redirect their wakes away from downstream turbines, thereby increasing the net wind plant power production and reducing fatigue loads generated by wake turbulence. In this paper, we present results from a wake-steering experiment at a commercial wind plant involving two wind turbines spaced 3.7 rotor diameters apart. During the 3-month experiment period, we estimate that wake steering reduced wake losses by 5.6 % for the wind direction sector investigated. After applying a long-term correction based on the site wind rose, the reduction in wake losses increases to 9.3 %. As a function of wind speed, we find large energy improvements near cut-in wind speed, where wake steering can prevent the downstream wind turbine from shutting down. Yet for wind speeds between 6–8 m/s, we observe little change in performance with wake steering. However, wake steering was found to improve energy production significantly for below-rated wind speeds from 8–12 m/s. By measuring the relationship between yaw misalignment and power production using a nacelle lidar, we attribute much of the improvement in wake-steering performance at higher wind speeds to a significant reduction in the power loss of the upstream turbine as wind speed increases. Additionally, we find higher wind direction variability at lower wind speeds, which contributes to poor performance in the 6–8 m/s wind speed bin because of slow yaw controller dynamics. Further, we compare the measured performance of wake steering to predictions using the FLORIS (FLOw Redirection and Induction in Steady State) wind farm control tool coupled with a wind direction variability model. Although the achieved yaw offsets at the upstream wind turbine fall short of the intended yaw offsets, we find that they are predicted well by the wind direction variability model. When incorporating the expected yaw offsets, estimates of the energy improvement from wake steering using FLORIS closely match the experimental results.

Experimental investigation on power performance testing using nacelle lidar measurements over excavated terrain

Abstract In order to find out the usability of the nacelle mounted light detection and ranging (lidar) for power performance testing under the condition of excavated terrain, an experimental investigation was carried out at the DBK wind farm on Jeju Island, South Korea. A nacelle lidar installed on a 2 MW test wind turbine was used with an 80 m tall met mast and a ground-based lidar for measuring wind conditions. The measurement sector was calculated for the site, which was reduced to the analysed sector for finding out the influence of the excavated landfill. Since the excavated landfill was located near the test wind turbine, the excavated terrain was evaluated according to International Electrotechnical Commission (IEC) 61400-12-1, 2nd edition and the power curve was drawn using met mast wind speeds at the hub height, which was compared with that from the turbine manufacturer. The excavated terrain was further analysed using the Computational Fluid Dynamics (CFD) technique to determine whether it has an influence on wind flow. Then, the wind speed data measured by cup anemometers on the met mast, the ground-based lidar and the nacelle mounted lidar were compared through the linear regression method. The rotor equivalent wind speed (REWS) was calculated using met mast and ground lidar wind speeds according to the IEC 61400-12-1, 2nd edition. The power curves were obtained by applying the REWS and the nacelle lidar measurements. In addition, the relative errors of the power outputs were estimated and further annual energy productions (AEPs) were analysed by applying the Rayleigh wind speed distribution to the power curves. As a result, the effect of the excavated terrain on the power curve was very small. The power curves using the nacelle lidar measurements were mostly the same as the power curve using the REWS.

Wind turbine load validation in wakes using wind field reconstruction techniques and nacelle lidar wind retrievals

Abstract. This study proposes two methodologies for improving the accuracy of wind turbine load assessment under wake conditions by combining nacelle-mounted lidar measurements with wake wind field reconstruction techniques. The first approach consists of incorporating wind measurements of the wake flow field, obtained from nacelle lidars, into random, homogeneous Gaussian turbulence fields generated using the Mann spectral tensor model. The second approach imposes wake deficit time series, which are derived by fitting a bivariate Gaussian shape function to lidar observations of the wake field, on the Mann turbulence fields. The two approaches are numerically evaluated using a virtual lidar simulator, which scans the wake flow fields generated with the dynamic wake meandering (DWM) model, i.e., the target fields. The lidar-reconstructed wake fields are then input into aeroelastic simulations of the DTU 10 MW wind turbine for carrying out the load validation analysis. The power and load time series, predicted with lidar-reconstructed fields, exhibit a high correlation with the corresponding target simulations, thus reducing the statistical uncertainty (realization-to-realization) inherent to engineering wake models such as the DWM model. We quantify a reduction in power and loads' statistical uncertainty by a factor of between 1.2 and 5, depending on the wind turbine component, when using lidar-reconstructed fields compared to the DWM model results. Finally, we show that the number of lidar-scanned points in the inflow and the size of the lidar probe volume are critical aspects for the accuracy of the reconstructed wake fields, power, and load predictions.

Spinner anemometer: wind speed and Spinner Transfer Function seasonal robustness in an offshore wind farm

This work presents the first, long-term, demonstration case of the use of spinner anemometers for power curve measurements in an offshore wind farm. The stability of the spinner anemometer wind speed and its STF during a 22-month measurement period was quantified, using a nacelle lidar as reference. Power curves and annual energy productions (AEPs) were obtained using this long-term data set, to assess the spinner anemometer wind speed stability. Next, several STFs were obtained at a reference turbine in different periods (four two-months periods) to assess the STF seasonality. Finally, the AEPs of three neighbouring turbines were analysed using the STFs generated at the reference turbine.

Aeroelastic load validation in wake conditions using nacelle-mounted lidar measurements

Abstract. We propose a method for carrying out wind turbine load validation in wake conditions using measurements from forward-looking nacelle lidars. Two lidars, a pulsed- and a continuous-wave system, were installed on the nacelle of a 2.3 MW wind turbine operating in free-, partial-, and full-wake conditions. The turbine is placed within a straight row of turbines with a spacing of 5.2 rotor diameters, and wake disturbances are present for two opposite wind direction sectors. The wake flow fields are described by lidar-estimated wind field characteristics, which are commonly used as inputs for load simulations, without employing wake deficit models. These include mean wind speed, turbulence intensity, vertical and horizontal shear, yaw error, and turbulence-spectra parameters. We assess the uncertainty of lidar-based load predictions against wind turbine on-board sensors in wake conditions and compare it with the uncertainty of lidar-based load predictions against sensor data in free wind. Compared to the free-wind case, the simulations in wake conditions lead to increased relative errors (4 %–11 %). It is demonstrated that the mean wind speed, turbulence intensity, and turbulence length scale have a significant impact on the predictions. Finally, the experiences from this study indicate that characterizing turbulence inside the wake as well as defining a wind deficit model are the most challenging aspects of lidar-based load validation in wake conditions.