Power curve measurement with a nacelle mounted lidar

Nacelle‐based lidars are an attractive alternative to conventional mast base reference wind instrumentation where the erection of a mast is expensive, for example offshore. In this paper, the use of this new technology for the specific application of wind turbine power performance measurement is tested. A pulsed lidar prototype, measuring horizontally, was installed on the nacelle of a multi‐megawatt wind turbine. A met mast with a top‐mounted cup anemometer standing at two rotor diameters in front of the turbine was used as a reference. After a data‐filtering step, the comparison of the 10 min mean wind speed measured by the lidar to that measured by the cup anemometer showed a deviation of about 1.4% on average. The power curve measured with the lidar was very similar to that measured with the cup anemometer although the lidar power curve was slightly distorted because of the deviation in wind speed measurements. A lower scatter in the power curve was observed for the lidar than for the mast. Since the lidar follows the turbine nacelle as it yaws, it always measures upwind. The wind measured by the lidar therefore shows a higher correlation with the turbine power fluctuations than the wind measured by the mast. Finally, the lidar is never in the wake of the turbine under test contrary to the cup anemometer; therefore, the wind sector usable for power curve measurement was larger than the sector for which the cup anemometer was not disturbed by any obstacle. The power curve obtained with the lidar for the wind sector in which the mast is in the wake of the turbine under test compared well with the power curve obtained on the standard sector. Copyright © 2013 John Wiley & Sons, Ltd.     

Wind turbine load validation using lidar‐based wind retrievals

We define and demonstrate a procedure for carrying out wind turbine load validation based on measurements from nacelle‐mounted scanning lidars. Two coherent Doppler lidar systems, a pulsed lidar and a continuous‐wave lidar, are mounted on a 2.3‐MW wind turbine equipped with load measurement sensors. Wind measurements from a meteorological mast mounted at 2.5 rotor diameters distance are used as reference. The study shows how lidar measurements are processed and applied as inputs to aeroelastic load simulations, and the results are then compared with simulations where the wind inputs have been determined using the meteorological mast data in compliance with the IEC61400‐13 standard. For the majority of simulation cases considered, the use of nacelle‐mounted lidar measurements results in load estimation uncertainties lower or equal to those that are based on measurements from cup anemometers on the mast. These results demonstrate the usefulness of nacelle‐mounted lidars as tools for carrying out load validation without the need of meteorological masts.     

Calibration of a spinner anemometer for wind speed measurements

The power curve of a wind turbine can be measured, according to IEC 61400‐12‐2 with a nacelle‐mounted anemometer. Typically, a sonic anemometer or a cup anemometer and a wind vane are mounted on the back of the nacelle roof. Another option is to use a spinner anemometer. The measurement principle of the spinner anemometer is based on the flow distortion caused by the wind turbine spinner. The flow on the spinner surface is measured by means of three 1D sonic sensors mounted on the spinner and a conversion algorithm to convert the wind velocity components measured by the three sonic sensors to horizontal wind speed, yaw misalignment and flow inclination angle. The algorithm utilizes two calibration constants that are specific to the spinner shape, blade root design and to the mounting positions of the sonic sensors on the spinner. The present analysis describes methods to determine the calibration constant related to wind speed measurements. The first and preferred method is based on the definition of the calibration constant and uses wind speed measurements during the stopped condition of the wind turbine. Two alternative methods that did not require the turbine to be stopped were investigated: one used relatively high wind speed measurements during normal operation of the wind turbine, while the other one used a CFD simulation of the flow over the spinner. The method that entails stopping the turbine in good wind conditions showed the best results and is recommended. The evaluation of uncertainty was not included in the present analysis. Copyright © 2016 The Authors Wind Energy Published by John Wiley & Sons Ltd.     

A spinner‐integrated wind lidar for enhanced wind turbine control

A field test with a continuous wave wind lidar (ZephIR) installed in the rotating spinner of a wind turbine for unimpeded preview measurements of the upwind approaching wind conditions is described. The experimental setup with the wind lidar on the tip of the rotating spinner of a large 80 m rotor diameter, 59 m hub height 2.3 MW wind turbine (Vestas NM80), located at Tjæreborg Enge in western Denmark is presented. Preview wind data at two selected upwind measurement distances, acquired during two measurement periods of different wind speed and atmospheric stability conditions, are analyzed. The lidar‐measured speed, shear and direction of the wind field previewed in front of the turbine are compared with reference measurements from an adjacent met mast and also with the speed and direction measurements on top of the nacelle behind the rotor plane used by the wind turbine itself. Yaw alignment of the wind turbine based on the spinner lidar measurements is compared with wind direction measurements from both the nearby reference met mast and the turbine's own yaw alignment wind vane. Furthermore, the ability to detect vertical wind shear and vertical direction veer in the inflow, through the analysis of the spinner lidar data, is investigated. Finally, the potential for enhancing turbine control and performance based on wind lidar preview measurements in combination with feed‐forward enabled turbine controllers is discussed. Copyright © 2012 John Wiley & Sons, Ltd.     

Nacelle anemometer measurement‐based extremum‐seeking wind turbine region‐2 control for improved convergence in fluctuating wind

For region‐2 operation of wind turbines in practice, the optimal torque gain can deviate from the nominal value because of the variations in turbine and wind conditions. The extremum‐seeking control (ESC) has shown its potential as a model‐free region‐2 control solution in some recent work; however, the ESC with rotor power feedback suffers from undesirable convergence under fluctuating wind. In this paper, we propose to use an estimated power coefficient as the objective function for the torque‐gain ESC, where the hub‐height free‐stream wind speed (FSWS) is estimated with the nacelle anemometer measurement on the basis of the so‐called nacelle transfer function (NTF) between the nacelle anemometer and met‐tower measurement. A sensitivity analysis is performed to quantify the impact of the wind speed estimation error on the estimation of power coefficient. An ESC integrated interregion switching scheme is proposed to avoid the load increase. Simulation results show that, compared with the power feedback‐based ESC, the proposed method can greatly improve the convergence rate of ESC under fluctuating wind, even under relatively large wind speed estimation error. Evaluation for the fatigue loads of wind turbine shows that the proposed control strategy induces mild increase of the wind turbine load.     

Calibration of a spinner anemometer for yaw misalignment measurements

The spinner anemometer is an instrument for yaw misalignment measurements without the drawbacks of instruments mounted on the nacelle top. The spinner anemometer uses a non‐linear conversion algorithm that converts the measured wind speeds by three sonic sensors on the spinner to horizontal wind speed, yaw misalignment and flow inclination angle. The conversion algorithm utilizes two constants that are specific to the spinner and blade root design and to the mounting positions of the sonic sensors on the spinner. One constant, k₂, mainly affects the measurement of flow angles, while the other constant, k₁, mainly affects the measurement of wind speed. The ratio between the two constants, k_α=k₂/k₁, however, only affects the measurement of flow angles. The calibration of k_α is thus a basic calibration of the spinner anemometer. Theoretical background for the non‐linear calibration is derived from the generic spinner anemometer conversion algorithm. Five different methods were evaluated for calibration of a spinner anemometer on a 500 kW wind turbine. The first three methods used rotor yaw direction as reference angular, while the wind turbine, was yawed in and out of the wind. The fourth method used a hub height met‐mast wind vane as reference. The fifth method used computational fluid dynamics simulations. Method 1 utilizing yawing of the wind turbine in and out of the wind in stopped condition was the preferred method for calibration of k_α. The uncertainty of the yaw misalignment calibration was found to be 10%, giving an uncertainty of 1° at a yaw misalignment of 10°. © 2014 The Authors. Wind Energy published by John Wiley & Sons, Ltd.     

Modelling and measurements of power losses and turbulence intensity in wind turbine wakes at Middelgrunden offshore wind farm

Understanding of power losses and turbulence increase due to wind turbine wake interactions in large offshore wind farms is crucial to optimizing wind farm design. Power losses and turbulence increase due to wakes are quantified based on observations from Middelgrunden and state‐of‐the‐art models. Observed power losses due solely to wakes are approximately 10% on average. These are relatively high for a single line of wind turbines due in part to the close spacing of the wind farm. The wind farm model Wind Analysis and Application Program (WAsP) is shown to capture wake losses despite operating beyond its specifications for turbine spacing. The paper describes two methods of estimating turbulence intensity: one based on the mean and standard deviation (SD) of wind speed from the nacelle anemometer, the other from mean power output and its SD. Observations from the nacelle anemometer indicate turbulence intensity which is around 9% higher in absolute terms than those derived from the power measurements. For comparison, turbulence intensity is also derived from wind speed and SD from a meteorological mast at the same site prior to wind farm construction. Despite differences in the measurement height and period, overall agreement is better between the turbulence intensity derived from power measurements and the meteorological mast than with those derived from data from the nacelle anemometers. The turbulence in wind farm model indicates turbulence increase of the order 20% in absolute terms for flow directly along the row which is in good agreement with the observations. Copyright © 2007 John Wiley & Sons, Ltd.     

Using nacelle‐based wind speed observations to improve power curve modeling for wind power forecasting

Wind power forecasting for projection times of 0–48 h can have a particular value in facilitating the integration of wind power into power systems. Accurate observations of the wind speed received by wind turbines are important inputs for some of the most useful methods for making such forecasts. In particular, they are used to derive power curves relating wind speeds to wind power production. By using power curve modeling, this paper compares two types of wind speed observations typically available at wind farms: the wind speed and wind direction measurements at the nacelles of the wind turbines and those at one or more on‐site meteorological masts (met masts). For the three Australian wind farms studied in this project, the results favor the nacelle‐based observations despite the inherent interference from the nacelle and the blades and despite calibration corrections to the met mast observations. This trend was found to be stronger for wind farm sites with more complex terrain. In addition, a numerical weather prediction (NWP) system was used to show that, for the wind farms studied, smaller single time‐series forecast errors can be achieved with the average wind speed from the nacelle‐based observations. This suggests that the nacelle‐average observations are more representative of the wind behavior predicted by an NWP system than the met mast observations. Also, when using an NWP system to predict wind farm power production, it suggests the use of a wind farm power curve based on nacelle‐average observations instead of met mast observations. Further, it suggests that historical and real‐time nacelle‐average observations should be calculated for large wind farms and used in wind power forecasting. Copyright © 2011 John Wiley & Sons, Ltd.     

Estimation of turbulence intensity using rotor effective wind speed in Lillgrund and Horns Rev-I offshore wind farms

Turbulence characteristics of the wind farm inflow have a significant impact on the energy production and the lifetime of a wind farm. The common approach is to use the meteorological mast measurements to estimate the turbulence intensity (TI) but they are not always available and the turbulence varies over the extent of the wind farm. This paper describes a method to estimate the TI at individual turbine locations by using the rotor effective wind speed calculated via high frequency turbine data.The method is applied to Lillgrund and Horns Rev-I offshore wind farms and the results are compared with TI derived from the meteorological mast, nacelle mounted anemometer on the turbines and estimation based on the standard deviation of power. The results show that the proposed TI estimation method is in the best agreement with the meteorological mast. Therefore, the rotor effective wind speed is shown to be applicable for the TI assessment in real-time wind farm calculations under different operational conditions. Furthermore, the TI in the wake is seen to follow the same trend with the estimated wake deficit which enables to quantify the turbulence in terms of the wake loss locally inside the wind farm.     

On wind turbine power performance measurements at inclined airflow

The average airflow inclination in complex terrain may be substantial. The airflow inclination affects wind turbine performance and also affects the cup anemometer being used in power performance measurements. In this article the overall dependence of the power curve on inclined airflow is analysed for its influence on both the wind turbine and the cup anemometer. The wind turbine performance analysis is based on results of measurements and theoretical calculations with the aeroelastic code HAWC coupled to a 3D actuator disc model for varying yaw angle. The cup anemometer analysis at inclined flow is based on an averaging of measured angular characteristics in a wind tunnel with the distribution of airflow inclination angles over time. The relative difference in annual energy production in terrain with inclined airflow compared with flat terrain is simulated for cup anemometers with theoretical optimal angular characteristics for two different definitions of wind speed, as well as for five commercial cup anemometers with measured angular characteristics. Copyright © 2004 John Wiley & Sons, Ltd.     

Investigation of wake interaction using full‐scale lidar measurements and large eddy simulation

In this paper, wake interaction resulting from two stall regulated turbines aligned with the incoming wind is studied experimentally and numerically. The experimental work is based on a full‐scale remote sensing campaign involving three nacelle mounted scanning lidars. A thorough analysis and interpretation of the measurements is performed to overcome either the lack of or the poor calibration of relevant turbine operational sensors, as well as other uncertainties inherent in resolving wakes from full‐scale experiments. The numerical work is based on the in‐house EllipSys3D computational fluid dynamics flow solver, using large eddy simulation and fully turbulent inflow. The rotors are modelled using the actuator disc technique. A mutual validation of the computational fluid dynamics model with the measurements is conducted for a selected dataset, where wake interaction occurs. This validation is based on a comparison between wake deficit, wake generated turbulence, turbine power production and thrust force. An excellent agreement between measurement and simulation is seen in both the fixed and the meandering frame of reference. Copyright © 2015 John Wiley & Sons, Ltd.     

Turbine‐scale wind field measurements using dual‐Doppler lidar

Spatially resolved measurements of microscale winds are retrieved using scanning dual‐Doppler lidar and then compared with independent in situ wind measurements. Data for this study were obtained during a month‐long field campaign conducted at a site in north‐central Oklahoma in November of 2010. Observational platforms include one instrumented 60 m meteorological tower and two scanning coherent Doppler lidars. The lidars were configured to perform coordinated dual‐Doppler scans surrounding the 60 m tower, and the resulting radial velocity observations were processed to retrieve the three‐component velocity vector field on surfaces defined by the intersecting scan planes. The dual‐Doppler analysis method is described, and three‐dimensional visualizations of the retrieved fields are presented. The retrieved winds are compared with sonic anemometer (SA) measurements at the 60 m level on the tower. The Pearson correlation coefficient between the retrievals and the SA wind speeds was greater than 0.97, and the wind direction difference was very small (<0.1^o), suggesting that the dual‐Doppler technique can be used to examine fine‐scale variations in the flow. However, the mean percent difference between the SA and dual‐Doppler wind speed was approximately 15%, with the SA consistently measuring larger wind speeds. To identify the source of the discrepancy, a multi‐instrument intercomparison study was performed involving lidar wind speeds derived from standard velocity‐azimuth display (VAD) analysis of plan position indicator scan data, a nearby 915 MHz radar wind profiler (RWP) and radiosondes. The lidar VAD, RWP and radiosondes wind speeds were found to agree to within 3%. By contrast, SA wind speeds were found to be approximately 14% larger than the lidar VAD wind speeds. These results suggest that the SA produced wind speeds that were too large. Copyright © 2013 John Wiley & Sons, Ltd.     

Detection of nacelle anemometer faults in a wind farm minimizing the uncertainty

The performance assessment of wind farms requires the acquisition of accurate power and wind speed data of each turbine. Nowadays, the nacelle anemometry is widely studied as an option for power performance verification. Therefore, systems to detect the nacelle anemometer faults in a wind farm in operation are necessary for maintenance purposes. In this paper, we propose a method to detect wind speed deviations of the nacelle anemometers by comparing them with the nearby anemometers. This comparison is made through an approach to estimate the wind speed in each nacelle. The approach is based on the discretization of wind speed data using the bin method. The key issue of this proposal is the estimation of the anemometer deviations considering the range of data with lower uncertainty. To this end, an average uncertainty model per bin and direction sector has been integrated into the method. The tests show that using wind speeds higher than 4.5 m s ^{? 1} gives the lowest uncertainty. Data from two wind farms have been used to test this method, and the obtained results have allowed the detection of problematic anemometers. Copyright © 2012 John Wiley & Sons, Ltd.     

Determining wind turbine power curves based on operating conditions

The relation between wind speed and electrical power—the power curve—is essential in the design, management and power forecasting of a wind farm. The power curve is the main characteristic of a wind turbine, and a procedure is presented for its determination, after the wind turbine is installed and in operation. The procedure is based on both computational and statistical techniques, in situ measurements, nacelle anemometry and operational data. This can be an alternative or a complement to procedures fully based on field measurements as in the International Electrotechnical Commission standards, reducing the time and costs of such practices. The impact of a more accurate power curve was measured in terms of the prediction error of a wind power forecasting system over 1 year of operation, whereby the methodology for numerical site calibration was presented and the concepts of ideal power curve and nacelle power curve introduced. The validation was based on data from wind turbines installed at a wind farm in complex topography, in Portugal, providing a real test of the technique presented here. The contribution of the power curve to the wind power forecasting uncertainty was found to be from 10% to 15% of the root mean square error. Copyright © 2013 John Wiley & Sons, Ltd.     

Testing and validation of multi‐lidar scanning strategies for wind energy applications

Several factors cause lidars to measure different values of turbulence than an anemometer on a tower, including volume averaging, instrument noise and the use of a scanning circle to estimate the wind field. One way to avoid the use of a scanning circle is to deploy multiple scanning lidars and point them toward the same volume in space to collect velocity measurements and extract high‐resolution turbulence information. This paper explores the use of two multi‐lidar scanning strategies, the tri‐Doppler technique and the virtual tower technique, for measuring 3‐D turbulence. In summer 2013, a vertically profiling Leosphere WindCube lidar and three Halo Photonics Streamline lidars were operated at the Southern Great Plains Atmospheric Radiation Measurement site to test these multi‐lidar scanning strategies. During the first half of the field campaign, all three scanning lidars were pointed at approximately the same point in space and a tri‐Doppler analysis was completed to calculate the three‐dimensional wind vector every second. Next, all three scanning lidars were used to build a ‘virtual tower’ above the WindCube lidar. Results indicate that the tri‐Doppler technique measures higher values of horizontal turbulence than the WindCube lidar under stable atmospheric conditions, reduces variance contamination under unstable conditions and can measure high‐resolution profiles of mean wind speed and direction. The virtual tower technique provides adequate turbulence information under stable conditions but cannot capture the full temporal variability of turbulence experienced under unstable conditions because of the time needed to readjust the scans. Copyright © 2016 John Wiley & Sons, Ltd.     

Numerical error estimation of conventional anemometry mounted on offshore floating met‐masts

Offshore wind energy is moving towards a future where the main challenge is to cope with the increasing water depth needed to access more and better wind resources. One of the first steps to be undertaken is the development of floating structures to support either wind turbines or measurement devices for the proper characterization of wind energy resources. The use of floating devices for measuring wind speed involves a number of uncertainties not presented by seabed fixed systems. These sources of uncertainty or error are present both in instantaneous wind measurements and averaged (10 min or hourly) values because of (i) variability in the measurement height, (ii) the tilt of the anemometer and (iii) the relative velocity between the anemometer and the wind, among others. In this paper, a methodology for assessing the error in the wind measurement characterization because of the movement of a floating meteorological mast is presented. By the numerical simulation of a floating mast, the short‐ and long‐term error in the characterization of the wind at different heights has been evaluated. In general, the error because of the tilt can reach up to 80% of the total error; the error because of the variation of the vertical position of the anemometer reaches values of up to 15% in some cases; moreover, the error associated with the relative velocity between the anemometer and the wind, for averaged values, is significantly less. Finally, it can be concluded that the total error is lower than 0.5% for 10 min averaged wind speed of up to 24 m/s. Copyright © 2016 John Wiley & Sons, Ltd.     

Application of simulated lidar scanning patterns to constrained Gaussian turbulence fields for load validation

We demonstrate a method for incorporating wind velocity measurements from multiple‐point scanning lidars into three‐dimensional wind turbulence time series serving as input to wind turbine load simulations. Simulated lidar scanning patterns are implemented by imposing constraints on randomly generated Gaussian turbulence fields in compliance with the Mann model for neutral stability. The expected efficiency of various scanning patterns is estimated by means of the explained variance associated with the constrained field. A numerical study is made using the hawc2 aeroelastic software, whereby the constrained turbulence wind time series serves as input to load simulations on a 10 MW wind turbine model using scanning patterns simulating different lidar technologies—pulsed lidar with one or multiple beams—and continuous‐wave lidars scanning in three different revolving patterns. Based on the results of this study, we assess the influence of the proposed method on the statistical uncertainty in wind turbine extreme and fatigue loads. The main conclusion is that introducing lidar measurements as turbulence constraints in load simulations may bring significant reduction in load and energy production uncertainty, not accounting for any additional uncertainty from real measurements. The constrained turbulence method is most efficient for prediction of energy production and loads governed by the turbulence intensity and the thrust force, while for other load components such as tower base side‐to‐side moment, the achieved reduction in uncertainty is minimal. Copyright © 2016 John Wiley & Sons, Ltd.     

Accounting for the speed shear in wind turbine power performance measurement

The current IEC standard for wind turbine power performance measurement only requires measurement of the wind speed at hub height assuming this wind speed to be representative for the whole rotor swept area. However, the power output of a wind turbine depends on the kinetic energy flux, which itself depends on the wind speed profile, especially for large turbines. Therefore, it is important to characterize the wind profile in front of the turbine, and this should be preferably achieved by measuring the wind speed over the vertical range between lower and higher rotor tips. In this paper, we describe an experiment in which wind speed profiles were measured in front of a multimegawatt turbine using a ground–based pulsed lidar. Ignoring the vertical shear was shown to overestimate the kinetic energy flux of these profiles, in particular for those deviating significantly from a power law profile. As a consequence, the power curve obtained for these deviant profiles was different from that obtained for the ‘near power law’ profiles. An equivalent wind speed based on the kinetic energy derived from the measured wind speed profile was then used to plot the performance curves. The curves obtained for the two kinds of profiles were very similar, corresponding to a significant reduction of the scatter for an undivided data set. This new method for power curve measurement results in a power curve less sensitive to shear. It is therefore expected to eventually reduce the power curve measurement uncertainty and improve the annual energy production estimation. Copyright © 2011 John Wiley & Sons, Ltd.     

Validation of a CFD model with a synchronized triple‐lidar system in the wind turbine induction zone

A novel validation methodology allows verifying a CFD model over the entire wind turbine induction zone using measurements from three synchronized lidars. The validation procedure relies on spatially discretizing the probability density function of the measured free‐stream wind speed. The resulting distributions are reproduced numerically by weighting steady‐state Reynolds averaged Navier‐Stokes simulations accordingly. The only input varying between these computations is the velocity at the inlet boundary. The rotor is modelled using an actuator disc. So as to compare lidar and simulations, the spatial and temporal uncertainty of the measurements is quantified and propagated through the data processing. For all velocity components the maximal difference between measurements and model are below 4.5% relative to the average wind speed for most of the validation space. This applies to both mean and standard deviation. One rotor radius upstream the difference reaches maximally 1.3% for the axial component. © 2017 The Authors. Wind Energy Published by John Wiley & Sons, Ltd.     

Potential of power gain with improved yaw alignment

One of the primary criteria for extracting energy from the wind using horizontal axis upwind wind turbines is the ability to align the rotor axis with the dominating wind direction. The conventional way of estimating the direction of the incoming flow is by using transducers placed atop the nacelle and downwind of the rotor. Recent studies have suggested methods based on advanced upwind measurement technologies for estimating the inflow direction and improving the yaw alignment. In this study, the potential of increased power output with improved yaw alignment is investigated by assessing the performance of a current measurement and yaw control system. The performance is assessed by analyzing data containing upwind wind speed and direction measurements from a met mast, and yaw angle and power production measurements from an operating offshore wind turbine. The results of the analysis indicate that the turbine is operating with a wind speed‐dependent yaw error distribution. The theoretical annual energy production loss due to the yaw error distribution of the existing system is estimated to approximately 0.2%. Copyright © 2014 John Wiley & Sons, Ltd.