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NREL is operated by Midwest Research Institute ● Battelle Contract No. DE-AC36-99-GO10337 

Lidar for Turbine Control 

March 1, 2005 – November 30, 2005 

M. Harris 

QinetiQ Limited 

Malvern, Worcestershire, United Kingdom 

M. Hand, A. Wright 

National Renewable Energy Laboratory 

Golden, Colorado 

Technical Report 

NREL/TP-500-39154 

January 2006 
Lidar for Turbine Control 

March 1, 2005 – November 30, 2005 

M. Harris 

QinetiQ Limited 

Malvern, Worcestershire, United Kingdom 

M. Hand, A. Wright 

National Renewable Energy Laboratory 

Golden, Colorado 

NREL Technical Monitor: S. Schreck 

Prepared under Task No. WER6.0301 and Subcontract No. YAM-5-33200-11

Technical Report 

NREL/TP-500-39154 

January 2006 

National Renewable Energy Laboratory 

1617 Cole Boulevard, Golden, Colorado 80401-3393 

303-275-3000 • www.nrel.gov 

Operated for the U.S. Department of Energy 

Office of Energy Efficiency and Renewable Energy 

by Midwest Research Institute • Battelle 

Contract No. DE-AC36-99-GO10337
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Executive Summary 

This work represents the first study (to our knowledge) that explores the potential of a turbine-mounted 

laser anemometer to enhance capabilities for wind energy production. The study combines laser 

anemometry (lidar) know-how from QinetiQ, Malvern, United Kingdom, with turbine design and control 

system expertise from the National Wind Technology Center (NWTC) at the National Renewable Energy 

Laboratory (NREL), Colorado, United States of America. 

Lidar offers a method of remote wind speed measurement. The technique was first demonstrated in the 

1970s and has been used in a number of research applications. Widespread deployment of the technique 

has so far been hampered by the expense and complexity of lidar systems. However, the recent 

development of lidar systems based on optical fiber and components from the telecommunications 

industry promises large improvements in cost, compactness, and reliability so that it becomes viable to 

consider the deployment of such systems on large wind turbines for the advance detection of fluctuations 

in the incoming wind field. Potential advantages of this approach include increased turbine energy output 

and reduced turbine fatigue damage (increased lifetime); here we only explore the latter. 

The overall goal of the Lidar for Turbine Control study is to examine the requirements of a forwardlooking lidar system, along with the associated interface to the turbine control systems. Different lidar 

options have been considered, including design choices such as output power, transceiver, signal 

processing, as well as turbine mounting and scanning options. Three configurations have been analyzed in 

more detail: Configuration #1 is the cheapest basic option, Configuration #2 is an intermediate option, 

while Configuration #3 has the greatest capability and cost. The lidar costs have been calculated on the 

basis of the production of 250 turbine-mounted lidar units per year with an initial run of 150 units. The 

final costings, which rely on assumptions and must be treated with caution, range from $45,000 to 

$95,000. Each of the options can accurately measure the axial component of wind speed in a timescale of 

order 10s of milliseconds. Hence, by scanning the beam it is possible to build a picture of the wind field 

in front of the turbine. 

A generic method for analysis of lidar output has been examined, allowing a modelling representation of 

the lidar input to the control system. This involves derivation of the line-of-sight component of wind 

speed for each point interrogated, followed by a weighted summation of these components to derive the 

lidar spectrum for a given geometry. Full-field simulated turbulent wind fields are created using TurbSim, 

a code developed by NREL that generates a binary data file containing u, v, and w velocity components at 

evenly spaced grid points in a plane. A module was then added to the FAST aeroelastic wind turbine 

simulation code to simulate a lidar device. The current study explores the inclusion of forward-looking 

lidar as input to a control algorithm to mitigate fatigue loads at the blade root in above-rated wind speed 

conditions. 

The use of forward-looking lidar could enhance a wide range of control systems in a number of regimes; 

however, the scope of this study is restricted to examination of speed regulation and load mitigation in 

Control Region 3 for the NWTC’s CART (Controls Advanced Research Turbine). This is a 2-bladed, 

teetered-hub, variable-speed machine, but a rigid hub was simulated in this study. The turbine is rated at 

600 kW; it has a 43-m rotor diameter and a 36-m hub height. At rated power, the turbine rotates at 41.7 

rpm. It is outfitted with servo-electric motors capable of pitching the blades independently, precisely, and 

quickly. This wind turbine was selected for this simulation study because a number of sophisticated 

control algorithms have been tested on it. The results indicate that damage equivalent flap loads can be 

reduced by approximately 10% under turbulent wind inflow conditions when lidar signals are included as 

measurements to the controller. Studies such as this could eventually feed into an estimate of the 

reduction in cost of energy (COE) that would result from the widespread deployment of turbine-mounted 

lidar. 

iii
As a first study in the field of turbine-mounted lidar, this work has identified a number of potential areas 

for future research, including further modelling investigations to examine some of the other regimes 

identified. Controllers that include lidar-based wind measurement inputs must be specifically designed for 

each of the operating regimes. Eventually the modelling studies must extend to larger-scale turbines in 

order to arrive at a realistic estimate for the reduction in the COE. Current industry standard machines are 

rated at 1.5 MW with rotor diameters on the order of 80 m, but many companies are developing even 

larger turbines. 

It would also be highly desirable to conduct experiments on turbine-mounted lidar to verify the results of 

the modelling work. Any scheme would ideally retain enough flexibility to explore different scan patterns 

and focus range options. These lidar experiments will also provide the capability for a wider range of 

investigations, including power curve measurement and turbine wake and shadowing studies. Also, field 

testing is required to address concerns regarding the true correlation between upwind flow measurements 

and the turbine response. These differences cannot be simulated with current simulation capabilities. 

iv
Table of Contents

1 Introduction...............................................................................................................................1

2 Overview of Lidar Techniques.................................................................................................2

2.1 Basic Principles of CLR............................................................................................2

2.2 Continuous Wave (CW) Lidar..................................................................................4

2.3 Pulsed Lidar ..............................................................................................................5

2.4 Choice of Laser Wavelength.....................................................................................5

2.5 The QinetiQ ZephIR Lidar System...........................................................................6

3 Options for Turbine-Mounted Lidar.........................................................................................9

3.1 Previous Work ..........................................................................................................9

3.2 Consideration of Turbine-Mounted Lidar Options.................................................10

3.2.1 Environmental Conditions ................................................................................10

3.2.2 CW Versus Pulsed Lidar...................................................................................11

3.2.3 Output Power ....................................................................................................11

3.2.4 Transceiver Optics ............................................................................................11

3.2.5 Monostatic Versus Bistatic ...............................................................................12

3.2.6 Direction Sensing..............................................................................................12

3.2.7 Signal Processing ..............................................................................................13

3.2.8 Lidar Mounting .................................................................................................13

3.2.9 Lidar Scanning ..................................................................................................14

3.3 Summary .................................................................................................................16

4 Analysis of Chosen Configurations........................................................................................17

4.1 Summary of Chosen Options..................................................................................17

4.1.1 Configuration #1 ...............................................................................................17

4.1.2 Configuration #2 ...............................................................................................17

4.1.3 Configuration #3 ...............................................................................................17

4.2 Comparison of Capabilities.....................................................................................18

4.3 Analysis of System Costs........................................................................................18

5 Lidar/Control System Interface ..............................................................................................19

5.1 Algorithms to Derive Lidar Output from Synthesized Wind Field Data ................19

5.1.1 Line-of-Sight Wind Component .......................................................................19

5.1.2 Weighting Function...........................................................................................19

5.1.3 Velocity Estimation...........................................................................................20

5.2 Modelling of Wind Field and Derivation of Lidar Input ........................................20

5.2.1 Simulated Wind Field .......................................................................................20

5.2.2 Simulated Lidar Signal......................................................................................21

6 Control Model: Assumptions and Objectives.........................................................................25

6.1 Approach.................................................................................................................25

6.2 Wind Turbine Simulation........................................................................................27

v
6.3 Wind Turbine Controller Designs...........................................................................28

7 Control System Study: Results and Analysis .........................................................................31

7.1 Results.....................................................................................................................31

7.2 Conclusions.............................................................................................................37

8 Conclusions ............................................................................................................................38

9 Recommendations and Proposed Future Work ......................................................................39

9.1 Extensions to the Current Study..............................................................................39

9.2 Other Lidar Capabilities..........................................................................................39

10 References/Bibliography ........................................................................................................40

A Appendix: Abbreviations and Definitions..............................................................................45

B Appendix: Table of ZephIR Specifications............................................................................47

vi
1 Introduction 

Laser anemometry (lidar) offers a method of remote wind speed measurement. The technique was first 

demonstrated in the 1970s and has been used in a number of research applications. Widespread 

deployment of the technique has so far been hampered by the expense and complexity of lidar systems. 

However, the recent development of lidar systems based on optical fiber and components from the 

telecommunications industry promises large improvements in cost, compactness, and reliability so that it 

becomes viable to consider the deployment of such systems on large wind turbines for the advance 

detection of fluctuations in the incoming wind field. Potential advantages of this approach include 

increased turbine energy output and reduced turbine fatigue damage (increased lifetime); we only explore 

the latter in the current report. 

Established by the United States Department of Energy Wind Energy Program, the Low Wind Speed 

Technology (LWST) Project aims to develop technology that will enable wind systems to generate costcompetitive electrical energy at low wind speed sites. The sites targeted by this effort have annual average 

wind speeds of 5.8 m/s, measured at a 10-m height. Such sites are abundant in the United States and 

would increase the available land area that can be economically developed by twentyfold. The program 

goal is: By 2012, reduce the cost of electricity from large wind systems in Class 4 winds to 3.6 cents/kWh 

for onshore systems and by 2014, reduce the cost of electricity from large wind systems in Class 6 winds 

to 5 cents/kWh for shallow water (depths up to 30 meters) offshore systems. 

The overall goal of the Lidar for Turbine Control study is to examine the requirements of a forwardlooking lidar system, along with the associated interface to the turbine control systems. We consider 

different installation options and perform an analysis of the costs and benefits of achieving enhanced 

performance and reduced maintenance. The report provides a summary of the lidar technique (Section 2) 

and a review of the various available turbine-mounting options (Section 3). From the numerous possible 

combinations, we analyze three configurations in detail (Section 4) and estimate their costs and 

capabilities. Next we examine the analysis of lidar output, allowing a modelling representation of the lidar 

input to the control system (Section 5). The control studies in Sections 6 and 7 analyze and quantify the 

potential advantage of advanced wind speed warning via turbine-mounted lidar. These studies could 

eventually feed into an estimate of the reduction in cost of energy (COE) that would result from the 

widespread deployment of turbine-mounted lidar. Sections 8 and 9 summarize the overall conclusions and 

propose further stages of investigation. 

1
2 Overview of Lidar Techniques 

A number of optical and acoustic methods have been developed that allow measurement of fluid 

velocities; reference [1] provides a useful recent review of the range of available wind-sensing equipment. 

In this report, we concentrate on the technique of coherent laser radar (CLR), also referred to as lidar, 

ladar, and CDL (coherent Doppler lidar), for the remote measurement of wind speed in the atmosphere. 

Lidar involves the emission of a coherent light beam and detection of the weak return reflected or 

scattered from a distant target. The technique provides a means to measure the line-of-sight component of 

wind speed via detection of the Doppler shift for light backscattered from natural aerosols (particles of 

dust, pollen, droplets, etc.) in the atmosphere. A basic underlying assumption that the scatterers accurately 

follow the flow is usually very reliable except in precipitation. The principles of lidar are described in 

more detail in Section 2.1. 

Other techniques for measurement of flow velocity include laser Doppler anemometry (LDA), also 

known as laser Doppler velocimetry (LDV), which detects the intensity modulation from particles 

traversing the fringe pattern created by the intersection of two laser beams [2], the modulation frequency 

being proportional to the transverse component of the particles’ velocity. LDA is commonly used in wind 

tunnels but is inappropriate for atmospheric wind speed measurements: it lacks sensitivity at ranges 

beyond a few meters and seeding is normally required. The nomenclature requires care and can be 

misleading: CLR systems are sometimes described as “reference beam LDA.” 

Particle imaging velocimetry (PIV) is another powerful technique for flow measurement in wind tunnels 

[3]. Like LDA, it requires the flow to contain seeding particles and is therefore unsuitable for atmospheric 

measurements. The same comments apply to the technique of Doppler global velocimetry (DGV) [4]. 

Incoherent (or “direct detection”) Doppler lidar is an alternative for atmospheric measurement of wind 

speed [5]; these pulsed systems usually operate in the UV and rely on molecular scattering to provide the 

return signal. While the capabilities of incoherent lidar are impressive (they are being considered for 

space-based global wind measurements), their current size, cost, and complexity suggest the technique is 

inappropriate for the turbine-mounted application considered here. 

Sodar is a commonly used method of remote atmospheric wind profiling. It involves the emission of 

sound pulses and relies on the detection of the weak echo scattered from temperature and velocity 

fluctuations in the atmosphere [6]. It measures the wind velocity via the Doppler shift of the acoustic 

pulses in a manner analogous to lidar. It is unlikely that sodar will be suitable for the application 

examined in this report: the background acoustic noise in a turbine-mounted location is likely to disturb 

the measurement, and side lobes on the emitted sound beam will inevitably strike solid objects (blades, 

the ground, etc.) leading to spurious returns. In addition, the sensitivity is typically lower than for lidar 

leading to much reduced data rates. 

2.1 Basic Principles of CLR 

The principle by which coherent laser radar measures the velocity of a target is deceptively simple: a 

beam of coherent radiation illuminates the target, and a small fraction of the light is backscattered into a 

receiver. Motion of the target along the beam direction leads to a change δν in the light’s frequency via 

the Doppler shift, given by: 

λ

δν ν LOS VLOS

c

2V 2 = = (2.1) 

2
where c is the speed of light (3 x 108

 m s-1), VLOS is the component of target speed along the line of sight 

(i.e., the beam direction), and ν and λ are respectively the laser frequency and wavelength. This frequency 

shift is accurately measured by mixing the return signal with a portion of the original beam and picking 

up the beats on a photodetector at the difference frequency. The essential features are readily seen in the 

simplified generic CLR depicted in Figure 1. To illustrate the concept, this is drawn as a bistatic system in 

which the transmit and receive optics are separate and distinct. In practice, a monostatic geometry is more 

usual, in which the transmit and receive paths share common optics. 

Figure 1. Generic coherent laser radar (CLR) system 

The reference beam, or local oscillator (LO), plays a crucial role in the operation of a CLR. Firstly, it 

defines the region of space in which light must be scattered for detection of the beat signal; radiation from 

other sources (e.g., sunlight) is rejected, so CLR systems are usually completely immune to the effect of 

background light. The LO also provides a stable reference frequency to allow precise velocity 

determination; as a consequence, CLR systems are inherently calibrated, provided there are no gross drifts 

in laser frequency. Finally, the LO amplifies the signal via the beating process to allow operation at a 

sensitivity that approaches the shot-noise (or quantum) limit. This very high sensitivity permits the 

operation of CLR systems in an unseeded atmosphere, relying only on detection of weak backscattering 

from natural aerosols. 

Figure 2 illustrates the stages of signal processing required for CLR wind signals. The detector output, 

containing the beat signal information embedded in broadband noise, is typically digitized by an analogto-digital converter (ADC). Spectral analysis (e.g., by fast Fourier transform methods) leads to the 

generation of Doppler spectra. It is usually necessary to average a number of these spectra to improve the 

signal-to-noise ratio (SNR), after which the Doppler peak should stand clearly above a flat shot-noise 

floor. A value for the line-of-sight wind speed can then be computed via a velocity estimation algorithm. 

This might calculate, for example, the peak or centroid value of the Doppler peak. 

Laser 

Transmitted light

Local oscillator 

Target (reference beam) Detector

Scattered & 

Reflected light 

(with Doppler frequency) 

3
Figure 2. Outline of lidar wind signal processing 

Lidar has been used in a wide range of atmospheric research applications: 

• Wind profiling 

• General meteorological investigations 

• True airspeed measurement 

• Characterization and tracking of aircraft wake vortices [7, 8] 

• Monitoring of gaseous species (DIAL, or differential absorption lidar). 

In addition, it has a number of applications related to the interrogation of hard targets: 

• Rangefinding 

• 3D imaging 

• Remote vibration analysis. 

2.2 Continuous Wave (CW) Lidar 

CLR systems can be either of pulsed or CW type. In its simplest embodiment, the lidar emits a continuous 

unmodulated radiation beam. The detector output is then processed in discrete chunks at a rate determined 

by the signal processing and other system requirements. Such systems achieve operation at different 

ranges by focusing at each chosen range in turn. Focusing of the lidar beam results in a Lorentzian spatial 

weighting function centered on the beam waist. The half-width of this function is determined by the 

Rayleigh range (i.e., the distance from the waist at which the beam area has doubled its value at the 

waist), and this increases roughly as the square of the range. CW systems have an inherent maximum 

range (usually of order a few 100s of meters) beyond which it is impossible to focus the beam because of 

diffraction. A variant of CW lidar, frequency-modulated continuous wave (FMCW), has been used to 

i Spectral analysis Detector output F

Time 

Frequency, velocity 

Averaging

Velocity 

estimation 

Frequency, velocity Mean speed, turbulence, .... 

4
improve the range-resolution capability [9], but this is only effective with hard targets and is not an 

appropriate technique for wind speed measurement. 

In wind speed measurements, the target scattering material is assumed distributed evenly throughout the 

probe volume. Under this condition, the time-averaged received optical signal power PS is well 

approximated by [10]: 

S ≈ π PP T β π )( λ (2.2) 

Where PT is the transmitted laser power, β(π) is the backscatter coefficient in (m sr)-1, and λ is the laser 

wavelength. Hence CW CLR possesses the interesting property that the return signal power has no 

dependence on range or aperture size1

. Taking a value of β(π) for clear boundary-layer air of 10-8 (m sr)-1

for a wavelength 1.5μm, we obtain a value of ∼5x10-14 for the fraction PS/PT of transmitted photons that 

are received by the system. This emphasizes the need for high sensitivity, with a received photon rate of 

∼7x105

 s-1 for 1W of emitted radiation. 

2.3 Pulsed Lidar 

In a pulsed lidar, the radiation is emitted in short bursts, typically of a few microseconds [11,12]. This 

approach allows range measurement from the time-of-flight and permits operation to much longer ranges 

(>10 km). Data from each range bin are obtained in parallel, in contrast to CW systems that must probe 

different ranges in series. The short pulse duration necessarily degrades velocity resolution, and there is a 

direct trade-off between range resolution (∆R) and velocity resolution (∆VLOS). These are determined by 

the approximate relations below for a pulse length T: 

T

V cT R LOS

π

λ

4 ; ~ 2

∆ ~ ∆ (2.3) 

As an example, a range resolution ∆R of 10m requires a pulse length T ∼ 60ns, giving a velocity 

resolution ∆VLOS ∼ 2 m s-1 for wavelength λ = 1.55μm. Note, however, that the mean value of VLOS can 

be determined to much higher accuracy than ∆VLOS. 

2.4 Choice of Laser Wavelength 

The best choice of wavelength for a lidar system depends on several factors, including eye safety, 

performance, and cost and availability of technology. 

Eye safety is assumed to be a prerequisite for the application considered here. Although the risk of a 

turbine-mounted system illuminating maintenance personnel is small, the system must achieve laser Class 

1 or 1M [13]. While it is possible to operate safely at a higher class rating, this will probably result in an 

unacceptable administrative burden involving, for example, the need to provide a trained Laser Safety 

Officer at each installation. Wavelengths longer than 1.4μm do not penetrate the eye and cannot reach the 

retina; hence a higher power emission is permitted within Class 1 at these wavelengths. With this 

restriction in mind, the available technology currently narrows the choice in practice to 3 three wavebands 

centered around 1.5μm, 2μm, and 10μm. The earliest lidar systems [14,15] used 10.6μm CO2 gas laser 

sources, but these require cryogenically cooled detectors; large, expensive optical components; and 

 

1

 A larger lens (or shorter range) subtends a greater solid angle at the target region, leading to the more efficient 

collection of light scattered from the probe volume. However, the probe volume becomes smaller, so it scatters less 

light in the first place. These two effects cancel, leading to the independence of signal on aperture and range until 

the maximum range is approached. 

5
delicate, free-space alignment optics. We conclude that such systems are unsuitable for the current 

application. Solid-state 2μm systems have reached a high level of development but are probably more 

suitable for long-range pulsed applications. Optical-fiber-based 1.5μm systems have been developed to 

exploit recent advances in the telecommunications industry. The use of fiber greatly simplifies system 

alignment, and highly reliable components are generally available off-the-shelf. 

The overall performance of a CW CLR wind sensor is not a sensitive function of its wavelength. While 

more light is scattered from aerosols at shorter wavelength, long wavelength systems are inherently more 

sensitive [16] (reasons include the emission of more photons per Watt of laser power and reduced 

Doppler shifts leading to lower noise bandwidth). These effects largely cancel, leaving an overall 

dependence in SNR of order λ0.5 in the relevant weak signal regime of low β(π). The slight advantage this 

confers on long-wavelength systems is more than offset by convenience and cost considerations: We 

conclude that fiber-based systems at 1.5μm are the best choice for this application. So far, only systems of 

this type have been successfully deployed for turbine-mounted lidar wind speed measurements 

(Section 3.1). 

2.5 The QinetiQ ZephIR Lidar System 

In the mid-1990s, the QinetiQ lidar team investigated alternatives to its existing CO2 lidar systems. New 

developments in optical fiber and related components from the telecommunications industry offered a 

promising approach to lidar construction. The team successfully demonstrated CW all-fiber lidar 

anemometers (at a wavelength 1.55μm) in the late 1990s [9,16] and have remained at the forefront with 

the development of a commercial fiber lidar system (ZephIR) that has an established track record of 

successful wind measurement in a number of applications related to the wind power industry [17-21]. 

QinetiQ began a program to develop a commercial fiber-based laser anemometer in 2001. Progress has 

been rapid, building on previous fundamental work, and prototype systems have been deployed 

successfully in several demanding applications. Initial deployment of the ZephIR laser anemometer 

(March 2003) was on the nacelle of a large (2.3-MW) wind turbine (Figure 3a), measuring the wind speed 

up to 200 m in front of the blades. The lidar consisted of a rack unit containing a laser source, detector, 

and signal processing computer situated in the base of the tower, connected via more than 100 m of 

electrical and optical fiber cable to the transceiver head mounted on the top of the nacelle. The lidar 

system was installed in one afternoon and was fully operational soon after, allowing advance warning of 

oncoming gusts and providing valuable experience in practical deployment issues. 

The system was returned to Malvern after several weeks of successful operation. It was then converted 

into a ground-based scanning unit for wind profiling (Figure 3b) and was first tested in December 2003. It 

has since been used in numerous campaigns in the United Kingdom, Europe, and other parts of the world. 

The experience gained through these trials has built confidence in the robustness and reliability of the 

ZephIR design (see Appendix B). In late 2004, work began on a production instrument (Figure 3c), 

designed to perform autonomous wind profiling measurements for site surveys at proposed wind farm 

sites. The design process has concentrated on providing a reliable compact system that requires minimal 

maintenance and no re-calibration. Much effort has also been devoted to minimizing power consumption 

and the ability to operate successfully in extreme conditions (e.g., low and high temperatures). 

6
Figure 3. Stages of evolution of the ZephIR lidar. Figure 3a shows the lidar head mounted 

on the nacelle of a Nordex N-90 wind turbine. Figure 3b shows prototype ground-based 

wind profiler at Risø wind energy test site, Høvsøre, Denmark. Figure 3c shows the ZephIR 

production model deployed in the field 

The scanning version of the ZephIR lidar instrument has been widely employed as a wind profiler, 

measuring wind speed and direction from a ground-based platform to altitudes up to 150 m. This was 

achieved by offsetting the beam at an angle of 30 degrees to the vertical and scanning in azimuth at a rate 

of 1 revolution per second. As the beam rotates, it intercepts the wind at different angles, thereby building 

up a map of wind speed around a disc of air [22]. In a uniform air flow, a plot of Doppler velocity (VLOS) 

versus scan angle takes the form of a rectified sine wave, with the peak Doppler values corresponding to 

upwind and downwind measurements: 

LOS cos( ) φ +−= cbaV (2.4) 

where φ is the scan azimuth angle. A non-linear least-squares fit is performed of this model to the data, 

from which the three best-fit parameters—a, b and c—are extracted. The wind data is then computed as 

follows: 

bBearing

Vertical cwspeed

Horizontal auspeed

=

°=

°=

30cos/)(

30sin/)(

A possible ambiguity of 180° in bearing is normally easily resolved with reference to a simple wind-vane 

reading. When displayed as a polar plot, the output gives rise to a figure-of-eight (Figure 4). Figure 5 

shows a typical result (from [17]) in which the 10-minute averaged values of horizontal wind speed 

obtained by the lidar are compared with those from a mast-mounted cup anemometer. Such verification is 

an important step toward wider deployment of lidar in the wind energy industry. 

7
Figure 4. Polar plot of line-of-sight velocity component in m/s versus azimuth scan angle. 

White squares: 75 measurements obtained over 3 seconds. Continuous curve: best-fit 

solution. Bold line from origin: upwind bearing. The excellent fit indicates uniform flow 

across the scanned area. Note also a slight asymmetry in the lobe sizes, indicating a small 

vertical component to the wind velocity. By adjusting the focus, wind measurements can be 

made at all heights from 5 m to >150 m above ground level. 

Lidar / Cup @ 100 m, 21 August 2004

20

15

10

Lidar

5 y = 1.0038x + 0.0793

R2 = 0.9883

0

0 5 10 Cup 15 20

Figure 5. Regression plot of 10-minute averaged wind speed 100 m above ground in m/s, 

measured by lidar and calibrated cup anemometer. The lidar was situated 120 m from the 

base of the mast. The measurements were obtained over a 24-hour period when there was 

little risk of shadowing of the cup (by the mast or by adjacent turbines). The corresponding 

plots at other heights (40 m, 60 m, 80 m) display similarly good agreement and correlation. 

The experience QinetiQ has gained with ZephIR and other related systems forms the basis for much of 

the detailed analysis of options presented in the following sections. 

8
3 Options for Turbine-Mounted Lidar 

3.1 Previous Work 

While CO2 coherent lidar has been used in some previous wind-power-related studies [23, 24], we are 

aware of only two examples in which lidar systems were mounted on a wind turbine. In the first 

experiment of this type to our knowledge, an early prototype of the QinetiQ ZephIR system was mounted 

on the nacelle of a Nordex N90 wind turbine at Postlow, 5 km west of Anklam in northeast Germany 

[20]. The lidar was configured as a continuous-wave (CW) monostatic design, capable of measuring lineof-sight wind speeds up to 39 m s-1. The lidar transceiver was mounted on top of the nacelle (Figure 3a) 

and was connected by fiber optic and electrical cable to a control unit (in the base of the tower) containing 

the laser source, detector, and signal processing computer. The transmitted laser power would normally 

be ∼1W; however, with the long (120 m) connecting fibers used here, the output was restricted to ∼400 

mW to avoid the non-linear optical mechanism of stimulated Brillouin scattering (SBS), which would 

lead to excessive noise and poor lidar performance. 

The system stared straight ahead through the blades and could be focused at any range R from 10 m out to 

200 m, and it was capable of measuring a value for wind speed approximately 10 times every second. 

Because the lidar beam was blocked approximately once per second by the rotating blades of the turbine, 

only 60% of measurements were successful, with the beam striking a blade in the remaining 40%. Such 

occurrences gave rise to very strong signals at low frequency (blade motion is nearly orthogonal to the 

lidar beam, so the Doppler shift is small) and are easily identified and rejected. The detector requires no 

recovery time from the intense blade reflection so that blocking presented no real problem in practice, 

with the lidar obtaining five or six independent wind measurements every second. 

Data were acquired successfully over the full period of deployment, but to illustrate the lidar capability, 

we concentrate here on an 18-hour period when disturbance of the flow caused by adjacent turbines 

should be negligible. The wind speed data showed a striking increase in the turbulence level during this 

period, although the mean wind speed remained broadly similar throughout. During the evening (19:00-

20:00), the airflow was remarkably stable and uniform; fluctuations increased through the night and 

became significantly more dramatic after sunrise (08:00 and later), exhibiting near-discontinuities in wind 

speed denoting the arrival of gusts, superimposed on a higher level of overall fluctuation. Figure 6 

examines in more detail a good example of a gust, manifested as a sudden jump in speed at 08:44. In 

Figure 6b, the gust passes the beam focus at 200 m (corresponding to the midpoint of the lidar probe 

region) so that the two speeds contribute equally to the overall spectrum. The upper trace (Figure 6a), 

obtained 5 seconds earlier, is dominated by the lower speed, while 7 seconds later the lower trace (Figure 

6c) shows virtually all the air in the probe volume moving at the higher velocity. The roughly 10-second 

timescale for the transition depicted in Figure 6 is consistent with the expected transit time of the gust 

through the lidar probe volume (∼80m divided by 7 m s-1). There are many other gust events in the 

measurement sequence comparable to that displayed in Figure 6; however, a full and detailed 

interpretation of these events is difficult since the wind field is being probed only along a single line, and 

hence no information can be obtained on the transverse structure of the gusts. 

9
Figure 6. Evolution of Doppler spectra through a gust, with the time indicated on each plot. 

Plot (b) corresponds to a near-discontinuity in wind speed from 4 m s-1 to 7 m s-1. 

In another example of turbine-mounted lidar, a ZephIR system was installed by Risø on a test turbine for 

wake analysis. The lidar was mounted on a rearward-looking pan-and-tilt scanner, allowing measurement 

of wake wind speed deficit and wander [21]. 

3.2 Consideration of Turbine-Mounted Lidar Options 

We begin with a summary of the generic characteristics that a lidar system must satisfy in this application. 

We envisage that the entire lidar will be mounted as an integrated unit, unlike previous turbine-mounted 

deployments discussed in Section 3.1. This will simplify installation and maintenance and eliminate any 

risk of problems due to SBS. The lidar must be eyesafe and achieve laser Class 1 or 1M. It must be 

capable of remote wind speed measurement at a rate of at least several Hz. The lidar must be 

economically viable over its entire lifetime, from initial procurement through ongoing in-service 

maintenance. And it must be capable of reliable operation in a wide range of environmental conditions. 

The specific lidar design options and requirements are now examined in more detail. 

3.2.1 Environmental Conditions 

The environment for either nacelle- or hub-mounted lidar is expected to be similar to that in an aircraft, 

with vibrations typically of order 0.5g at a few Hz. Neither mounting option results in a severe constraint 

on lidar size or shape. Temperature requirements are -40° C to +40° C. The lidar operating lifetime should 

be 20 to 30 years, with servicing at a minimum interval of 6 months, and a requirement for simple, rapid 

10
removal. Other considerations include lightning surge protection; the effect of bugs, dust, and humidity 

on window transmission; and the possibility of damage from prolonged exposure to solar radiation. 

3.2.2 CW Versus Pulsed Lidar 

Pulsed lidar is a powerful technique for range-resolved measurement of atmospheric wind speed at 

considerable ranges of several km [11]. A pulsed lidar typically comprises the following elements: A 

stable master oscillator laser provides the coherent input to a pulsed optical amplifier, which then 

provides a train of transmitted output pulses. Care must be taken to maintain coherence during each pulse 

and avoid excessive frequency chirping. The local oscillator may be derived by tapping off a fraction of 

the master oscillator, and it is usually shifted in frequency to resolve directional ambiguity and avoid 

“wraparound” of the spectrum close to zero frequency. The target range is identified from accurate timing 

information requiring synchronization of the output pulse with the detection and signal processing 

electronics. By contrast, the elements of a CW system (laser output, detector, signal processing) operate 

entirely independently of each other and are possibly more compatible with modular construction. 

Parasitic scattering of the pulse within the transmitter can lead to saturation of the detection electronics 

for a finite time, leading to a minimum range that can be several tens of meters. This is avoided by a 

bistatic design at the cost of increased complexity (Section 3.2.5). The eye safety constraints (Section 

3.2.3) can be more stringent for a pulsed system; however, for high pulse repetition rates, the Class 1 limit 

of mean power converges with that of a CW system. Care is also needed in optical-fiber-based pulsed 

systems to avoid non-linear optical effects such as SBS that can be generated by the high peak power of 

the pulse [12]. 

Overall we believe that it is currently not possible for a pulsed lidar design to satisfy all the requirements 

listed above, principally on the grounds of complexity, cost, and lifetime. In the discussion that follows it 

is therefore assumed that a CW system will be employed. 

3.2.3 Output Power 

The sensitivity of a CW CLR is directly proportional to its output power. High sensitivity is required to 

achieve a successful measurement rate in the clearest atmospheric conditions (i.e., when backscatter β(π) 

is very low). As an example, the ZephIR production system emits PT = 1W of laser radiation, giving rise 

to a minimum detectable backscatter of order β(π) = 10-9 (m sr)-1 for a 20 ms measurement time (50 Hz). 

For a reduced output of 200 mW, the minimum value of β(π) required for wind speed measurement rises 

by a factor 5 to ∼5 x 10-9 (m sr)-1. This may result in a reduced level of availability, with the missed signal 

frequency possibly being a strong function of geographical location. Global atmospheric data on β(π) at λ

= 1.5μm is very sparse, so it is currently impossible to make reliable quantitative estimates of availability 

for a given system sensitivity. From experience (in England, Scotland, Denmark, Germany, and The 

Netherlands), PT = 200 mW should provide availability >95% in Northern Europe2

 and other temperate 

maritime climates. The laser is the most expensive single component of the lidar, so there is a significant 

cost reduction associated with lower output power. 

3.2.4 Transceiver Optics 

The choice of lens aperture diameter has implications for system size/cost and performance. In general, 

the cost of a lens increases dramatically with its diameter. The resulting system is also likely to increase 

 

2

 This estimate is based on the performance of the ZephIR prototype, which operated at PT = 1W with reduced duty 

cycle and a sub-optimal detector. These amount to a deficit in sensitivity compared with the ZephIR production 

system of roughly a factor 5; hence the availability estimate is correct for an improved system with PT = 200 mW. 

11
significantly in weight and volume, and an increase in lens size does not improve sensitivity (Section 2.2). 

However, the lens size does impact on the maximum range and also on the probe volume (spatial 

resolution) for the wind speed measurement. 

The lens quality must be high, with a need to achieve near-diffraction-limited performance3

; a poorquality lens will result in degraded spatial resolution. The issue of spatial resolution is analyzed in more 

detail in Section 5.1.2. 

3.2.5 Monostatic Versus Bistatic 

Bistatic CW lidar systems (with separate transmit and receive optics) are commonly used for hard-target 

applications such as vibrometry [25]. Bistatic CW wind lidars have also been successfully demonstrated 

[16] (Figure 7) and have the advantage of improved probe volume definition. In particular, the sensitivity 

weighting function cuts off rapidly, unlike the extended Lorentzian wings associated with a monostatic 

geometry (Section 5.1.2). 

 Figure 7. A simple all-fiber bistatic lidar design. Note the provision of an acousto-optic 

modulator (AOM) to allow direction sensing (Section 3.2.6) 

However, a number of disadvantages associated with bistatic systems makes them inappropriate for this 

application. They are considerably more complex, requiring more optical components; a costly alignment 

and calibration process will likely be needed; they are prone to vibration; and they suffer a penalty in 

overall sensitivity. On this basis, we conclude that only monostatic designs should be considered further. 

3.2.6 Direction Sensing 

The local oscillator (LO) beam in a CLR is conventionally derived from a portion of the transmitted 

beam. Such systems are incapable of distinguishing the sign of the target motion (i.e., movement at the 

same speed, either toward or away from the lidar, leads to a signal at the same beat frequency). In order to 

build a lidar system that is capable of sensing the direction of motion, a difference in frequency must be 

created between the LO and transmitted beams. This is achieved by passing one of these beams through 

an acousto-optic modulator (Bragg cell), which imposes a frequency shift ∆ (typically several 10s of 

 

3

 The effect of lens quality on SNR can be quantified by considering the focused beam area in comparison with a 

diffraction-limited spot. The signal power from the target focal plane depends inversely on this area; if the focused 

spot has double the diffraction-limited area, then there will be a 3dB penalty for sensitivity in the focal plane. 

Laser 

OI 

Transmit 

AOM

Receive 

splitter

splitter 

Local 

Oscillator

FPC 

Detector splitter 

12
MHz). Scattering from a stationary object then generates a beat at the “intermediate frequency” ∆, and the 

sign of any motion is derived according to whether the beat shifts up or down in frequency. 

Again, the implementation of direction sensing comes with a penalty of increased complexity, reliability, 

and cost. A cost-benefit analysis must consider the likelihood of significant flow reversal in the forwardlooking lidar geometry and also assess the possible penalty of being unable to distinguish such a flow 

reversal event. For wind profiling with the ground-based ZephIR system, directional ambiguity is usually 

resolved with a simple wind vane measurement; however, it is likely that this system will risk incorrect 

analysis of extreme flow reversal events. 

3.2.7 Signal Processing 

The optical beats that result from mixing the wind signal with the local oscillator are picked up on a 

photodetector. At a wavelength λ ∼ 1.5μm, reliable InGaAs photodiodes are readily available that satisfy 

the essential requirements for a CLR of high quantum efficiency and low noise. The detector output must 

be spectrally analyzed in order to extract wind speed data; until recently, analog techniques such as 

surface acoustic wave (SAW) spectrum analyzers were common, but with current advances in computer 

power, digital methods are now becoming routine. 

We illustrate the signal processing requirements with an example that is well matched to the needs of this 

application. An 8-bit ADC with a sampling rate of 100 MHz permits spectral analysis up to a maximum 

frequency of 50 MHz, corresponding to a wind speed VLOS of 38.8 m s-1 (Eq. 2.1, with λ = 1.55μm). 

Spectra are calculated by digital Fourier transform (DFT) methods; a 512 point DFT will give rise to 256 

points in the output spectrum with a bin width of ∼200 kHz (corresponding to a velocity range of 

∼0.15 m s-1). Each DFT will represent ∼5μs of data; successive DFTs are then calculated, and the 

resulting power spectra are averaged to find a mean spectrum. The random fluctuation in the shot noise 

floor of the spectrum reduces as the square root of the number of averages; the sensitivity increases by 

this same factor. For 4,000 averages, the measurement time amounts to ∼20 ms (or a data rate of 50 Hz). 

This assumes the processing is capable of 100% duty cycle, which has been shown to be achieved in 

ZephIR with a fast Fourier transform (FFT) block within a field-programmable gate array (FPGA), and it 

is likely that a standard fast PC with no additional duties to perform can achieve a similar performance. It 

is possible to accommodate reasonable variations in any of the above parameters (sample rate, DFT size, 

number of averages) and maintain the 100% duty cycle. 

In conclusion, the choice of signal processing hardware does not impact greatly on the overall cost of the 

proposed lidar systems, and some flexibility on the choice of parameter values can be assumed. 

3.2.8 Lidar Mounting 

There are two main options for lidar mounting to consider: nacelle mounting and hub mounting. The 

relative merits are summarized in Table 1. 

13
Table 1. Main Options for Lidar Mounting

Pros Cons 

Nacelle mounting Ease of mounting Rotating blades block beam at 

∼1Hz 

Tested solution – low risk Restricted field of view for 

some scan patterns 

Hub mounting Unobscured view Installation more awkward 

Hub rotation can be used to 

provide conical scan without 

additional hardware by 

aligning beam at angle to 

rotation axis 

Need to provide power to 

rotating hub and return wind 

speed data to nacelle 

Overall the choice is between the greater simplicity of nacelle mounting and the improved capability 

associated with hub mounting. 

3.2.9 Lidar Scanning 

In general, the incoming wind field is turbulent, varying substantially in space and time and containing 

flow structures of different size and orientation. These fluctuations impose the fatigue loads that we seek 

to attenuate. The goal of scanning the lidar beam is therefore to provide a complete picture of the 

incoming wind field and hence provide the most beneficial input to the turbine control system to optimize 

performance. There are a wide variety of scanning options, to be considered here, ranging from a fixed 

focus, forward-staring arrangement to a fully programmable system that can interrogate any sequence of 

points in a 3D (x,y,z) scan pattern. 

There are three options for lidar focusing (i.e., range adjustment), which are fixed focus, continuously 

variable, and limited number (e.g., three) of discrete range settings. In a fiber-based lidar, the focus is 

conveniently adjusted by moving the position of the output fiber relative to the focusing lens. A fixed 

focus system is simple, avoids moving parts, and involves low risk. The continuously variable option 

needs to maintain precise axial positioning of the fiber end (to within a few 10s of microns) and hence 

introduces lifetime and calibration issues. While the discrete setting option still contains moving parts, its 

performance is at lower risk of deterioration through years of operation.

Scanning options (Figure 8) include a conical scan, simply implemented with a rotating wedge as in the 

ground-based ZephIR system (as indicated above, this is available automatically for a hub-mounted 

system); linescan; and raster scan, which will require complex dual-prism or dual-mirror arrangement and 

is not considered further. Another interesting option that can provide a more complex scan pattern without 

excessive complexity is a combined hub/lidar conical scan, in which the lidar executes a conical scan that 

adds to that of the hub motion. Variation in scan pattern is possible by choosing the relative cone angles 

and scan rates for lidar and hub. In any scheme, the scan rate and cone angle must be chosen with 

consideration for area coverage and eye safety. A static focused 1-W 1.55μm beam will considerably 

exceed the maximum permissible exposure (MPE) of 1000 W m-2 (1 mW mm-2), and such a beam can 

only comply with the laser Class 1 or 1M requirements if it is scanned so as to sufficiently reduce the 

mean, time-averaged, power at any given point in space. Continuous scanning is favored over a “scan, 

stop, and stare” scheme for this reason, and it is also easier to implement. Hence, there is a possible tradeoff between cone angle, scan rate, lens diameter, and minimum range (large lens and short range resulting 

14
in a tighter focus with shorter probe volume). However, in practice this does not impose a severe 

restriction, and the options proposed here fall well within Class 1M limits; in fact, a near-staring Class 

1M option is possible at ranges beyond 20 m by introducing only a very small cone angle of 1 degree. 

Figure 8. Examples of scanning options, showing hub-mounted staring scheme and hubmounted conical scan 

A number of other issues may influence the choice of scan pattern. Portions of a conical scan will clearly 

involve pointing the lidar beam with a downward or upward component, running a risk of striking the 

ground or clouds respectively. It is possible that these highly scattering objects, even though far from the 

beam focus, may provide more backscattered light than the actual wind signal of interest unless the lidar 

is of the more sophisticated pulsed or bistatic type. Returns from the ground are unlikely to cause a 

problem: stationary and slow-moving objects (<1 m s-1) have only a small Doppler shift that can be 

simply eliminated via high-pass filtering (either pre- or post-spectral analysis). Returns from fastermoving objects such as cars will be clearly inconsistent with the overall wind behavior and can be 

addressed with outlier rejection algorithms. This also applies to spurious signals that can be generated 

when the lidar beam strikes birds or the moving blades of adjacent turbines. Scattering from clouds is a 

potentially greater problem, becoming more serious as the cone angle increases, as the range increases, 

and as the atmospheric backscatter reduces. Filtering algorithms have been developed at QinetiQ to 

eliminate cloud returns for the ZephIR wind profiler, and these could be further enhanced, if required, for 

use in this application. 

Finally, the influence of rain on the measured wind speed must be considered; in rain, the falling drops 

provide a large fraction of the total backscatter, and there is a breakdown of the assumption that the 

scatterers faithfully follow the wind field. For a system in which the beam only points horizontally, errors 

from the raindrops’ downward motion can be neglected, but wind speed may be slightly overestimated 

when the rain falls through a shear gradient and take some time to reach terminal horizontal velocity. 

Larger errors can occur when the beam has an upward- or downward-pointing component, as the drops’ 

fall speed adds (or subtracts) from the true line-of-sight wind speed. The above effects are very dependent 

on droplet size, becoming more severe for large drops (e.g., a 0.2mm-diameter drop falls under gravity at 

speed ∼1.2 m/s and reaches terminal velocity in a distance ∼15 cm). 

The overall decision on scanning is a choice between simple forward staring and more complex conical 

scan patterns that provide more information on the spatial wind field, but which may introduce errors in 

rainfall and spurious returns from clouds. 

15
3.3 Summary

From the discussion in the previous section, we conclude it is likely that a cost-effective lidar system in 

this proposed application will be monostatic, continuous-wave, and at a wavelength of ∼1.5μm allowing 

use of optical fiber and other telecoms components. Even with the rejection at this stage of certain options 

(bistatic, pulsed, etc.) and the concentration on fiber-based systems, there is still potentially a wide 

parameter space to examine, with consideration to be given to: 

• Laser output power 

• Aperture size 

• Scanning options 

• Mounting options 

• Direction sensing. 

The next section concentrates on three specific options with differing capabilities, allowing realistic 

estimates of the total system cost for each of the options. 

16
4 Analysis of Chosen Configurations 

This section describes the three chosen configuration options and their respective capabilities. The 

components of each system are identified, allowing an approximate cost breakdown. It is an 

assumption that all options must fall within laser Class 1 or 1M. 

4.1 Summary of Chosen Options 

4.1.1 Configuration #1 

This is the cheapest basic option, comprising the following: 

• Small aperture (diameter ∼30 mm), monostatic, fixed focus (range ∼50 m) 

• No scanner, hub mounted for conical scanning 

• Laser output power 200 mW. 

The precise focus range is flexible and can be anywhere in the region of 20 m to 80 m with no 

impact on cost. The same comments apply to the choice of scanning cone angle. The lower laser 

power will result in reduced sensitivity and hence a possible reduction in wind speed availability. 

4.1.2 Configuration #2 

This is an intermediate cost option: 

• Large aperture (diameter ∼70 mm), monostatic, fixed focus 

• No scanner, hub mounted for conical scanning 

• Laser output power 1 W. 

The larger aperture permits operation at longer range, with a maximum range on the order of 150 m 

(or possibly 200 m if the resulting extension of probe length is acceptable). The short range limit is 

of order 20 m; this value depends on the cone angle because of eye safety considerations. The 1-W 

laser power should ensure high availability with minimal drop-out rate. 

4.1.3 Configuration #3 

This option has the greatest capability and cost: 

• Large aperture (diameter ∼70 mm), monostatic 

• Three discrete focus settings 

• Laser output power 1W 

• Conical scan 

• Nacelle or hub mounted 

• Direction sensing. 

The system is capable of operation at long range and with high sensitivity. The focus settings can be 

at any range between 20 m and 200 m, and it will be possible to switch rapidly between them during 

operation. The conical scan will be achieved by a rotating wedge arrangement with a rotation rate in 

17
the range of 0.3Hz to 3Hz; precise scan rate and cone angle have little impact on cost. The hubmounted option will permit the most detailed spatial investigation of the incoming wind field with 

the potential to superimpose the lidar scan on the hub motion. 

4.2 Comparison of Capabilities 

Configurations #1 and #2 differ only in their sensitivity and spatial resolution. In the same 

conditions, #2 will achieve a SNR higher by a factor 5, and this will lead to a lower probability of 

“missed signals” and a higher availability. As previously discussed (Section 3.2.3), the improvement 

in availability is difficult to quantify, and it will vary with geographical location. The improved 

spatial resolution allows #2 to attain a greater potential maximum range, which is double that of #1. 

At a given range, the region of sensitivity (probe length) of #1 is roughly 4 times that of #2 (see 

Section 5.1.2 for quantitative analysis of probe length). Note that either #1 or #2 can be mounted on 

the nacelle for forward-staring operation, as long as a simple small-angle-wedge scanner is 

incorporated (a 1° cone angle is probably sufficient – Section 3.2.9) to satisfy eye safety limits. 

Configuration #3 differs from the other two in its ability to scan and focus at different ranges. It also 

has the ability to measure the sign of the Doppler shift and distinguish flow reversals. Its sensitivity 

and availability will be the same as #2. It is at higher risk of disturbance from vibration, and it will 

require the use of more expensive polarization maintaining (PM) optical fiber and components, 

instead of the non-PM fiber of #1 and #2. 

4.3 Analysis of System Costs 

The following estimates of lidar costs have been calculated on the basis of the production of 250 

turbine-mounted lidar units per year, with an initial run of 150 units. The estimate first takes into 

account component costs; these are generally available off-the-shelf from a range of suppliers, with 

the principal exception of the laser. The laser specification must satisfy stringent requirements for 

coherence length and intensity noise, and there are significant cost reductions through volume 

production. Despite this, the laser dominates the component cost in each of the three cases examined. 

Greater uncertainty is associated with the contributions to the cost from lidar assembly, calibration, 

and testing. In addition, we have taken into consideration the rough order-of-magnitude costs of 

installation, maintenance, warranty, and profit. The final prices, shown in the Table 2, must be 

treated with caution; they rely on certain assumptions and ignore likely future price variation. 

Table 2. Lidar Costs 

Configuration #1 Configuration #2 Configuration #3 

System price $45,000 $60,000 $95,000 

Includes laser cost $10,500 $15,000 $20,000 

In summary, Configuration #1 achieves a lower price than #2 by employing a lower-power laser and 

smaller optics. The higher price of Configuration #3 reflects the additional complexity associated 

with scanning, focus control, and direction sensing. 

18
5 Lidar/Control System Interface 

5.1 Algorithms to Derive Lidar Output from Synthesized Wind Field Data 

In this section, we provide the means to derive the relevant simulated lidar data from a given 

realization of a synthesized wind field to provide input to the turbine control algorithms. The 

approach is not specific to a particular scan pattern. It is assumed that the wind field is described by 

values of the wind vector (u, v, w components – see Appendix A and Figure 9 for definitions) at 

discrete points forming a rectangular 3D grid (i.e., space is divided into a number of “boxes” and 

each wind vector represents some kind of average value for that particular box). A Cartesian 

coordinate system is assumed (see table of definitions) with the lidar located at (0,0,0). 

There are three steps to derive the required lidar O/P at a given instantaneous value of scan angle: 

1. Derive the component of wind speed along the beam direction (“line of sight”) for each “box” 

intersected by the lidar beam. 

2. Perform a weighted sum of these components to derive the lidar spectrum. 

3. Use the spectrum to perform an estimation of line-of-sight velocity. 

For Steps 1 and 2, it is a convenient approximation to ignore the motion of the beam during each 

individual measurement. Smearing over several boxes will only occur for scan patterns significantly 

faster than the current rate. In the prototype ZephIR system, the relevant timescale is 2.6 ms; in the 

upgraded production version with improved duty cycle, this increases to 20 ms. There is some 

flexibility in the choice of these values, involving a trade-off between SNR and measurement time 

(SNR ∝ τ

0.5). 

5.1.1 Line-of-Sight Wind Component 

Coherent lidar measures the Doppler shift resulting from the component of target velocity along the 

beam (or line-of-sight) direction. Motion of the target transverse to the beam direction produces no 

net Doppler shift. Hence, for a box located at (x,y,z) with wind components (u,v,w), the lidar will 

detect a line-of-sight velocity given by the dot product of the wind vector (u,v,w) and the unit vector 

along the beam direction: 

⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

++

++

++=

222 ).(

zyx

zyx

LOS wvuV (5.1) 

Where the modulus implies a lack of direction sensing (i.e., the lidar is incapable of distinguishing 

motion toward/away). Eq. (5.1) simplifies to: 

222

zyx

wzvyux VLOS

++

++ = (5.2) 

5.1.2 Weighting Function 

The focusing of the lidar beam results in a spatial weighting function along the beam direction [10, 

16]. (There is also a spatial dependence of sensitivity transverse to the beam, but because the beam is 

very narrow we can ignore it.) To simulate the observed lidar spectrum, we need to calculate the 

weighted sum of all the values of VLOS for those boxes that the beam intersects. 

19
To a good approximation, the weighting function for a continuous-wave (CW) monostatic coherent 

lidar (e.g., ZephIR) is given by a Lorentzian function: 

22

/

Γ+∆

Γ = π F , (5.3) 

where ∆ is the distance from the focus position along the beam direction, and Γ is the half-width of 

the weighting function (to -3dB point; i.e., 50% of peak sensitivity). Note that F is normalized such 

that its integral from –∞ to ∞ gives unity4

. To another good approximation, Γ is given by: 

2

2

A

R

π

λ=Γ , (5.4) 

where λ is the laser wavelength (for ZephIR, 1.55 × 10-6 m), R is the distance of the beam focus from 

the lidar output lens, and A is the beam radius at the output lens (the beam intensity profile is 

assumed to be an axially symmetric 2D Gaussian; A is calculated for the point at which the intensity 

has dropped to 1/e2

 of its value at the beam center). For example: in ZephIR, A takes the value 24 

mm, so at a range R of 100 m, Γ has a value of ∼8.5 m, or a probe length (to -3 dB points) of 2 × 8.5 

m or ∼17 m. 

Further weighting may be performed to reflect the quantization of space. The contribution from each 

box should be weighted to reflect the beam/box interaction length (i.e., the length of the beam as it 

traverses each box). 

5.1.3 Velocity Estimation 

There are several options, but a calculation of centroid velocity (VC) is a good first choice (this is the 

default method in ZephIR). These resulting values of VC will provide the inputs to the control 

algorithm. In practice, with real lidar signals, the algorithm will be performed after a thresholding 

routine has been applied in order to discriminate the wind signal from the noise background. For this 

simulation, it is sensible initially to ignore noise and assume infinite lidar SNR. 

Alternative approaches to centroiding include peak value VP (the simplest option, though probably 

inappropriate in this instance where space is quantized) and median VM (which is potentially more 

robust against spurious outliers). 

5.2 Modelling of Wind Field and Derivation of Lidar Input 

5.2.1 Simulated Wind Field 

Full-field simulated turbulent wind fields are created using a code developed by NREL called 

TurbSim [26]. The TurbSim code generates a binary data file that contains u, v, and w velocity 

components at evenly spaced grid points in a plane. The grid size is generally chosen to encompass 

the wind turbine rotor with the maximum number of grid points. The number of grid points is limited 

 

4

 The following are some quantitative examples to give a feel for the cut-off of the tails of the Lorentzian. 

Integration of Eq. 5.3 shows that 80% of the signal power originates from ±3Γ of the focus, corresponding to 

the 10% points of the Lorentzian function. The 5% points correspond to ±4.3Γ, containing 86% of the signal 

power. 

20
by computer memory capability. To simulate time-varying wind fields, multiple planes are created 

corresponding to a specified time step. This simulated turbulence is considered “frozen” in that the 

structures do not evolve over time. In the FAST aeroelastic wind turbine simulation code [27, 28], 

these planes “march” through the turbine rotor at the mean wind speed for the turbulence file. An 

illustration is shown in Figure 9. A subroutine in FAST computes the u, v, and w velocity 

components for a given (x, y, z) location by linearly interpolating between the grid points. This 

subroutine was used to obtain estimates of lidar measurements by providing a series of (x, y, z) 

points corresponding to points along the simulated lidar beam to obtain corresponding u, v, and w 

velocity components. The procedure outlined in Section 5.1 was then used to estimate the lidar 

output along the vector prescribed by the lidar beam. 

θ

Time

u

v

w

x

y

z

Probe Volume

θ

Time

u

v

w

x

y

z

θ

Time

u

v

w

x

y

z

Probe Volume

Figure 9. Illustration of marching planes representing turbulent flow field and lidar 

beam 

5.2.2 Simulated Lidar Signal 

A module was added to the FAST code to simulate a lidar device. To reduce necessary code 

modification, the lidar device is not attached to the turbine in any way. For instance, if the turbine 

should yaw with a wind direction change, the simulated lidar device will remain at its specified 

location and will continue to point along its specified vector. The lidar device cannot scan the flow 

field, but the number of devices that can be simulated is unlimited. For instance, a conical scan 

pattern can be defined by 36 individual lidar devices positioned as a common off-axis angle and 

spaced at 10° azimuth increments. Each simulated lidar device produces a velocity measurement at 

each simulation time step, and this is included with standard simulation output such as the blade 

azimuth angle and blade-bending moments. Subsequent manipulation of the multiple lidar outputs is 

required to simulate a single, hub-mounted lidar device that rotates with one of the turbine blades. 

Figure 9 illustrates a lidar device located at the origin of the ground coordinate system. The origin 

occurs at the intersection of the turbine low-speed shaft with the center of the tower. The user must 

specify the Cartesian coordinates of the device, the off-axis angle (θ), and the azimuth angle that 

21
define the vector along which the lidar points. In the example shown in Figure 9, the azimuth angle 

is 0° (which corresponds to the blade 1 azimuth angle convention). The user also specifies the focal 

distance, the lidar wavelength, and the beam radius. User-specified parameters specific to the 

mathematical modeling include the tail cutoff percentage and the number of points describing the 

weighting function. The procedure outlined in Section 5.1 is used to determine the line-of-sight 

velocity along the prescribed vector to simulate lidar output at every simulation time step. 

In this study, the focal distance was selected to be 1 turbine rotor diameter (1D) to avoid 

complications with induced flow near the rotor. The lidar wavelength was 1.55e-6 m, and the beam 

radius was 0.024 m. A tail cutoff of 1% was chosen to capture the majority of the probe volume. One 

consideration for this parameter is that the simulated flow field is finite in the plane of the rotor; i.e., 

the grid of simulated turbulent wind has precisely defined y- and z-axes. The tail cutoff percentage 

specifies the distance from the focal point that is used to simulate velocity measurements along the 

Lorentzian function in Eq. 5.3. The grid size must be selected to accommodate the probe volume that 

extends from the focal point to the position defined by the tail cutoff percentage. As the focal 

distance increases, the defined volume also increases. Extending the wind field grid significantly 

beyond the turbine rotor diameter reduces the resolution of wind inputs to the aerodynamic model of 

the wind turbine simulation. The grid chosen for this study was 13 x 13, representing a square with 

44-m sides. Thirty-one weighted velocity measurement points were used to describe the Lorentzian 

function defined in Eq. 5.3. Increasing the number of averaging points beyond 31 did not 

significantly alter the statistics. It appears that the Lorentzian function is adequately represented by 

31 evenly spaced points. 

The lidar configuration defined for the study consisted of a single, hub-mounted device that was 

focused in front of one of the turbine blades by canting the lidar device to some constant off-axis 

angle. A conical scan ahead of the turbine rotor would be achieved through rotor rotation. The offaxis angle, θ, was determined by positioning the lidar such that the focal point of 1D corresponded to 

a height above the hub center of 75% of the turbine radius, or 1D * sin (θ). Thus the lidar would 

obtain the wind velocities along a ring with a radius of 75% of the turbine radius upwind of the 

turbine, a distance of 1D * cos (θ). 

To simulate this lidar configuration with the capability added to the FAST code, 36 simulated lidar 

devices were located in the wind turbine hub. They each pointed upwind of the rotor with an off-axis 

angle such that the 1D focal distance corresponded to 75% of the turbine blade radius. They were 

spaced at 10-degree azimuth increments. Thus, these 36 lidar devices observed wind speed 

corresponding to a ring with a radius of 75% of the rotor radius. A line-of-sight velocity 

measurement at each time step was output for all 36 lidar devices. 

To simulate the single lidar device rotating with a turbine blade, subsequent processing of the 36 

velocity measurements was performed. Based on the turbine blade azimuth position, the interpolated 

velocity between the two nearest lidar devices was computed. A moving average of velocity 

measurements over one complete rotor rotation was updated with each time step to provide an 

estimated uniform velocity over the rotor. 

An estimate of vertical shear was obtained by subtracting the velocity as the rotor passed 180 

degrees azimuth from the velocity as the rotor passed 0 degrees azimuth. This estimate of vertical 

shear was updated with each half of a rotor revolution. 

Within a simulated wind field, the total velocity at four points representing azimuth positions of 0°, 

90°, 180°, and 270° were averaged together at each simulation time step to create the “actual” disk 

average wind speed. This “actual” disk-average wind speed is compared to the lidar-derived moving 

azimuth average in Figure 10. Note that the lidar-based signal is derived upwind of the wind turbine 

disk. This distance produces a time shift of 2.1977 s for a mean wind speed of 18 m/s. Because the 

22
23

20 40 60 80 100 120

30

140

10

12

14

16

18

20

22

24

26

28

Turbulent field disk average

LIDAR-derived moving azimuth average

Wind Speed (m/s)

Time (s)

Figure 10. Comparison of actual turbulent flow field disk average and lidar-derived 

moving azimuth average wind speed 

lidar observes line-of-sight velocity and is canted by the off-axis angle θ, it tends to underestimate 

the total velocity vector at the focal point. The moving average derived from lidar output does not 

retain some of the high frequency wind fluctuation, but the general trends are captured. 

A comparison of the linear vertical shear across the rotor at each time step to the lidar-derived 

vertical shear is shown in Figure 11. The lidar-derived shear signal appears as a step function 

because the value at the top of the bottom of the rotor is updated every one-half rotor revolution. The 

time shift between the lidar measurement and the actual shear is evident in this figure. 
Figure 11. Comparison of actual turbulent flow field vertical shear across rotor and 

lidar-derived vertical shear 

Several considerations are unique to the simulation environment and could not be duplicated in the 

real environment. For instance, the simulated turbulence upwind of the turbine is exactly the wind 

that impinges upon the rotor. In the real atmosphere, the wind is constantly evolving such that 

measurements upwind of a turbine are never perfectly correlated with the wind that actually 

impinges upon the turbine rotor. Analysis of experimental data from the LIST experiment conducted 

at the NWTC [29] showed that correlation coefficients between disk-averaged anemometers and 

wind turbine response over a distance of 86 m ranged between 0.92 and 0.94. The correlation was 

dependent on wind speed and data sample time. These data, sampled at 1 Hz, indicate that the 

correlation between upwind measurements and turbine response can be quite good.

20 40 60 80 100 120

15

140

-5

0

5

10

Instantaneous vertical shear

Lidar-derived vertical shear

Vertical Shear (m/s)

Time (s)

24
6 Control Model: Assumptions and Objectives 

6.1 Approach 

To begin exploring the potential implications of including upwind wind speed measurements in 

sophisticated wind turbine control algorithms, this control simulation study was designed. The wind 

turbine was simulated using the FAST code [27, 28] developed by NREL. Another NREL code 

called TurbSim [26] was used to generate turbulent flow fields input to the wind turbine simulation. 

The wind turbine selected for this study was the Controls Advanced Research Turbine (CART) 

located at the NWTC. This turbine has been used for testing of sophisticated control algorithms. 

Multiple wind turbine control systems are employed for several different turbine operating regimes. 

These control algorithms can improve energy capture and reduce dynamic loads during normal 

operation. Supervisory control algorithms start the turbine as the wind speeds increase and shut the 

turbine down when the wind speed exceeds the design criteria. Wind farm supervisory control 

algorithms maximize energy capture over multiple turbines. Use of forward-looking lidar could 

enhance each of these different control systems, as described in The scope of this study was 

restricted to one particular case, and this has been highlighted in boldface type in Figure 12. 

25
I. Normal turbine Operation 

a. Region 2 

i. Energy capture improvements 

1. Assess possible improvements to current generator torque controls for

optimum Cp tracking through input of Lidar measured wind disturbances 

ii. Load mitigation 

1. Assess improvements in load mitigation control schemes from input of

symmetric and asymmetric wind disturbances. Assess reductions in rotor

drive-train and tower loads. 

b. Region 3 

i. Speed regulation 

1. Assess improvements to speed regulation control through input of

Lidar measured wind 

ii. Load mitigation 

1. Assess improvements in load mitigation control schemes from input

of symmetric and asymmetric wind disturbances. Assess reductions

in rotor drive-train and tower loads. 

c. Region 2 to 3 transition 

i. Assess potential of Lidar measured wind inputs to facilitate controller

transitioning between regions 2 and 3. 

II. Turbine Supervisory Control 

a. Startup 

i. Assess use of Lidar in measuring upwind windspeed to facilitate preparation of

turbine for startup. 

b. Shutdown 

i. Assess potential of Lidar to measure high windspeeds and prepare turbine for

shutdown before turbine experiences destructive loads. 

III. Parked Turbine loads 

i. Assess potential of Lidar to measure extreme winds at a sufficient distance 

upwind of turbine so that turbine can be prepared (rotor re-positioned or nacelle 

yaw controlled) to minimize turbine loads during high wind events. 

IV. Wind Farm Supervisory Control 

a. Explore use of Lidar to facilitate wind farm control.

Figure 12. Wind turbine control regimes that could be enhanced through the inclusion of 

forward-looking Lidar in control algorithms 

In normal turbine operation, there are two distinct operational regimes. In below-rated wind speed 

conditions (Region 2), generator torque is commanded such that the wind turbine operates at variable 

rotational speeds to maximize energy capture. In above-rated wind speeds (Region 3), the blade pitch 

angle is controlled to maintain the rotational speed at a constant value such that the turbine produces 

power at the generator’s rated level. Achieving a seamless transition between these two operational 

regimes (Region 2.5) creates additional control challenges. 

Wind turbine designers simulate operation in normal turbulent flow fields as well as discrete, 

extreme events. Designs are driven by the maximum loads obtained through simulation of extreme 

26
gusts and extreme wind direction changes and fatigue loads that contribute to lifetime damage 

calculations. As wind turbines become larger, either of these load types can become design drivers 

[30]. This study focused on the case in which the wind turbine operates normally in a turbulent flow 

field with the objective of reducing the fatigue loads. Mitigating fatigue loads reduces the 

corresponding damage, which ultimately leads to increased component life. This is one consideration 

in wind turbine design that may lead to reductions in cost of energy, and this operating condition was 

selected for further exploration in this study. 

The current study explores the inclusion of forward-looking lidar as input to a control algorithm to 

mitigate fatigue loads at the blade root in above-rated wind speed conditions. Blade root-bending 

moments are generally computed about two orthogonal axes. The flap-bending moment results from 

the force applied in the predominant wind direction. The edge-bending moment is dominated by the 

gravity force as the blade rotates. Reduction of the flap-bending moment fatigue loads is the load 

mitigation objective for this study. 

The damage equivalent load (DEL) parameter (Fe) is currently the wind industry’s primary measure 

for quantifying these amplitude variations observed over some time period in relation to the fatigue 

damage attributed to the fluctuations [31-34]. Essentially, the equivalent fatigue load weights each 

cyclic variation of the load over the record using Miner’s Rule, as shown in Eq. 6.1. The DEL 

represents a constant-amplitude, sinusoidal load applied at a constant rate over some period that 

would cause fatigue damage equivalent to that sustained by the fluctuating load amplitudes that 

result from the wind over the same period. In this study, a rain-flow cycle counting routine [35] was 

used to count full and half cycles. 

( ) m

o

i

i

m

i

e N

nF

F

1

⎥

⎥

⎥

⎦

⎤

⎢

⎢

⎢

⎣

⎡

=

∑

 (6.1) 

In above-rated wind speeds, the primary control objective is to produce rated power by regulating 

blade pitch angles to maintain a constant rotor speed. Controller performance is generally assessed 

by minimizing the Root Mean Square (RMS) of speed deviation from the desired rotor speed and 

minimizing the peak speed deviation from the desired rotor speed. This study includes the objective 

of reducing the blade root flap DEL, the peak root flap-bending moment, and the range of flapbending moments. Reducing these parameters indicates reduction in fatigue damage, which can 

extend the blade life. This simulation study explores this particular operating condition to assess the 

potential for inclusion of forward-looking lidar to improve control algorithms. Two types of 

controllers are compared, a typical Disturbance Accommodating Controller (DAC) and a DAC 

modified to include wind speed inputs obtained using lidar. 

6.2 Wind Turbine Simulation 

The wind turbine simulation was performed using FAST [27, 28]. This code uses aerodynamics 

subroutines included in AeroDyn [36, 37]. Although FAST is capable of simulating many degrees of 

freedom, this study modelled only the first blade flap-bending degree of freedom and the generator 

degree of freedom to simplify the controller design. 

The wind turbine selected for modelling is the Controls Advanced Research Turbine (CART) located 

at NREL’s National Wind Technology Center (NWTC) near Boulder, Colorado, USA [38-40]. This 

wind turbine is a two-bladed, teetered-hub, variable-speed machine, but a rigid hub was simulated in 

this study. The turbine is rated at 600 kW; it has a 43-m rotor diameter and a 36-m hub height. At 

rated power, the turbine rotates at 41.7 rpm. It is outfitted with servo-electric motors capable of 

27
pitching the blades independently, precisely, and quickly. This wind turbine was selected for this 

simulation study because a number of sophisticated control algorithms have been tested on it [41-

44]. 

Typical wind turbine controllers rely on single-input-single-output systems and are generally 

Proportional-Integral (PI) based controllers. These controllers have a single objective: speed 

regulation. Advanced controllers based on linear state-space models, such as DAC, are capable of 

achieving multiple objectives. These controllers regulate speed as well as mitigate wind turbine 

loads. Field tests at the NWTC on the CART have provided preliminary results that demonstrate the 

effectiveness of DAC compared to PI controllers [41, 44] in mitigating wind turbine loads, as well as 

regulating speed. 

6.3 Wind Turbine Controller Designs 

Sophisticated multiple-input-multiple-output (MIMO) control algorithms have been developed by 

NREL for wind turbine applications, primarily based on DAC theory [45-51]. These algorithms use 

linear, state-space representations of the wind turbine with state estimation. The wind input is 

considered a persistent disturbance that can be theoretically accommodated using a disturbance 

estimator [52]. These controllers require measurements of turbine response, such as the generator 

speed and tip deflection of each blade. In addition to these multiple inputs, multiple outputs, such as 

individual blade pitch angles, result. Individual blade pitch permits the alleviation of asymmetric 

blade loading that occurs when the mean wind profile presents a vertical shear across the entire rotor. 

For the purposes of this study, the DAC included five turbine states: rotor speed, Blade 1 tip 

displacement, Blade 2 tip displacement, Blade 1 tip velocity, and Blade 2 tip velocity. The wind 

disturbance was represented by three states: one for the uniform wind disturbance and two for the 

vertical shear disturbance. The DAC assumed measured inputs of the generator speed and the tip 

displacement of both blades. A diagram of the DAC simulation is shown in Figure 13. In addition to 

speed regulation, this controller is designed to reduce the blade load fluctuations that are induced by 

vertical shear across the rotor. 

28
theta_out

Out1 OutData

Yaw Controller

Torque Controller

Time

To Workspace

HSS Gen. Speed (rpm)

Blade1 Flap Displ.

Blade 2 Flap Displ. 

Pitch Controller

f(u)

HSS Generator Speed (rpm)

Gen. Torque (Nm) and Power (W)

Yaw Position (rad) and Rate (rad/s)

Blade Pitch Angles (rad)

OutData

FAST Nonlinear Wind Turbine

Clock

f(u)

Blade 2 Flap Tip Displacement (m)

f(u)

Blade 1 Flap Tip Displacement (m)

Figure 13. Diagram of wind turbine simulation with DAC controller 

The controller that includes wind speed information obtained from the lidar (DAC + LIDAR) is 

based on a subset of states contained in the DAC controller. In the DAC + LIDAR controller, the 

shear disturbance is measured instead of estimated (as in the DAC controller). This eliminates the 

need to estimate two of the eight states (the original DAC controller estimated eight states). The 

remaining six states are estimated in the control model: generator speed, Blades 1 and 2 flap 

displacement and velocity, as well as the uniform wind disturbance. Measurements of the vertical 

shear across the rotor as a function of blade azimuth position are provided to the controller. A big 

advantage of the DAC + LIDAR controller is that blade-tip flap deflection measurements are no 

longer needed (as in the DAC controller). 

Individual blade pitch angles are output. This controller must regulate rotor speed as well as mitigate 

blade load fluctuations related to vertical shear across the rotor. Figure 14 illustrates this controller. 

The simulated lidar measurement consists of the 36 individual measurements at 10° azimuth 

increments. The LIDAR box in the lower, left side of the figure contains the scripts that convert 

these 36 measurements into the difference between velocity at the top and bottom of the rotor plane 

(the azimuth average mean velocity is also computed, but it was not necessary for input to this 

controller). This shear value is then multiplied by the cosine of the azimuth angle to obtain the 

velocity fluctuation as a function of azimuth position. 

29
HSShftV (rpm)

theta_out

Out1 OutData

Yaw Controller

Torque Controller

Time

To Workspace

Gen speed HSS (RPM)

Blade 1 linear shear

Blade 2 linear shear

Pitch Controller

Blade 1 azimuth position

Simulated LIDAR wind speed measurements

zd2 = Vsh*cos(psi)

zd2 = Vsh*cos(psi+180) 

LIDAR - Ave. Shear, Disk Ave. Wind Speed

U(E) U

LIDAR

last 36 channels of OutData

U(E) U

HSS Generator Speed (rpm)

Gen. Torque (Nm) and Power (W)

Yaw Position (rad) and Rate (rad/s)

Blade Pitch Angles (rad)

OutData

FAST Nonlinear Wind Turbine

Clock

U(E) U

Blade 1 Azimuth Angle (Deg)

pitch

Figure 14. Diagram of wind turbine simulation with DAC+LIDAR controller 

Four simulated wind conditions were selected to input to both of the controller models. Two steady 

wind conditions with power law exponents of 0.17 and 0.3 were simulated. Vertical shear as a 

function of height above ground is described with the following equation, where m represents the 

power law exponent: 

m

g

g h

z

WzW ⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

1*)( += (6.2) 

These values were chosen because according to experimental data obtained at a site near Lamar, 

Colorado, 5-minute average shear exponents of 0.17 and 0.3 were observed 600 hours and 400 

hours, respectively, during a 1-year period [53]. These are representative of average shear exponent 

values that a wind turbine would typically endure. These cases were 40 s in duration, but the first 20 

s were neglected to exclude model startup transients. These cases show the idealized performance of 

both controllers. 

Two turbulent wind conditions with similar power law exponents were also simulated to provide a 

more representative comparison among the different control algorithms. The turbulent wind cases 

are each 140 s in duration, and again the first 20 s are excluded to avoid startup transients. The lidar 

signals are delayed by 2.1977 s to accommodate the distance from the lidar focal distance to the 

wind turbine (42.672 m * cos(22.024°) ), assuming a mean wind speed of 18 m/s. 

30
7 Control System Study: Results and Analysis 

7.1 Results 

Simulations were performed for both of the controllers with each of the four wind fields described 

above. Table 3 summarizes the results of these simulations for the two steady wind conditions. The 

performance metrics related to speed regulation are the RMS of the speed error, or deviation from 

the desired rotor speed, and the peak speed error as a percentage of rated speed. For the steady wind 

conditions, these metrics are similar for both controllers, suggesting that the primary objective of 

speed regulation is achieved. The two controllers produce similar blade flap-bending moment 

response for the low shear case, but the DAC + LIDAR controller reduced the blade flap-bending 

moment response in the high shear case. 

Table 3. Simulation Results for Steady Wind Conditions 

Steady 18 m/s wind, 

power law exponent = 0.17

Steady 18 m/s wind, 

power law exponent = 0.3

DAC DAC + LIDAR DAC DAC + LIDAR

RMS speed error (rpm) 1.5 1.5 1.9 2.2

Max speed error (% of rated) 0 0 0 0

Blade 1 root flap bending moment DEL (kNm) 53 51 83 64

Blade 1 peak root flap bending moment (kNm) 304 297 317 301

Blade 1 root flap bending moment range (kNm) 59 58 93 72

The corresponding time series signals of the flap-bending moment, tip deflection, pitch angle, and 

rotor speed are illustrated in Figure 15 and Figure 16. The DAC and DAC + LIDAR controllers 

command a sinusoidal pitch angle variation to mitigate the load variation that results from sheared 

flow. The bending moment and tip deflection signals are comparable. The rotor speed is maintained 

at the desired speed of 41.7 rpm. For this steady wind condition, the amplitude of bending moment 

fluctuations is consistent. Because the range resulting from the DAC + LIDAR controller is less than 

that resulting from the DAC controller, the DEL is also reduced. In general, it appears that the 

inclusion of the wind shear measurement gives the controller more authority over load reduction as 

shear increases than inclusion of an estimation of the wind shear. 

31
20 25 30 35 40

0

100

200

300

400

500

kNm

Root Flap Bending Moment DAC

DAC + LIDAR

20 25 30 35 40

0

0.4

0.8

1.2

m

Blade 1 Tip Deflection

20 25 30 35 40

0

5

10

15

20

deg

Blade 1 Pitch Angle

20 25 30 35 40

41

42

43

rpm

Rotor Speed

Time (s)

Figure 15. Simulated turbine operation with steady 18 m/s wind and power law shear 

exponent of 0.17 

32
20 25 30 35 40

0

100

200

300

400

500

kNm

Root Flap Bending Moment DAC

DAC + LIDAR

20 25 30 35 40

0

0.4

0.8

1.2

m

Blade 1 Tip Deflection

20 25 30 35 40

0

5

10

15

20

deg

Blade 1 Pitch Angle

20 25 30 35 40

41

42

43

rpm

Rotor Speed

Time (s)

Figure 16. Simulated turbine operation with steady 18 m/s wind speed and power law 

shear exponent of 0.3 

33
The simulation results for the two turbulent wind inflow cases are presented in Table 4. Again, speed 

regulation is achieved equally well for both controllers. With the turbulent flow field, the DAC + 

LIDAR controller reduced the blade flap DEL by 11% for the low shear case, and it is reduced by 

10% for the high shear case when compared to the standard DAC controller. The peak bending 

moment is similar for both controllers. Corresponding time series of the bending moment, tip 

deflection, pitch angle, and rotor speed are shown in Figure 17 and Figure 18. 

Table 4. Simulation Results for Turbulent Wind Conditions 

Turbulent 18 m/s wind, 

power law exponent = 0.17

Turbulent 18 m/s wind, 

power law exponent = 0.3

DAC DAC + LIDAR DAC DAC + LIDAR

RMS speed error (rpm) 8.8 8.7 8.8 8.6

Max speed error (% of rated) 1.5 1.6 1.5 1.6

Blade 1 root flap bending moment DEL (kNm) 295 263 310 278

Blade 1 peak root flap bending moment (kNm) 467 464 475 475

Blade 1 root flap bending moment range (kNm) 466 400 480 421

34
20 40 60 80 100 120 140 0

100

200

300

400

500

kNm

Root Flap Bending Moment

20 40 60 80 100 120 140 0

0.4

0.8

1.2

m

Blade 1 Tip Deflection

20 40 60 80 100 120 140 0

5

10

15

20

deg

Blade 1 Pitch Angle

20 40 60 80 100 120 140 41

42

43

rpm

Rotor Speed

Time (s)

DAC

DAC + LIDAR

Figure 17. Simulated wind turbine operation for turbulent 18 m/s wind speed and 

average power law exponent of 0.17 

35
20 40 60 80 100 120 140 0

100

200

300

400

500

kNm

Root Flap Bending Moment

20 40 60 80 100 120 140 0

0.4

0.8

1.2

m

Blade 1 Tip Deflection

20 40 60 80 100 120 140 0

5

10

15

20

deg

Blade 1 Pitch Angle

20 40 60 80 100 120 140 41

42

43

rpm

Rotor Speed

Time (s)

DAC

DAC + LIDAR

Figure 18. Simulated wind turbine operation for turbulent 18 m/s wind speed and power 

law shear exponent of 0.3 

36
7.2 Conclusions 

Inclusion of vertical shear measurements in wind turbine control for above-rated wind conditions 

appears to provide additional load mitigation capability over current state-of-the-art DAC. Damage 

equivalent flap loads were reduced by approximately 10% under turbulent wind inflow conditions 

when lidar signals were included as measurements to the controller. Additional components that 

must be considered include the drive train and tower. As the potential exists to reduce blade loads, it 

is reasonable to expect that lidar inputs to turbine controllers could serve to reduce loads on these 

other components as well. 

The DAC controller requires measurements of the tip deflection of each blade for input to the 

controller. This type of measurement is not feasible for wind turbines, so this type of controller could 

not currently be implemented in the field. Some preliminary work has been done to use bending 

moment measurements as input to the controller because this type of measurement is possible to 

obtain. Because the lidar measurement is a feasible signal to add to controller inputs, the DAC + 

LIDAR controller could potentially be implemented. 

Similarly, reduction in fatigue loading is only one aspect that will ultimately lead to design changes 

that result in cost of energy reductions. The loads resulting from extreme, discrete load cases that 

prescribe gusts and wind direction changes must also be reduced. Again, it is reasonable to expect 

that Lidar inputs to controllers could serve to reduce these extreme loads. 

This simulation study was performed on a relatively small wind turbine by today’s standards. The 

characteristics of lidar Configuration #1 are sufficient for this size of turbine. The focal distance of 

1D is 43 m, which is easily achieved with Configuration #1. However, for a larger turbine exceeding 

100 m in diameter, the extended focal distance of Configuration #2 may be required. Another 

consideration for application to larger turbines is the rotor speed. Very large turbines (rated at 5 

MW), which are being considered by many turbine manufacturers, have rotation speeds of 12 rpm, 

which is significantly lower than the 42 rpm rotor speed in this study. Because the vertical shear 

computation is dependent on the rotor speed (Configuration #1 does not include internal scanning 

capability but relies on rotor rotation for scanning), slower speeds extend the time between 

measurements at the top and bottom of the rotor. This could result in a less accurate measurement of 

the vertical shear impinging on the rotor. 

Finally, field testing is necessary to ascertain the effectiveness of including lidar-based controller 

inputs in wind turbine operation. Different operating regimes have different control objectives, and 

the transition between these regimes occurs frequently. It is only through field testing that these 

control systems can be thoroughly evaluated. Also, field testing is required to address concerns 

regarding the true correlation between upwind flow measurements and the turbine response. These 

differences cannot be simulated with current simulation capabilities. 

37
 

8 Conclusions 

The work presented here represents the first study (to our knowledge) that explores the potential of a 

turbine-mounted laser anemometer to enhance capabilities for wind energy production. The study 

has combined lidar know-how from QinetiQ with turbine design and control system expertise from 

the NWTC. 

A number of cost-effective lidar options have been explored, and it has been shown that a system 

based on optical fiber operating at a wavelength close to 1.55μm will provide an economically viable 

solution. This technology is also well-proven, having been successfully deployed on the only two 

turbine-mounted lidar experiments of which we are aware. 

The potential advantages of advance warning of wind speed fluctuations have been reviewed. One 

aspect has been examined in detail with a modelling study of a specific wind turbine control system, 

and the promising results obtained demonstrate the possible improvement in load mitigation. A full 

assessment of the advantages awaits further work on the remaining aspects identified in this study.

38
 

9 Recommendations and Proposed Future Work 

9.1 Extensions to the Current Study 

As a first study in the field of turbine-mounted lidar, this work has identified a number of areas for further 

research: 

• There is a need for more modelling investigations to examine some of the other regimes listed in 

Figure 12. Controllers that include lidar-based wind measurement inputs must be specifically 

designed for each of the operating regimes. 

• Eventually the modelling studies must extend to larger-scale turbines to provide a realistic estimate 

for the reduction in the COE. Current industry standard machines are rated at 1.5 MW with rotor 

diameters on the order of 80 m, but many companies are developing even larger turbines. 

• It would be desirable to conduct experiments on turbine-mounted lidar in order to verify the results of 

the modelling work. Any scheme would ideally retain enough flexibility to explore different scan 

patterns and focus range options. 

• The lidar experiments suggested above will also provide the capability for a wider range of 

investigations, including power curve measurement and turbine wake and shadowing studies. 

9.2 Other Lidar Capabilities 

We outline briefly two possible future lidar developments with potential for wind-energy-related research. 

A three-axis scanned ground-based lidar system could examine wind field details for, e.g. turbine inflow. 

The main elements of such a system would be three lidar transceiver units, connected to a central rack and 

processing unit. Direction sensing is almost certainly required to resolve a six-fold ambiguity, adding 

considerable expense and complexity. The optimum baseline separation will depend on deployment 

details but will be of order 100 m. The three scanner units must ensure approximate coincidence in space 

of the three lidar foci (perfect overlap is not needed), from which the three components of wind speed (u, 

v, w) can be computed. 

An additional option to be considered is a long-range pulsed system to provide information on 2D wind 

vector at any “point” in a volume of space out to ranges of 3-5 km. This would consist of two pulsed 

lidars on a baseline of ∼1km. (Full 3D vector is not really possible because it is impractical to mount the 

third lidar in a location well outside the scan plane. QinetiQ has pioneered the use of dual-Doppler lidar 

for pollution dispersion studies and has several publications on the results of field trials and analysis of 

important issues [54]. While these studies used CO2 laser sources (at a wavelength 10.6μm), we do not 

believe this technology provides an adequate solution to the current problem. Established 2μm lidars are 

well suited to this task, but it would also be possible to develop a system based on fiber/telecoms 

components at wavelength 1.5um. This would require some R&D to develop a laser source with the 

required pulse energy, repetition rate, and coherence properties; recent developments have been rapid in 

this area. 

39
 

10 References/Bibliography 

[1] Zak, J. A. Atmospheric Boundary Layer Sensors forAapplication in a Wake Vortex Advisory System.

NASA/CR-2003-212175. Hanover, MD: NASA Scientific and Technical Information Program Office, 

April 2003. 

[2] Durst, F.; Melling, A.; Whitelaw, J.H. Principles and Practice of Laser Doppler Anemometry. 

London: Academic Press, 1976. 

[3] Adrian, R. J. “Particle-Imaging Techniques for Experimental Fluid Mechanics.” Ann. Rev. Fluid 

Mech; Vol. 23, 1991; p. 261. 

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44
 

A Appendix: Abbreviations and Definitions 

ADC analog-to-digital converter 

AOM acousto-optic modulator 

CART Controls Advanced Research Turbine 

CLR coherent laser radar 

CDL coherent Doppler lidar (n.b. CLR, CDL, and “reference beam LDA” are precisely 

equivalent) 

COE cost of energy 

CW continuous wave 

DAC disturbance accommodating controller 

DEL damage equivalent load 

DFT digital (or discrete) Fourier transform 

DGV Doppler global velocimetry 

DIAL differential absorption lidar 

FFT fast Fourier transform 

FPGA field programmable gate array 

LDA laser Doppler anemometry 

LDV laser Doppler velocimetry 

LO local oscillator 

LWST Low Wind Speed Technologies 

MPE maximum permissible exposure 

MIMO multiple-input-multiple-output 

PIV particle imaging velocimetry 

PM polarization maintaining 

RMS root mean square 

SAW surface acoustic wave 

SBS stimulated Brillouin scattering 

SNR signal-to-noise ratio 

λ lidar wavelength 

ν laser frequency 

δν Doppler frequency shift 

VLOS line-of-sight component of wind velocity 

PT transmitted laser power 

PS received signal power 

β(π) backscatter coefficient 

Γ half-width at half height of lidar axial sensitivity function 

x horizontal direction along turbine axis (+ve x in upwind direction) 

y horizontal direction perpendicular to turbine axis 

z vertical direction 

VC centroid velocity of a given spectrum 

F axial lidar sensitivity weighting function 

A beam radius at lidar output (to 1/e2

 in intensity) 

R distance from lidar aperture to beam focus 

∆ distance from focus along beam direction 

θ off-axis angle for lidar device, deg 

h wind turbine hub height, m 

i number of cycle counting bins 

m material constant, exponent for the S-N curve (10 for fiberglass composite material), or 

vertical wind shear exponent 

45
 

n number of rain-flow cycles in the ith bin 

u,v,w wind velocity components corresponding to x, y, z coordinates, respectively, m/s 

x,y,z blade element coordinates in aerodynamic code coordinate system, m 

zg height above ground level, m 

Fi amplitude of the ith cycle counting bin, kNm 

Fe equivalent fatigue load, kNm 

No cycles over 10-minute period (840) 

W wind speed, m/s 

46
 

B Appendix: Table of ZephIR Specifications 

This table lists the essential specifications of the ZephIR production instrument. These provide an 

accurate representation of the expected requirements of a turbine-mounted lidar system. 

Wavelength 1.55 μm 

Output power Up to 1 Watt 

Telescope aperture diameter 72 mm 

Focus range from 5 m to 200 m 

Detector InGaAs photodiode 

Sample rate 100 MHz 

FFT size 512, giving 256 bins in 50 MHz 

spectrum 

Number of spectral averages 4000 

Minimum detectable wind speed 0.14 m s-1

Maximum detectable wind speed 38.7 m s-1

Bin size (velocity resolution) 0.15 m s-1

Data rate ∼50 measurements per second 

47
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Lidar for Turbine Control: March 1, 2005 – November 30, 2005 

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QinetiQ, Inc., P.O. Box 4365, Philadelphia, PA 19118 

National Renewable Energy Laboratory, 1617 Cole Blvd. 

Golden, CO 80401-3393 

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14. ABSTRACT (Maximum 200 Words)

This work represents the first study (to our knowledge) that explores the potential of a turbine-mounted laser 

anemometer to enhance capabilities for wind energy production. The study combines laser anemometry (lidar) knowhow from QinetiQ, Malvern, United Kingdom, with turbine design and control system expertise from the National 

Wind Technology Center (NWTC) at the National Renewable Energy Laboratory (NREL), Colorado, United States of 

America. 

15. SUBJECT TERMS

wind energy; lidar; laser anemometry; wind speed measurement 

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