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Contract No. DE-AC36-08GO28308

Lidar-Enhanced Wind Turbine 

Control: Past, Present, and 

Future

Preprint

Andrew Scholbrock, Paul Fleming, Alan Wright, 

and Na Wang

National Renewable Energy Laboratory

David Schlipf

Stuttgart Wind Energy

Kathryn Johnson

National Renewable Energy Laboratory and Colorado 

School of Mines

Presented at the 2016 American Control Conference

Boston, Massachusetts

July 6‒8, 2016

Conference Paper

NREL/CP-5000-65879

July 2016
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Lidar-Enhanced Wind Turbine Control: Past, Present, and Future

Andrew Scholbrock1, Paul Fleming1, David Schlipf2, Alan Wright1, Kathryn Johnson1,

3

and Na Wang1

Abstract— The main challenges in harvesting energy from the

wind arise from the unknown incoming turbulent wind field.

Balancing the competing interests of reduction in structural

loads and increasing energy production is the goal of a wind

turbine controller to reduce the cost of producing wind energy.

Conventional wind turbines use feedback methods to optimize

these goals, reacting to wind disturbances after they have

already impacted the wind turbine. Lidar sensors offer a

means to provide additional inputs to a wind turbine controller,

enabling new techniques to improve control methods, allowing

a controller to actuate a wind turbine in anticipation of an

incoming wind disturbance.

This paper will look at the development of lidar-enhanced

controls and how they have been used for various turbine load

reductions with pitch actuation, as well as increased energy

production with improved yaw control. Ongoing work will also

be discussed to show that combining pitch and torque control

using feedforward nonlinear model predictive control can lead

to both reduced loads and increased energy production. Future

work is also proposed on extending individual wind turbine

controls to the wind plant level and determining how lidars

can be used for control methods to further lower the cost of

wind energy by minimizing wake impacts in a wind farm.

I. INTRODUCTION

Power generation from wind energy is complex, mainly

due to the fact that the energy source for a wind turbine is

largely unknown until the energy source impacts the turbine.

This has inspired many research topics on controls for wind

turbines, all with a similar goal: to minimize the levelized

cost of energy (LCOE). This general goal can be broken into

two major subgoals: increasing the energy production of a

wind turbine, and reducing the loads experienced by a wind

turbine to extend the turbine lifetime.

An example of using controls to reduce the loads experienced by a wind turbine is to control the pitch of the blades.

Initial constant-pitch wind turbines experienced large aerodynamic loading on the blades when a wind gust would impact

the turbine [1]. With the advent of variable-pitch blades

and controls, wind turbines were able to pitch the blades to

feather, reducing the aerodynamic loads experienced by the

wind turbine [1]. This led to wind turbines being redesigned

to produce the same or larger amount of energy with reduced

materials in the wind turbine.

This example does not address the fact that the controller

used a feedback design, meaning that the wind turbine is

1National Renewable Energy Laboratory 15013 Denver West Parkway Golden, CO 80401. NREL is a national laboratory of the

U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, operated by the Alliance for Sustainable Energy LLC.

andrew.scholbrock@nrel.gov

2Stuttgart Wind Energy, Universitat Stuttgart, Allmandring 5B, 70569  ̈

Stuttgart, Germany.

3Colorado School of Mines, 1500 Illinois St., Golden, CO 80401.

Fig. 1. Lidars mounted on two- and three-bladed research wind turbines at

the National Wind Technology Center. (Photo by Lee Jay Fingersh, NREL

32910)

reacting to the wind disturbance that has already impacted

the wind turbine. In essence, this is similar to driving a

car while blindfolded and reacting to how the car feels. To

address this, light detection and ranging (lidar) technology

has been proposed as a new sensing capability to measure the

wind before it impacts the turbine [2]. In the driving analogy,

this allows the blindfold to be removed to see ahead and

anticipate control inputs before reacting to a disturbance. An

initial simulation study showed good promise that including

lidars with wind turbines could lead to lowering the LCOE

of wind turbines [2].

Other research has followed the example of [2] using lidars

to enhance wind turbine controls. This has included field

testing where lidars have been mounted on the nacelles of

wind turbines as seen in Fig. 1. The goal of this paper is

to survey the lidar-enhanced wind turbine controls research

and draw conclusions based on this research.

II. BACKGROUND INFORMATION

A. Wind Turbine Control Basics

Most utility-scale wind turbines today are configured as

upwind horizontal-axis machines and are capable of running

at variable speeds through command of the torque of the

generator. Further, most wind turbines also have the capability of varying the pitch angle of the blades. For largeThis report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. 1
scale turbines, the nacelle is actively controlled to change

the turbine’s yaw direction so that the turbine is aligned with

the wind direction. These three actuation methods, torque,

pitch and yaw, are three typical control actions of a wind

turbine [3] and are discussed throughout this paper.

A typical utility-scale wind turbine operates in three

regions: Region 1 - the turbine sits idle waiting for the wind

speed to increase as there is not enough wind to operate

the turbine, Region 2 - the turbine’s rotor speed is varied to

maximize the rotor efficiency with a fixed blade pitch, and

Region 3 - the turbine’s generator torque is fixed and the

blade pitch is varied to control a fixed rotor speed. Figure 2

illustrates the different regions and how the power production

varies from region to region. The red curve in Fig. 2 shows

the power available in the wind as given by equation 1:

Pwind =

1

2

ρAv3(1)

where Pwind is the power available in the wind, ρ is the air

density, A is the rotor area, and v is the wind speed. The

blue curve in Fig. 2 accounts for the aerodynamic efficiency

of the wind turbine, given by the ratio in equation 2:

Cp =

P

Pwind

(2)

where Cp is the power coefficient, P is the power captured

by the wind turbine, and Pwind is the power available in the

wind. Cp is governed by many factors, but two of interest are

the blade pitch angle and the tip speed ratio, which relates

the blades tip speed to the wind speed as seen in equation 3:

λ =

ωR

v

(3)

where λ is the tip speed ratio, ω is the rotor speed, R is the

rotor radius and v is the wind speed.

Due to turbulence, the wind speed varies from one point

in the rotor to another and therefor the wind speed is not

typically used as a feedback signal to the controller. Instead,

the generator speed is used as a feedback signal, and in

Region 2, the commanded torque is determined from the

generator speed using equation 4:

τ = Kω2(4)

where τ is the generator torque, ω is the generator speed,

and K is a constant given by:

K =

1

2

ρπR5 Cpmax

λ3

∗

(5)

where CPmaxis the maximum power coefficient and λ∗ is

the tip speed ratio at the maximum power coefficient. Thus,

when the wind speed increases, the rotor (and generator)

accelerates, and the commanded torque increases. This in

turn decelerates the rotor, balancing the acceleration from

the wind.

In Region 3, the commanded torque is held constant at

the rated torque value. In this region, the blade pitch angle

is varied to change the Cp value of the turbine. Hence, if the

wind speed increases, the rotor begins to accelerate, and the

Fig. 2. Power curves for different wind speeds. The “Available Wind

Power” red curve is the total power available at a given wind speed, and

the “Turbine Power” blue curve is the power that a wind turbine is able to

produce at a given wind speed. The different regions of operation are given

at the top of the figure.

controller varies the blade pitch angle to lower the Cp value,

which in turn decelerates the rotor until a balance is found.

For some controllers, there exists a transition region between

Region 2 and Region 3, known as Region 2.5, where the

rotor speed reaches its rated value before the generator torque

reaches its rated value. Controllers use different techniques

to ensure a smooth transition, such as a generator-slip control

method. For more detail on wind turbine control, refer to [3].

In addition to torque and pitch control, a horizontalaxis wind turbine needs the rotor to be aligned with the

wind direction to produce the maximum possible power. As

mentioned earlier, this is done actively for large scale wind

turbines, which traditionally rely on a nacelle-mounted wind

vane to align it with the wind direction. Building off of

equations 1 and 2, the theoretical power captured by a wind

turbine can be expressed by the following equations [4]:

Pmax =

1

2

ρACP V

3

p

(6)

Vp = V0cos (θE) (7)

Equation 6 shows that the maximum power (Pmax) that

could be captured depends on the wind speed perpendicular

to the rotor plane (Vp). In equation 7, the perpendicular

component of the wind speed is expressed in terms of the

freestream wind speed (V0), and the cosine of the yaw

alignment error (θE) and is illustrated by the green vector

component in Fig. 3. Combining these two equations implies

that a yaw misalignment reduces the power produced by the

wind turbine, hence it is the goal of a yaw controller to

minimize the yaw misalignment.

B. Lidar Basics

To remotely measure the wind velocity, a lidar sends out a

laser beam which reflects back to the lidar from particulates

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. 2
in the atmosphere. A comparison of the sent and reflected

wavelength is then performed, and the Doppler effect is

used to derive the wind speed [5]. To get a high quality

measurement, there needs to be enough backscattered light

from the atmosphere to have a high carrier-to-noise ratio.

Different factors can impact the carrier-to-noise ratio of the

lidar measurement, including the amount of particulates in

the atmosphere, weather conditions, as well as the installed

location on a wind turbine [6].

There are two types of lidars that use different methods to

calculate a wind speed measurement. The first type of lidar

is a continuous-wave lidar (illustrated in Fig. 4), which uses

a laser beam that is focused at a desired distance ahead of

the lidar, known as the focal distance. The second type of

lidar is a pulsed lidar, which uses a timing-based method that

waits for the reflected light to return at different times after

a pulse of light is emitted from the lidar, thereby allowing it

to measure the wind speed at different distances [7].

These two methods measure the wind speed along the lineof-sight (LOS) of the laser beam as illustrated in Fig. 3.

To estimate a wind velocity, a lidar needs to take multiple

measurements in different directions, which can be done

either by scanning the wind field with a rotating beam

(seen in continuous-wave lidar systems) or by switching the

beam between different staring positions (seen in pulsed

lidar systems). The angle between the lidar’s center line

and the LOS measurement is known as the half-cone angle

as illustrated by φ in Fig. 3. Simulation studies have been

done to determine the optimal settings of the focal distance,

half-cone angle, and measurement frequency [7], [8]. Additionally, the correlation between the lidar measurements and

the turbine’s reaction can be analytically determined based

on the spectral properties of the wind. This allows directly

optimizing a lidar scan configuration, avoiding extensive

simulation studies [9], [10]. These studies have shown that

different settings are optimal for different control objectives.

For example, a wider cone angle is more desirable when

trying to measure wind direction, even though this diminishes

the measurement quality of the wind speed [7]. Additionally,

a lidar could be mounted in the hub of a wind turbine in such

a way that the lidar measurement will be rotating with the

rotor’s speed, allowing for the lidar wind measurement to

have a higher coherence with the wind experienced by each

blade [10].

The scanning/staring methods use a single lidar system,

and therefore the LOS measurements are taken at different

locations and at different times. To resolve the LOS wind

speed measurements into signals for control such as the

rotor-effective wind speed, wind shears, and wind direction,

some assumptions are needed and wind field reconstruction

methods need to be applied [11], [12], [13]. Another method

is to use several lidar systems in different ground-based

locations that are controlled to measure the same atmospheric

location [5].

Fig. 3. An example of a nacelle-mounted lidar with two LOS beams

shown in red and the corresponding LOS wind measurements shown in

purple. The turbine in blue is shown with a yaw misalignment, θE, and the

wind component perpendicular to the rotor plane, Vp, is shown in green.

The angle φ is the lidar’s half cone angle. For simplicity, a uniform wind

field is shown as black vectors, V0.

III. LITERATURE REVIEW

A. Online Lidar Data Processing

Before the lidar measured data can be used for control in

wind turbines, the lidar data must be processed to transform

LOS wind speed measurements into signals that can be

readily used by the turbine controller. Having a high coherence between the lidar measurement and what is processed

to be the estimated rotor-effective wind speed is essential

for feedforward control to be advantageous. In [14], it was

shown that an increasing amount of coherence will lead to

greater improvements from using feedforward control.

Because the lidar data are being measured ahead of the

wind turbine, some delay needs to be taken into account for

the measured wind to travel to the wind turbine. A simple

way to resolve this is to use Taylor’s frozen turbulence

hypothesis [15], which states that the time (t) it will take

for the wind to reach the turbine is equal to the measured

distance (d) divided by the mean flow velocity (V ̄ ) as seen

in equation 8.

t =

d

V ̄

(8)

In [16], it was found from field experiments that Taylor’s

frozen turbulence hypothesis was suitable for wind turbine

control. Improvements can be made on the coherence between the lidar measurement of the wind speed and the

rotor-effective wind speed by avoiding the Taylor hypothesis,

by decoupling the time delay from the mean wind speed in

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equation 8, which has been done in simulation [17], and

additionally from analyzing field test data [18].

The turbulence of the wind must also be considered in

that eddies measured by the lidar will evolve over time

and not necessarily correlate to the wind that is experienced later by the wind turbine. Smaller eddies dissipate

on short timescales, and trying to control them is unnecessary, whereas larger eddies are more coherent over

longer timescales. Hence, it is necessary to filter the lidarmeasured wind speed so that lower-frequency measurements

are controlled for, and higher-frequency measurements are

filtered out [19]. Additional data processing using wind

measurements taken from different distances can be used to

improve the coherence between the lidar measurement and

the rotor-effective wind speed [13].

Additional consideration of the current atmospheric conditions the wind turbine experiences could be taken into

account when optimizing the lidar’s processing parameters

for wind turbine control applications. As conditions vary,

the turbulence statistics will change and it will be desirable

to change the settings of a lidar to optimize the measurement

for these differences. In [20], simulation studies showed that

a longitudinal spatial coherence model could be developed

with varying atmospheric conditions. From the model developed, the work in [20] went on to evaluate the optimal scan

radius and the optimal preview distance for a continuouswave lidar as a function of the turbulence intensity and mean

wind speed. Results in [20] showed that the optimal scan

radius increased for lower mean wind speeds, and the optimal

preview distance increased for lower turbulence intensities.

B. Pitch Control

Feedforward lidar control differs from standard wind turbine feedback control in that control decisions are made in

anticipation of future events, and with lidars this is based on

a measurement of the wind field ahead of the wind turbine.

As mentioned in Section III-A, after the lidar measurement

is made, data processing is necessary to account for the wind

evolution as it transports to the wind turbine. This process

is illustrated in the red blocks in Fig. 5. An advantage of

this design is that if a fault is detected in the feedforward

process, it can simply be switched off, and the feedback only

portion of the controller can take over the control of the wind

turbine.

An initial study looked to combine a standard feedback

controller with a feedforward blade pitch controller using

lidar wind preview measurements [21] like the process shown

in Fig. 5. The controller design allowed for a preview pitch

angle to be determined from the lidar wind measurement,

and known rotor aerodynamics using a nonlinear look-up

table of the static pitch angle. Using a FAST simulation

environment, results showed that with the preview control

a decrease of 70%-80% in the standard deviation of the

rotor speed was achievable without increasing the pitch rate

activity. The work in [22] looked to further investigate the

results from [21] by using a model-inverse technique: NonCausal Series Expansion (NCSE). Using this method with a

Fig. 4. Illustration showing a lidar mounted on top of the wind turbine

nacelle showing a circular scanning pattern from a continuous-wave lidar.

(Illustration by Al Hicks, NREL)

FAST simulation setup, improved results were found, where

blade flap and tower fore-aft damage equivalent loads (DELs)

were reduced using feedforward-feedback control over using

a baseline feedback-only controller. Additionally, this came

at no loss in power production [22].

In another study [23], simulations were performed to assess the effectiveness of using blade pitch control with wind

preview measurements. The simulations in [23] assumed no

wind evolution between the lidar measurement and wind

turbine. Two types of controllers were designed in [23], a

model-based controller with preview wind measurements,

and a nonmodel-based preview controller. Simulation results

showed that blade flap loads could be reduced by using the

rotating preview measurements, as long as the measurements

correlated well with what each blade experienced. If there

was an error between the wind preview measurement and the

wind that impacted the blade, then the feedforward controller

would perform worse than the feedback-only controller [23].

It was noted in the analysis of [23] that when simulating

lidars, wind evolution should be taken into account for

the simulations to be more realistic and to better design a

feedforward controller.

Another study [24] compared two independently developed feedforward controllers from [25] and [26], as well as

two different lidar configurations, one a rotating continuouswave lidar, and the other a pulsed lidar. Although the feedforward pitch controllers were developed using very different

methods, the simulation results showed similar performance

for the two controllers in terms of load alleviation [24]. It

was noted in [24] that the controller from [26] was simpler

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to design and more tunable, making it better for real-world

applications. It was also noted in [24] that the pulsed lidar

configuration is more realistic and provides more accurate

wind speed estimates; however, the rotating continuous-wave

lidar is capable of producing better blade effective wind

speeds. The blade effective wind speed is an estimate of

the wind that flows around the blade in order to produce

torque for the generator. A later study [27] looked to extend

the control techniques used in [25], using a H∞ control

design for a floating offshore wind turbine. With the use

of lidar, simulation results showed that a H∞ feedforwardfeedback controller was able to reduce the standard deviation

of the rated generator speed by 44% and load mitigation was

observed as well [27]. Later, a study [7] used the Bladed

turbine simulator, rather than the FAST turbine simulator that

was used in [23], [22], [24]. Despite the different turbine

simulator, controller performance results were similar and

showed a reduction in the blade root flap moment by 6%-

12%, and a reduced tower base fore-aft moment by 16%-

20%.

All of the work discussed so far in this subsection has

been analysis by simulation alone; however, field test results

have been achieved as well. In [19] a scanning pulsed lidar

was installed on the nacelle of a wind turbine, and the lidar

data were integrated into the turbine’s supervisory control

and data acquisition (SCADA) system in real time. This

allowed the lidar’s LOS measurements to be directly fed to

the controller. The controller was designed to use the lidar

preview measurement to allow for combined feedforwardfeedback control by applying a feedforward pitch rate update

to the standard feedback controller. Additionally, Taylor’s

frozen turbulence hypothesis was assumed to determine the

time delay between the lidar measurement and when the

wind impacted the turbine. A similar method was used

in [28]; however, a different turbine was used as well

as different pulsed lidar with three fixed beams. Results

from both [19] and [28] showed that improved rotor speed

regulation was achieved by using the lidar-based feedforward

controller. Later, a followup to these studies was done [29]

and looked to improve the online data processing of the

lidar measurement to correlate better with the rotor-effective

wind speed using some of the techniques described in

Section III-A. Field tests were done using a continuous-wave

lidar in [29] and results showed that the online correlation

did improve between the lidar measurement and the rotoreffective wind speed, which could be used to improve a lidarbased feedforward controller [29].

To summarize lidar-enhanced pitch control, it was found

early on that it is important to include wind evolution in a

lidar model when running simulations [23]. An additional

study compared two controllers, one using a pulsed lidar,

and the other using continuous-wave lidar, and found that

both lidar configurations were capable of reducing turbine

loads [24]. Improvements to lidar-turbine simulation environments and controller design led to load reductions in

the blade flap root bending moment, and the tower base

fore-aft bending moment with no change in the energy

Fig. 5. An example of a lidar-based feedforward controller. The red

blocks are additional control blocks for feedforward control added to the

conventional feedback controller.

production [7], [27], [22], [26]. Lastly, field tests were also

conducted that showed improved rotor speed regulation is

achievable using lidar-based feedforward pitch control [19],

[28], [29].

C. Torque Control

Similar to pitch control, a lidar could be used to provide

preview wind speed information to the torque control aspect

of the wind turbine controller when the turbine is operating

in Region 2. Using the preview information, a controller can

better track the optimal tip speed ratio of the wind turbine,

which in turn will improve the aerodynamic efficiency of

the rotor. In Fig. 5, changing the pitch command to a torque

command in the block diagram would show a similar process

for feedforward torque control.

In [30], an aeroelastic simulator was used to evaluate

a controller with a lidar as an input for optimal torque

tracking. Results from [30] showed that by using the optimal

torque tracking controller, an improvement of 0.09% in

power production could be achieved; however, this came

at a cost of increasing the DEL of the rotor speed shaft

by 8.9%. The conclusion from [30] was that the power

performance gains were marginal, and were not cost-effective

when considering the increase in turbine loads. Similarly,

in [7], a different turbine simulation model was used and

saw a 0.2% increase in power production at the expense

of unacceptable torque loads in the drivetrain. Additionally,

in [31], a disturbance-tracking controller and an optimally

tracking rotor controller, both augmented using lidar as well

as a lidar-based preview controller, were developed and

tested in simulation. Results from the three controllers in [31]

showed that power performance was improved by less than

0.5% with an increase in the rotor shaft torque DEL by at

least 5.7%.

The work in [30] was extended in [32] to better account

for the wind evolution between the lidar measurement and

the rotor-effective wind speed. In [32], field test data were

gathered and used as wind field inputs for aeroelastic simulations. In the simulations, an improvement in the optimal

torque tracking was observed when using the lidar in Region

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2, which was calculated to have a 0.3% power production

increase; however, this was again at the expense of the rotor

shaft DEL approximately doubling.

Another torque controller was developed in [33], with

special consideration given to the Region 2.5 portion of the

torque controller, where the generator speed reached its rated

value but the generator torque has not. In [33], a disturbanceaccommodating control technique was used in combination

with a continuous-wave lidar feedforward input to develop

the controller. Simulation results showed that rotor speed

regulation could be improved in Region 2.5, which showed

promise for rotor shaft torque load mitigation. Although no

power performance change was observed in these results, the

improved rotor speed regulation without much cost in turbine

loads was an improvement from other research discussed

in this section on lidar feedforward torque control. In all,

the research with feedforward torque control was not as

promising as pitch control for improved performance.

D. Yaw Control

As mentioned earlier, it is the goal of a yaw controller

to minimize yaw misalignment in order to maximize power

production. For an upwind turbine, the yaw system can have

a potential bias in the measurement because the vane is

located in the near wake of the rotor, leading to a steady

state yaw misalignment offset that the controller will not be

able to account for. With a nacelle-mounted lidar, a wind

direction measurement can be made ahead of the turbine

and averaged over multiple points, reducing any biases in

the wind direction measurement.

In [7], it was discussed that lidars could be used to improve

yaw control of wind turbines; however, the simulations in

the study were too short to draw any definitive conclusions.

Additionally, in [30], field test data were gathered using a

nacelle-mounted lidar on a wind turbine. Analysis comparing

the lidar-measured wind direction to the wind turbine yaw

direction showed that a yaw misalignment of 0.7◦was found,

leading to a power loss of about 0.02% based on equations 6

and 7. Since then, a field test experiment showed that a

nacelle-mounted lidar could be used to calibrate the conventional wind vane measurement system and correct any

bias in the measurement [34]. A 2.4% increase in the annual

energy production was found in this study by using the lidar

to correct for a bias [34]. The amount of increase can vary

from turbine to turbine, as it depends on how much of a bias

there is in the system to begin with. Additionally, another

study showed that a lidar can be directly used to control

the yaw set point of a wind turbine instead of using the

lidar to calibrate the wind vane [35]. This study also showed

similar results to [34], in that power improvement can be

obtained when a bias exists in the conventional wind vane

measurement system. The advantage to the method in [34]

is that the lidar only needs to be installed on the turbine

until the calibration is determined. It can then be moved to

the next turbine. The advantage to the method in [35] is that

it removes the nacelle wind vane from the control system,

eliminating the need to perform periodic calibrations.

E. Combined Controllers

A combined controller for a wind turbine uses a combination of the techniques described in Section III-B, Section IIIC or Section III-D to develop a further optimal controller that

balances the often-competing objectives of maximizing energy production and minimizing turbine loads. In [36], a nonlinear model-predictive controller (NMPC) was developed to

use lidar measurements with predictions of the turbine state

trajectories. Using the combined NMPC technique, simulation results from [36] showed reductions in turbine loads,

and an increase in energy production. Another controller

was developed using a flatness-based controller with lidar

preview measurements to control both torque in Region 2

and pitch in Region 3 [37]. Using this approach, comparisons

were made to a conventional feedback controller and a

simple feedback-feedforward controller. The flatness-based

controller showed improvements over both of the other

controllers, with reductions in turbine loading, reductions in

control actuation, and an increase in power production [37].

These results are promising as both load reduction and power

increase are observed, whereas other studies usually show a

trade-off between the two. The drawback to these controllers

is that the models are computationally expensive and were

not able to run in real time; however, this could be overcome

in an industrial application by using hardware with adequate

computational power.

In [38], a Region 3 combined feedback-feedforward controller was extended with a Region 2.5 combined feedbackfeedforward controller using a linear-quadratic-based preview collective pitch control scheme. Turbulent wind simulation studies showed that by including feedforward control in

Region 2.5, energy production could be improved by 3%,

while the feedforward Region 3 portion of the controller

was able to reduce the tower base fore-aft loading by more

than 10% without diminishing power production or reducing

the rotor speed regulation [38]. Yet, the pitch actuator usage

was increased [38]. This controller shows promise that both

load reduction and increased energy production are possible

when different regions of the controller are combined with

feedforward controls.

F. Lidar applications for wind farm control

Recently, some research has focused on controls that improve the global performance of a wind farm, by coordinating

the controls of individual turbines. Because neighboring wind

turbines can impact each other through their wakes, there is

potential for improved power output and reduced loading by

trying to steer the wake propagation.

Many initial studies have been based on simulation and

use “open-loop” control strategies that seek to determine the

general controllability of wakes (see for example [39], [40],

[41]). Studies such as these and more recent results from

field testing and wind-tunnel tests have indicated promise

for this approach [42] using indvidual pitch control and to a

greater extent using yaw control.

However, when considering the complexity of controlling

wakes within a wind farm, the applicability of lidars to

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. 6
the problem becomes apparent. Lidars provide a means for

making measurements of the flow and estimating the wake

locations for use in feedback [43]. Additionally, for wind

farm control systems that use the yaw control system to

steer wakes, a strong dependence is placed on accurately

applying a particular misalignment between the turbine rotor

and incoming wind mean flow direction. As shown in the

work improving turbine yaw alignment error [34], [35], lidars

can be very effective in providing an accurate measurement

of wind turbine yaw alignment with the wind direction.

IV. ONGOING WORK

In Section III-B, many papers were discussed in which

feedforward pitch was used to reduce turbine loads in simulation. Additionally, two papers ([19], [28]) in that section

also discussed feedforward pitch control field test results;

however, the results were limited to improved rotor speed

regulation in above rated winds. Field test work has been

ongoing to extend this to reduce loads as predicted by the

simulation studies. This is done by retuning the feedbackfeedforward gains such that the feedback portion of the

controller is used to control the loads of the wind turbine, and

the feedforward portion of the controller is used to control the

rotor speed in Region 3. Initial results [44] have shown that

by retuning the feedback-feedforward controller, the rotor

speed regulation is as good as, if not better than the standard

feedback controller, and that the tower fore-aft DEL can be

reduced as well in Region 3.

Additionally, as mentioned in Section III-E, the NMPC

controller described in [36] was very computationally expensive and not able to run in real time. Since then, ongoing

work has been done to work through the computational

hardware problem by separating the lidar, feedforward data

processing, and turbine controller to each be handled by its

own dedicated computer. This way, the NMPC could run

its own separate process receiving inputs from the lidar and

the turbine controller, and determining feedforward updates

to send to the turbine controller to be applied to the wind

turbine [45].

V. CONCLUSIONS AND FUTURE WORK

In this paper, a review of the use of lidars for enhanced

wind turbine controls has shown that they can offer much improvement to lower the cost of energy of wind turbines with

the potential for reducing wind turbine loads, and increasing

energy production. Each of the three conventional control

actuation methods (pitch, torque, and yaw) was investigated

to show how lidars can be used with each actuator.

Many simulation studies have shown that feedforward

pitch control can reduce the blade root flap bending moment,

as well as the tower base fore-aft bending moment. Additional field tests have been performed to show that improved

rotor speed regulation is achievable with lidar feedforward

pitch control. Feedforward torque control strategies have

also been investigated, and have shown that drivetrain loads

increased drastically with little to no power increase. These

studies showed that feedforward torque control has not

been as promising as feedforward pitch control; however,

ongoing research using a combined feedforward torque/pitch

nonlinear model predictive controller shows promise for

both reducing turbine loads as well as increasing power

production. Yaw control was also discussed, and several

studies found from field test data that power production could

be increased either by using lidars as a means to precisely

calibrate a nacelle-mounted wind vane, or by using a lidar

to directly control the yaw position of the turbine.

Beyond the individual wind turbine, research was also

discussed on using lidars at the wind plant level. Research

at the wind plant level is largely just emerging, and most

control studies have been done in simulation using “openloop” control methods to evaluate the general controllability

of wakes. A first use of lidars for this research will be to

extend the simulation studies of wake control to field test

experiments. With this, a rear-facing nacelle-mounted lidar

will be able to track a wind turbine’s wake and verify the

wind turbine’s capability of controlling the wake. Additionally, as this research advances lidars will become very useful,

if not necessary, to provide detailed real-time measurements

of wakes within a wind farm as well as precisely controlling

the yaw position of each wind turbine. With this, lidars can

be used as additional wind-plant-level sensors for closedloop wind plant control, and further reductions in the LCOE

for wind energy could be achieved.

ACKNOWLEDGMENT

This work was supported by the U.S. Department of

Energy under Contract No. DE-AC36-08GO28308 with the

National Renewable Energy Laboratory. Funding for the

work was provided by the DOE Office of Energy Efficiency

and Renewable Energy, Wind and Water Power Technologies

Office.

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This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. 8