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Contract No. DE-AC36-08GO28308

An Adaptive Data Processing 

Technique for Lidar-Assisted 

Control to Bridge the Gap 

Between Lidar Systems for 

Wind Turbines

Preprint

David Schlipf

University of Stuttgart and National Renewable Energy Laboratory

Paul Fleming and Andrew Scholbrock

National Renewable Energy Laboratory

Steffen Raach, Florian Haizmann, Po Wen Cheng

University of Stuttgart

Raghu Krishnamurthy and Matthieu Boquet

Avent Lidar Technology

Presented at the EWEA 2015 Annual Event

Paris, France

November 17–20, 2015

Conference Paper

NREL/CP-5000-65273

December 2015
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An Adaptive Data Processing Technique for
 

Lidar-Assisted Control to Bridge the Gap between Lidar
 

Systems and Wind Turbines
 

David Schlipf1,2, Paul Fleming2, Steffen Raach1, Andrew Scholbrock2, Florian Haizmann1, Raghu Krishnamurthy3, Matthieu
 

Boquet3, and Po Wen Cheng1
 

1Stuttgart Wind Energy (SWE) @ Institute of Aircraft Design, University of Stuttgart, Germany.
 2National Renewable Energy Laboratory (NREL), Boulder, Colorado, USA.
 3Avent Lidar Technology, Orsay, France.
 

Abstract

This paper presents first steps toward an adaptive lidar data processing technique crucial for lidarassisted control in wind turbines. The prediction time

and the quality of the wind preview from lidar measurements depend on several factors and are not constant. If the data processing is not continually adjusted, the benefit of lidar-assisted control cannot be

fully exploited or can even result in harmful control action. An online analysis of the lidar and turbine data is

necessary to continually reassess the prediction time

and lidar data quality.

In this work, a structured process to develop an

analysis tool for the prediction time and a new hardware setup for lidar-assisted control are presented.

The tool consists of an online estimation of the rotor

effective wind speed from lidar and turbine data and

the implementation of an online cross-correlation to

determine the time shift between both signals. Further, we present initial results from an ongoing campaign in which this system was employed for providing

lidar preview for feedforward pitch control.

1 Introduction

For wind turbines, wind is the energy source as well

as the main disturbance to the wind turbine control

system. The control system has to balance competing control objectives: increasing the energy yield

while reducing the structural loads. However, traditional feedback controllers are only able to react to

the disturbance of the inflowing wind field after it has

already impacted the turbine. With the recent development of lidar technology, the information about incoming disturbances can be made available ahead of

time and used for feedforward control. A comprehensive overview of lidar-assisted control can be found in

[1].

In an initial field testing on the two- and three-bladed

Controls Advanced Research Turbines (CART2 and

CART3 at the National Wind Technology Center in

Boulder, Colorado), a collective pitch feedforward controller using lidar wind disturbance preview was able to

reduce the rotor speed variation [2, 3]. However, this

reduction cannot be directly converted into a reduction of the levelized cost of energy (LCOE). Thus, one

of the long-term research challenges identified by the

European Academy of Wind Energy is the transformation from scientific proof-of-concept to studies that

provide a measurable benefit of lidar-assisted control

[4]. A first study shows an LCOE reduction of 6.5% for

large offshore wind turbines [5].

Lidars are only able to measure the wind speed

along the line-of-sight (LOS) of the laser beam. Multiple LOS measurements can be put together to form

a general wind field, with a longitudinal wind speed,

as well as horizontal and vertical shear. Additionally,

these wind speed measurements are taken upstream

of the wind turbine, and as the wind travels toward the

wind turbine, it will change due to the turbulence in the

atmosphere. A coherence measurement between the

lidar wind measurement and the rotor effective wind

speed measured by the wind turbine helps to quantify

the turbulent wind evolution. Higher and higher coherence values will lead to further and further improvements in the controller’s ability to use the lidar preview information for feedforward control. An example

of this is in [6], where simulation studies showed that

improving the coherence will lead to improvements in

feedforward control for load reductions.

Having a high coherence between the lidar measured wind speed and the rotor effective wind speed

is quite challenging, as the coherence has to take into

account the lidar measurement techniques as well as

the turbine dynamics. From an industrial standpoint,

lidars and wind turbines come from different manufacturers and have their own individual data acquisition

systems. Additionally, due to the multi- and interdisciplinary character of the problem, there is a gap in

knowledge: on the one hand, a thorough understanding of lidar measurement principles and limitations is

mandatory for providing usable signals to the con­
trol system. On the other hand, detailed knowledge

about wind turbine dynamics and controls are necessary to determine which signals can be used for preview control. These challenges make it hard for lidars

and wind turbines to relate to one another in order v0 [m/s]

15

xT

FB+FF 

FB 

10 to effectively enhance the turbine control system with 20 lidar wind preview. Instead, a centralized system—

developed by a joint project between industry and

research institutions—which has access to real-time

data from both the wind turbine and the lidar, would

[m] 

Ω [rpm] 

Mg [kNm] θc [deg]

10

0

be better suited to close the gap between lidars and

wind turbines. 2

A consortium of NREL, SWE, and the lidar manufacturer Avent Lidar Technology started to test advanced lidar-assisted control on the CART2 in January 2015. The same lidar-turbine combination has

been used in an previous campaign [7]. A new adaptive data processing technique independent from lidar

and turbine control software and hardware was developed during this campaign. The improved setup and

the combination of lidar- and turbine-specific knowl1.5

1

38

36

edge enables a comparison of the rotor-effective wind 34

estimates from turbine and lidar data. With a cross- 0.05

correlation calculated in real time, the lidar estimate

can be aligned with the turbine’s reaction via a graph­ 0

ical user interface (GUI). The feedforward control action can be applied to the turbine with the desired preview time, which improves the overall control perfor­ −0.05

30 35 40 45 50

mance.

This system was then used to provide a feedforward

pitch update to the feedback controller, and a campaign to assess the improvement in performance from

the baseline controller was performed. Initial results

from this campaign are provided to show the value of

the approach.

2 Approach

As discussed in the introduction, this paper presents

a system for producing an accurate wind preview that

can be used for maximally effective feedforward control of wind turbines.

In this section, we present the approach taken for

designing this complete system, from the design of

the feedforward controller that will apply the lidar signal, the development of the data processing that produces the signal, and the stages of refinement and

implementation that would be expected in an industrial application.

2.1 Structured Code Development for

Lidar-Assisted Control

The code development for lidar-assisted control is

structured in five stages: feedforward controller development, data processing development, real-time environment development, hybrid simulations, and field

testing.

1.	 Feedforward Controller Development: Assuming

perfect wind preview, the feedforward controller

time [s] 

Figure 1: Reaction to an EOG at 12 m/s: Feedback only

(dark blue) and with additional feedforward (light

blue).

is first designed and tested using the Simplified

Low Order Wind turbine (SLOW) model [8] with

only 2 degrees-of-freedom (rotor and tower motion). In this case, the simulation model is identical to the controller design model and the control performance should be as desired. Then, the

same wind is used in simulations with an aeroelastic model (FAST [9]) to test the robustness of

the controller against model uncertainties. Figure

Figure 1 shows simulations with the FAST model

for an extreme operating gust (EOG). The feedforward controller is able to reduce the impact of

wind speed changes to the rotor speed following

its design objective [10]. Figure 2 (left) shows a

diagram of the SLOW model.

2.	 Data Processing Development: In the previous

stage, the feedforward controller was designed to

perform well assuming perfect wind preview. In

this stage, we develop the data processing that

will be used given realistic lidar measurement of

the wind. Using the FAST model, we now simulate the turbine operating in a turbulent wind

field, rather than a uniform flow, which can be

easily represented by a single velocity. A lidar

simulator [11] is used to scan the incoming wind

field. The data is condensed to an estimate of
NWTC 

TwinCAT on Gateway 

CART 

DP FF

Labview on CART-SCADA 

FB 

Lidar 

Simulink 

FAST/ 

SLOW FB 

FF 

଴ݒ

Simulink 

FB FAST 

FF 

RLD 

ோ଴ݒ

DP 

Figure 2: Code development. Stage 1 (left): Simulation within Simulink with perfect wind preview; the rotor-effective wind v0

disturbs the turbine and the feedforward controller (FF) is designed to assist the feedback controller (FB). Stage

4 (center): Hybrid Simulations within Simulink; the rotor-effective wind speed from field test turbine data v0R and

simultaneously measured field test raw lidar data (RLD) are used to adjust the data processing (DP). Stage 5

(right): Field Testing; the DP and FF are compiled for TwinCAT on the Gateway and the FB for Labview on the

CART-SCADA.

the rotor-effective wind speed, filtered, and transferred to the feedforward controller. The data processing can be evaluated by comparing the correlation between the lidar estimate and the real

rotor-effective wind speed to a correlation model

[12, 13]. Simulations are done over the full operation range to test the robustness of the controller

against measurement uncertainties.

3.	 Real-Time Environment Development: The data

processing system and the feedforward controller

are compiled to be used within a real-time capable frame (TwinCAT) on a separate computer (referred in this work as “Gateway”). The same simulations from Stage 2 are done and thus allow a

direct verification of the real-time environment.

4.	 Hybrid Simulations: Effects such as the wind evolution can be included [14] in simulations, but effects such as measurement errors and changing

lidar data quality are difficult to simulate. Thus,

the approach of the Hybrid Simulations [15] is

used to adjust the lidar data processing and feedforward controller: The rotor-effective wind speed

is extracted from real turbine data [16] and together with simultaneously measured lidar data

used for simulations, as shown in Figure 2 (center).

5.	 Field Testing: Finally, following the above iterations, the Gateway is connected to the actual lidar and turbine controller, as shown in Figure 2

(right).

The approach has several advantages:

•	 The feedforward controller, the data processing,

and the real-time environment are developed independently. Thus, the data processing can be

combined with different feedforward controllers.

•	 Each stage has a defined goal. This helps to develop several controllers in parallel.

Figure 3: The Avent 5-Beam installed on the nacelle of the

CART2 at the NWTC. (Photo Credit: Lee Jay Fingersh, NREL 33621)

•	 The code is developed in the control-engineerfriendly Simulink environment and is organized in

one single library. Thus, adjustments can be directly transferred to other stages.

2.2 Hardware Setup for Lidar-Assisted

Control

The CART2, located at the National Wind Technology Center (NWTC), is a 600-kW turbine heavily instrumented with sensors. A control system (CARTSCADA) was developed and implemented in Labview

by NWTC engineers running at 400 Hz, containing a

dynamic link library (DLL) compiled from the Simulinkbased feedback controller.

The Avent 5-Beam pulsed system was installed on

the nacelle of the CART2 (see Figure 3) and measures at 10 distances in front of the rotor. At each distance, five line-of-sight measurements are taken sequentially within 1.25 seconds and are transferred to

the CART-SCADA via an Ethernet connection in real

time.
CART2 

Lidar 

v0 [m/s] 

4

6

8

10

12

14

0 200 400 600 800 1, 000 1, 200 1, 400

time [s] 

Figure 4: Rotor effective wind speed: from CART2 data (light blue) and lidar data (dark blue).

The data processing and feedforward controller are

model 

data 

0

0.2

0.4

0.6

0.8

1

10−3 10−2 10−1 100

realized on the Gateway, which is a deterministic, realtime capable industrial PC and is connected to the

CART-SCADA via an Ethernet connection. The lidar data is condensed into an estimate of the lidarmeasured rotor-effective wind speed. Additionally, the γ2 [-] RL

Gateway receives turbine data, including rotor speed,

blade pitch angle, and rotor shaft torque, to obtain

the turbine-measured rotor-effective wind speed. The

Gateway provides its feedforward update signals to

the CART-SCADA, and the CART-SCADA can independently choose whether or not to use the signals in

order to provide robust operation.

A separate computer connected to the Gateway visualizes the processed data and offers a way to directly interact with the Gateway via a GUI. Further,

the feedforward control action (blade pitch, generator

torque, desired rotor speed) are compared to measured data. Additionally, the software provides the

possibility of adjusting parameters used for the online

cross-correlation that will be described in the next section.

3 Results

3.1 Correlation Study

Similar to previous work, the rotor effective wind

speed estimated from the raw lidar data and from the

turbine reaction has been compared before the feedforward controller was applied. Figure 4 compares

both signals in the time domain. Larger trends, such

as the gust at the end of the period, are very well detected by the lidar.

This is confirmed by Figure 5, which compares both

signals in the frequency domain: for small wavenumbers (large turbulent eddies) the coherence is close

to one (1 means perfect correlation), and for larger

wavenumbers (smaller turbulent eddies) the coherence γ2

RL is going toward zero (0 means no correlation). The correlation is verified by the analytical

model [12]. The longitudinal decay parameter for the

wind evolution was set to 0.2 based on the detected

value from [17].

The detected correlation is used to design an adapk [rad/m] 

Figure 5: Coherence between the lidar and turbine estimate

of the rotor-effective wind speed: From data of

Figure 4 (dark blue) and from analytic correlation

model (light blue).

Figure 6: Cross-Correlation between the lidar and turbine

estimate of the rotor-effective wind speed over the

last 10 s: Newest (dark blue) and oldest (light blue)

data.

tive filter, which adjusts the cut-off-frequency depending on the mean wind speed. In future work, the adaptation needs to be extended to detect changes in the

correlation and adjust the filter accordingly.
3.2 Online Calculation of

Cross-Correlation

The feedforward control inputs are calculated based

on the lidar estimate of the rotor-effective wind speed

and sent to the CART-SCADA with an adjustable preview time before the wind disturbance reaches the

turbine. This timing is crucial and the lidar estimate

needs to be aligned with the rotor-effective wind speed

from the turbine data. The preview time of the lidar

estimate is based on Taylor’s Frozen Turbulence Hypothesis and calculated by dividing the measurement

distance by the mean wind speed. Changes in the

preview can be due to the changing impact of the induction zone or inaccuracies in Taylor’s hypothesis or

the measurement distance.

On the Gateway, the timing is evaluated online by

calculating the cross-correlation between the rotoreffective wind speed from lidar and turbine data. The

normalized cross-correlation gives a measure of the

similarity of the estimation and the timing of the estimation. An example of the online cross-correlation

over the last 10 seconds is given in Figure 6. The timing can be adjusted manually by shifting the lidar preview via the GUI, and the changes can be observed

in real time. During the ongoing field testing, an offset

of 1 second was identified and corrected.

3.3 Initial Results of Field Testing

Finally, a field-campaign was conducted in which the

baseline feedback pitch controller was augmented by

the lidar-preview feedforward pitch update. Because

the lidar preview measurement was shown to have

good coherence to turbine measurements and was

robust over time, the feedback controller could be detuned to maximize the benefit of using lidar feedforward. Detuning the feedback controller allows the

feedforward controller to handle the lower wind disturbance frequencies, up to the coherence limit, which

should be the optimal combination.

The field test is set up so that the controller cycles

between 5 minutes of running the normal baseline

feedback controller and 5 minutes of combined a feedforward and detuned feedback controller as described

above. By cycling in this way, the two controllers are

tested in wind conditions that are as similar as possible.

Currently, field tests have been run intermittently

over several months, across a range of seasons and

atmospheric conditions. While still somewhat initial,

the data is already demonstrating promising trends.

To analyze the data, we process each 5-minute data

file as follows. The first 30 seconds of each file are

ignored, to allow the change in performance of transitioning from one controller to another to be established. The remaining time is divided in 45-second

continuous chunks and processed. For each chunk,

statistics such as mean and standard deviation are

computed for all signals, and for signals related to

fatigue, a damage equivalent load (DEL) is likewise

computed.

We first consider the speed-regulation performance

of the lidar-enhanced controller compared to the baseline. The collective pitch controller regulates the rotor

speed to the rated setpoint. The first question to answer is how has our modification affected this performance.

Figure 7 compares the performance of speed regulation. Note that for the plots, the statistics computed from the 45-second chunks have been binned

by wind speed, and for each wind speed and controller

the mean value and standard error of the mean are

computed. First, in Figure 7 (left), the standard deviation of the rotor speed is compared across the collected 45-second chunks of data. From this plot, it appears that the speed regulation performance has not

been impacted, which is the desired result. Had lidar

feedforward been ineffective, detuning the feedback

controller would have significantly worsened and rotor

speed variation would have increased. Figure 7 (right)

plots the frequency of occurrence of each per-chunk

maximum rotor speed. While the highest observed

rotor speeds did occur with the lidar-enhanced controller, there is not much noticeable change in performance. Finally, Figure 8 (left) shows the pitch

rate standard deviation, which indicates the amount

of pitch activity. Here, it is clear the lidar-enhanced

controller is achieving similar results in speed-control

with significantly less pitch actuation when compared

to the feedback-only controller. Because the feedback

controller can only react after a wind event, it would

normally need to pitch more aggressively than a controller that previews the upcoming wind event and can

begin acting ahead of time.

Additionally, the standard deviation of the tower acceleration in Figure 8 (right) is reduced. We can now

compare the controllers in terms of the fatigue loads

by plotting the per-chunk DEL statistics. Because collective pitch is most tightly coupled to fatigue loads

related to rotor thrust, we focus on those—specifically

blade flap bending moment and tower fore-aft bending

moment.

The comparison of flap bending is shown in Figure 9 (left). Although additional data collection in

higher winds would greatly aid in drawing conclusions,

a reduction in this load is evident in wind speeds

above rated. Fore-aft tower bending, shown in Figure 9 (right), is significantly reduced by the experimental controller.

4 Conclusion and Outlook

In this work a solution is presented that allows the

data processing and feedforward control to be independently calculated of the lidar system and the turbine controller. This setup allows robust operation of

the wind turbine and intensive calculations on time

scales different from the feedback control loop.

Further, the setup provides the possibility to determine not only the rotor-effective wind speed estimate
1.2 60

1 50

0.8 40

FB+FF 

FB 

100

75

50

25

8 9 10 11 12 13 14

FB+FF 

FB 

36 36.2 36.4 36.6 36.8 37 37.2 37.4 37.6 37.8 38

rotor speed STD [rpm] 

0.2 10

0 0

mean wind speed [m/s] maximum rotor speed [rpm] 

Figure 7: Comparing controller effects on rotor speed regulation. Left: standard deviation of rotor speed. The points are

the mean value for each bin, while the error bars indicate the standard error of the mean. Right: frequency of

occurrence of each per-chunk maximum rotor speed.

2 0.32 number of chunks [-] 

0.6 30

0.4 20

FB+FF 

FB 

100

75

50

25

tower acceleration STD [m/s2] 

8 9 10 11 12 13 14

FB+FF 

FB 

100

75

50

25

8 9 10 11 12 13

1.5 0.3

0.28

0.26

pitch rate STD [deg/s] 

1

0.5

0 0.24

mean wind speed [m/s] mean wind speed [m/s] 

Figure 8: Comparing controller on standard deviation of pitch rate (left) and tower acceleration (right). The points are the

mean value for each bin, while the error bars indicate the standard error of the mean.

180 900

FB+FF 

FB 

100

75

50

25

tower fore-aft DEL [kNm] 

8 9 10 11 12 13 14

FB+FF 

FB 

100

75

50

25

8 9 10 11 12 13

160 800

blade flap-wise DEL [kNm] 

140

120

700

600

100 500

80 400

mean wind speed [m/s] mean wind speed [m/s] 

Figure 9: Comparing controller effects on loads. Blade flap-wise bending (left) and tower fore-aft bending (right). The points

are the mean value for each bin, while the error bars indicate the standard error of the mean.

14

14
from the lidar data that is used for lidar-assisted control but also of the rotor-effective wind speed from the

turbine data. Using both signals, an online crosscorrelation is computed and visualized allowing an

adjustment of the timing of the lidar-assisted control. This improves the performance of the feedforward

controller.

Results of field testing a feedforward controller designed using the above approach indicate success in

improving the performance over the baseline feedback

controller, in terms of both reduced actuation usage

and reduced fatigue loads.

In future work, the setup will be extended by an automated adjustment of the timing and filtering once

the method has been proven to be robust. The Gateway will be used for advanced feedforward controllers

such as the flatness-based approach [18] and Nonlinear Model Predictive Control [19, 20].

5 Acknowledgments

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

Energy under Contract No. DE-AC36-08GO28308

with the National Renewable Energy Laboratory.

Funding provided by the DOE Office of Energy Efficiency and Renewable Energy, Wind and Water

Power Technologies Office.

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