NREL is a national laboratory of the U.S. Department of Energy, Office of Energy 

Efficiency & Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.

Contract No. DE-AC36-08GO28308

Refinements and Tests of an 

Advanced Controller to Mitigate 

Fatigue Loads in the Controls 

Advanced Research Turbine

Preprint 

A.D. Wright and P. Fleming

National Renewable Energy Laboratory

J.W. van Wingerden

Delft University of Technology

To be presented at 49th AIAA Aerospace Sciences Meeting, 2011

Orlando, Florida

January 4-7, 2011

Conference Paper

NREL/CP-5000-50006

December 2010
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1

Refinements and Tests of an Advanced Controller to 

Mitigate Fatigue Loads in the Controls Advanced 

Research Turbine

Alan D. Wright†

Paul Fleming‡

National Wind Technology Center

National Renewable Energy Laboratory€

Golden, Colorado

Jan-Willem van Wingerden€€

Delft University of Technology

Delft Center for Systems and Control

2628 CD, Delft, The Netherlands

Abstract

Wind turbines are complex, nonlinear, dynamic systems forced by aerodynamic, gravitational, centrifugal, and 

gyroscopic loads. The aerodynamics of wind turbines are nonlinear, unsteady, and complex. Turbine rotors are 

subjected to a complicated 3-D turbulent wind inflow field, with imbedded coherent vortices that drive fatigue loads 

and reduce lifetime. Design of control algorithms for wind turbines must account for multiple control objectives. 

Future large multi-megawatt turbines must be designed with lighter weight structures, using active controls to 

mitigate fatigue loads, while maximizing energy capture. Active damping should be added to these dynamic 

structures to maintain stability for operation in a complex environment.

At the National Renewable Energy Laboratory (NREL), we have designed, implemented, and tested advanced 

controls to maximize energy extraction and reduce structural dynamic loads. These control designs are based on 

linear models of the turbine that are generated by specialized modeling software. In this paper, we present field test 

results of an advanced control algorithm to mitigate blade, tower, and drivetrain loads in Region 3. The advanced 

state-space controller uses independent blade pitch to mitigate the effects of shear across the rotor disk, and a 

collective pitch component to add active damping to the tower’s first fore-aft bending mode, and, to regulate turbine 

speed. In addition, a separate generator torque control loop adds active damping to the tower’s first side-side mode 

and the first drivetrain-torsion mode. 

In this paper we show a refinement to the generator torque control loop to account for actuator delay. We 

discovered a delay in actuation between commanded generator torque and the torque actually applied to the highspeed shaft. If this delay is not properly accounted for in the plant model used for control design, the generator 

torque control loop tends to destabilize the first drivetrain torsion mode. We show modifications to the torque 

control loop to account for this delay, and, to prevent unnecessary control actuation at certain harmonics in the rotorspeed. We present field tests of this controller and make comparisons with a simple PID baseline controller for the 

2-bladed Controls Advanced Research Turbine (CART2) located at NREL’s National Wind Technology Center

† Senior Engineer II, National Wind Technology Center, 1617 Cole Blvd., Mailstop 3811, AIAA member

‡ Engineer III, National Wind Technology Center, 1617 Cole Blvd., Mailstop 3811, AIAA member € Employees of the Alliance for Sustainable Energy, LLC, under Contract No. DE-AC36-08GO28308 with the U.S. Dept. of 

Energy have authored this work. The United States Government retains, and the publisher, by accepting the article for 

publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, worldwide license to 

publish or reproduce the published form of this work, or allow others to do so, for United States Government purposes. €€ Assistant Professor, Delft Center for Systems and Control, 2628 CD, Delft, AIAA nonmember
2

Introduction

Control can improve the performance of wind turbines by enhancing energy capture and reducing dynamic loads. 

At the National Renewable Energy Laboratory (NREL), we are implementing and testing state-space controls on the 

2-bladed Controls Advanced Research Turbine (CART2), a turbine specifically configured to test advanced controls. 

Most large commercial wind turbines are variable-speed machines. In Region 2, the generator torque is controlled 

to extract maximum power from the wind, while blade pitch is held constant. In Region 3, generator torque is held 

constant, and blade pitch is used to regulate the speed of rotation of the turbine. Machines of the past used singleinput/single-output (SISO) controllers to meet these control objectives. These simple controllers, designed using 

Proportional Integral Derivative (PID) control, were proven effective for the rigid machines of the past, since the

structural modes were well separated in frequency from the frequency band needed for the primary control 

objectives. 

Today’s trend is to design larger wind turbines with lighter weight components to reduce cost. These larger, more 

flexible designs may reduce the natural frequencies of important flexible modes. Now the frequencies of these

structural modes might lie within the frequency band of the primary speed regulation loop. Designing the controls to 

properly account for these structural modes is critical. In addition, the number of objectives that the controller must 

meet increases to include: 1) maximizing power, 2) regulating turbine speed, 3) enhancing damping and increasing 

stability, and 4) reducing loads. These multi-objective controllers could be designed using the SISO classical control 

design methods, but this necessitates multiple control loops. The danger in this approach is that if not carefully 

designed, these loops can couple with each other, leading to destabilization of the turbine. Advanced multi-variable 

control design methods, in which all objectives are met in a very limited number of control loops, may be a better 

long term solution, especially as machines become lighter, larger, and more flexible.

A promising load reduction control strategy uses independent blade pitch to mitigate the effect of asymmetric 

wind variations across the rotor disk.1 Recently, an independent blade pitch controller (IPC) has been designed and 

field-tested in the NREL CART2 machine.

2 This controller uses a classical control approach to design PID SISO 

control loops to reduce the once-per-revolution (1P) rotor loads that occur due to asymmetric shear across the disk. 

They also regulate speed in Region 3 and add active damping to the tower’s first fore-aft mode.1 Impressive load 

alleviation results were demonstrated with this conventional IPC control approach. In other work, a feedback/feedforward individual pitch control method was described and tested through simulation.

3 This method used an optimal 

multi-variable Linear Quadratic Gaussian (LQG) controller and a feed-forward disturbance rejection controller 

based on estimated wind speed in Region 3. This IPC approach is extended from the usual alleviation of just 1P 

loads, to higher harmonics – 2P and 3P load reduction. Simulation results demonstrated very good load reduction 

compared to the conventional IPC method in simulation studies. In another approach, two separate multivariable 

control loops were designed, one for the speed control and active damping of the tower fore-aft mode using 

collective pitch, and the other for reduction of yaw and tilt rotor moments acting on the hub.4 These two controllers 

were based on robust control design methods by minimizing an H∞ norm of a weighted cost function.

In a previous paper, we showed preliminary test results from state-space controllers that perform speed regulation, 

load mitigation, and active damping using just two control loops.

5 A single blade pitch control loop performs Region 

3 speed regulation, active damping of the tower first fore-aft (f-a) mode using collective pitch, and mitigation of 

shear across the rotor disk with IPC. The independent blade pitch control loop is designed using Disturbance 

Accommodating Control (DAC), including disturbance models, to account for the uniform and asymmetric wind 

disturbances across the rotor disk.

6 A separate generator torque control loop adds active damping to the tower first 

side-side (s-s) mode and first drivetrain torsion mode. The generator torque control loop adds perturbations onto the 

normally constant Region 3 generator torque to perform this active damping. 
3

This paper presents a refinement of the generator torque control loop, as well as, further field-tests of this twoloop control architecture. We first discuss the turbine configuration and test set-up. We then review the pitch and 

generator torque control designs. We describe refinements made to the generator torque control loop to improve its 

performance in the presence of actuator delay, and to reduce control actuation at per revolution harmonics of the 

rotor-speed, where control is not needed. We then show field test results on the CART2 (Fig. 1) at the National 

Wind Technology Center (NWTC). We compare performance of the multi-variable state-space controller to a 

baseline controller based on a form of PID design.

Now, we describe the turbine for field implementation and testing. 

CART2 Configuration

The CART2 (Fig. 1) is a two-bladed, teetered, upwind, 

active-yaw wind turbine. This machine is used as a test bed to 

study aspects of wind turbine control technology for mediumto large-scale machines.7

The CART2 is variable speed, and each blade is capable 

of being independently pitched with its own 

electromechanical servo. The pitch system can pitch the 

blades up to 18 degrees per second (deg/s) with pitch 

accelerations up to 150 degrees per second/second (deg/s^2). 

The squirrel cage induction generator, with full power 

electronics, can control torque from minus rating (motoring) 

to plus rating (generating) at any speed. The torque control 

loop has a very high rated bandwidth of 500 radians per 

second (r/s).

Rated electrical power (600 kilowatts at a low-speed shaft 

[LSS] speed of 41.7 revolutions per minute [rpm]) is 

maintained in Region 3 using a conventional variable-speed 

approach. Power electronics are used to command constant torque from the generator, and the blade pitch regulates 

the rotor speed. 

The machine is equipped with a full complement of instruments that gather meteorological data at four heights. 

Blade-root flap and edge-strain gages, tower-bending gages, and low-speed shaft (LSS) and high-speed shaft (HSS) 

torque transducers gather load data. Accelerometers in the nacelle measure the tower’s fore-aft (f-a) and side-side (ss) motion. Absolute position encoders gather data on pitch, yaw, teeter, LSS, and HSS positions. These data are 

sampled at 100 Hz. The custom-built control system collects the data and controls the turbine at a control-loop cycle 

rate of 100 Hz. This system is personal-computer-based and very flexible. 

Next, we will review the design of the state-space controller that we field-tested.

Review of Original Control Designs

The control designs tested in this paper are based on linear time invariant (LTI) state-space control design 

methods. A linear time-invariant state-space model for the CART2 dynamics used for control design can be 

described as:

(1)

where u is the vector of control inputs; perturbed pitch rate of each blade or generator torque to be described 

shortly. The vector ud is the disturbance input. x and y are the state vector and measurement vector respectively, 

d d

d d

x Ax Bu B u

y Cx Du D u

=++

=++



Figure 1. The two-bladed controls 

advanced research turbine (CART2).
4

described below. The matrix A is the system matrix, B is the control input transmission matrix, Bd is the 

disturbance input transmission matrix, C relates the measurements to the states, D relates the measurements to the 

control input, and Dd relates the measurements to the disturbance inputs. The system matrices A , B , Bd ,C , D , 

and Dd are found by linearizing an aeroelastic model about an operating point in Region 3 using the FatigueAerodynamics-Structures-Turbulence (FAST) code.

8

This control is performed in two separate control loops, as shown in Figure 2. The independent blade pitch 

control loop regulates turbine speed, mitigates asymmetric wind disturbances across the rotor disk, and actively 

damps the tower’s first fore-aft mode. The separate generator torque control loop adds active damping to the tower’s 

first side-side mode, as well as, the drivetrain’s first torsion mode. 

Figure 2. State-space controller architecture.

Review of Independent blade pitch control loop:

We based our independent pitch control design on Disturbance Accommodating Control (DAC).6 The basic idea 

of DAC is the augmentation of the usual state-estimator-based controller to recreate disturbance states via an 

“assumed-waveform” model. These disturbance states are used as part of the feedback control to reduce 

(“accommodate”) or counteract any persistent disturbance effects.6 In Eq. (1), we assume a disturbance generator in 

the form of an additional state-space model for the wind disturbances: 

, (2)

where d z is the disturbance state vector, F is the disturbance generator state matrix, and Θ relates the 

disturbance ( ) u t d to the disturbance states.6

The objectives of the Region 3 independent blade pitch control algorithm are to regulate speed to 41.7 

revolutions per minute (rpm), and to add active damping to the tower’s first fore-aft bending mode, as well as to 

mitigate the effects of asymmetric wind disturbances across the rotor disk. As described above, the CART2 is a twobladed teetering hub machine. We will assume that the teeter degree of freedom (DOF) is locked in the design of 

this independent blade pitch controller, since the states corresponding to the teeter DOF are not included in the 

control design.

We include two wind disturbances in the vector u d in Eq. (1): a disturbance representing the linear part of the 

wind shear, and a disturbance uniform over the rotor disk.7 The linear shear disturbance terms will induce

independent blade pitch commands that will mitigate the effects of shear variations across the rotor disk. The 

uniform disturbance will induce rotor collective pitch commands, which act to regulate speed. A collective pitch

CART

Conventional 

Variable-Speed 

Controller

State-Space 

Torque Controller

State-Space 

Pitch Controller

Nominal Generator

Torque

Total Gen.

Torque

Ind. Blade Pitch Rates

Added Gen.

Torque

y

y

y

y

 

 

() ()

() ()

d d

d d

z t Fz t

ut zt

=

= Θ


5

component also actively damps the tower first fore-aft mode. Both the independent blade pitch and collective pitch 

commands are contained in the blade independent pitch loop shown in Fig. 2.

1

2

( )

( )

d

d

d

u t

u

u t

  = =    

linear shear disturbance

uniform wind disturbance

 

   

. (3)

The vector of plant states x for this control model can be described by:

st

st

perturbed tower 1 f-a bending deflection

perturbed blade 1 flapwise deflection

perturbed blade 2 flapwise deflection

perturbed tower 1 f-a bending velocity

perturbed generator speed

x = perturbed blade 1 flapwise velocity

perturbed blade 2 flapwise velocity

blade 1 perturbed pitch angle

blade 1 perturbed pitch rate

blade 2 perturbed pitch angle

blade 2 perturbed pitch rate

 

 







 









(4)

In this plant model, extra states are included to model the effects of actuator dynamics. These states are appended 

to the normal plant model states, as seen in Eq. (4). The control input u consists of the perturbed pitch rate of each 

blade (generator torque will be the control input in a separate control loop described in the next section):

perturbed blade 1 pitch rate

perturbed blade 2 pitch rate

u

 

   

= (5)

We select the following five CART2 measurements:

perturbed generator speed

perturbed blade-1 pitch angle

perturbed blade-2 pitch angle

perturbed tower-top fore-aft acceleration

blade-1 root flapwise bending moment - blade-2 root flapwise bending mo 1 (

2

y =

ment)

 

 

 

(6)

Based on these turbine measurements, state estimation is used to estimate states needed in the full state feedback 

control. 7 These measurements provide the number necessary to provide an observable system so that state 

estimation can be performed. 7

The controller was designed from a linear state-space model described above for the Region 3 operating point,

defined by a rotor speed of 41.7 rpm, hub-height wind speed of 18.0 meters per second (m/s), and a pitch angle of 11 

degrees (deg.). Reference (2) describes selection of the gains corresponding to the plant and disturbance states as 

well as state estimator gains.

Review of Torque control loop:

Normally, generator torque in Region 3 is held constant. In this generator torque control, perturbations in 

generator torque are used to add active damping to the tower first side-side (s-s) mode, as well as the first drivetrain 

torsion mode. We applied the state-space controls as perturbations of torque, in addition to the conventional Region 

3 constant torque. This controller is designed separately from the rotor collective pitch controller using a separate 

linear model of the CART2.
6

The state-space model of the CART2 for design of the generator torque control is described by Eq. (1), without 

the disturbance ud . For the generator torque controller, we do not need to mitigate shear or uniform wind 

disturbances as with the pitch controller. 

A linear model based on the following states was used to design this controller:

perturbed tower first s-s bending deflection

perturbed drivetrain first torsional deflection

perturbed tower first s-s bending velocity

perturbed generator speed

perturbed drivetrain first torsional vel

 

 

x =

ocity

tower-top acceleration filter state 1

tower-top acceleration filter state 2

 

 

 

. (7)

The inputs (measurements) to the state estimator controller are: 

perturbed generator speed

perturbed tower-top s-s acceleration y

 

   

= (8)

We added filter states to the state-space model to include a second order filter for removing high frequency noise 

from the tower accelerometer data. 1 We again used LQR to design this controller. 

Now u consists of the perturbed generator torque as the single control input for this control loop. The system

matrices (A, B, C, and D) are found by linearizing a FAST aeroelastic model at the same operating point selected for 

the blade pitch control design described above. The LQR state weightings were chosen to add active damping to the 

tower first side-side and drivetrain first torsion model.

10

Initial Tests:

We implemented and began initial field tests of this two-loop, state-space controller in the winter of 2009. In the 

spring of 2009, the CART2 experienced a gearbox failure (caused by a gear-tooth alignment problem). The gearbox 

was replaced in the summer of 2009 and controls testing resumed later in the fall. Many 10-minute datasets were 

collected, with both the state-space controller and baseline controller, before gearbox failure and after gearbox 

replacement. 

Testing of both controllers was performed, with the CART2 teeter degree of freedom locked, by maintaining 

high teeter brake pressure throughout the tests. In addition, to compare turbine loads and response between the 

controllers in Region 3, we extracted segments of data from each data-set that met certain criteria for Region 3 

operating conditions, such as a rotor-speed above a certain limit, blade pitch angles between a certain range, etc. 

After these sub-sets of data were extracted, we concatenated these data together to form one long continuous dataset 

representing Region 3 operation for each controller. We then used MCrunch11 to calculate statistics of the data and 

fatigue damage equivalent loads (DELs)12 for the tower, drivetrain, and blades.
7

Perhaps the best illustration of these initial field results can be seen by examining the component fatigue DELs, 

shown in Figure 3. Here, all results have been normalized so that the fatigue DELs for the PID baseline controller 

are equal to one (shown in dark blue). The green bars show the data for the state-space controller tested before 

gearbox failure and replacement. The red bars show the results for the same state-space controller tested after 

gearbox replacement. The most striking result is the state-space control results after gear replacement, which show a 

large increase in Low-Speed Shaft (LSS) Torque DEL (200%). Indeed, even before gearbox failure and 

replacement, the state-space controller was not reducing the DEL loads in the low-speed shaft as designed, as 

indicated by the green bars. The state-space controller tended to reduce loads in the other components, although this 

performance seemed to degrade after the gearbox replacement. 

A plot of the time series for Low-Speed Shaft (LSS) Torque indicated a dramatic load increase from the statespace controller after the gearbox replacement, as seen in Figure 4. Data was collected early in 2009 until the 

gearbox failure, which corresponded to the time series up to 700 seconds; data collected after gearbox replacement 

is shown after 700 sec. Figure 4 also shows a shorter section of data that compares data collected from the statespace and baseline controller after gearbox replacement. The state-space controller clearly excites the drivetrain at 

the first torsion mode, and does not meet the intended goal to add damping in this mode. This reflects a problem 

with the generator torque control design. Indeed, the generator torque controller tended to excite this mode to a 

lesser extent even before gearbox failure, but this problem was not discovered until after the gearbox replacement 

because of the dramatic increase in loads. A slight increase in the natural frequency of the actual drivetrain caused 

by the new gearbox may have accentuated this problem, causing the large increase in loads from the state-space 

controller after 700 sec.

We calculated the Power Spectral Density (PSD) of the LSS torque from each controller as seen in Figure 5.

These results show peaks at 3.4 and 3.6 Hz, which correspond to the first drivetrain torsion mode in closed-loop. 

The state-space controller not only increases the magnitude of this peak, but also increases its frequency, further 

indicating that the state-space controller is destabilizing this mode.

Figure 3. PID and state-space controller field-test results 

component DELs.

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

Low-speed Shaft 

Torque DEL (Nm)

Tower s-s bending 

DEL (kNm)

Tower f-a bending 

DEL (kNm)

Blade flap-bending 

DEL (kNm)

Normalized Value

Load Category

PID

State-space before GB replacement

State-space after GB replacement
8

We investigated the cause for this loss in control performance. Upon examining CART2 test data, we discovered 

that a small time delay occurs between the time that generator torque is commanded by the controller and the time 

that this torque is actually applied to the system. This delay is present in data collected both before and after the 

gearbox replacement. Figure 6 shows field-test data for the CART2, illustrating a significant time delay between the 

commanded generator torque (demanded) and the applied torque to the High-Speed Shaft (measured). After 

examining several of these events, we determined that the average time delay was about 0.05 sec. The fact that the 

generator torque controller does not account for this time delay could explain the cause of the destabilization in this 

mode. 

To examine the effects of the delay more closely, we modeled it using a first order Pade approximation.13 The 

phase loss at the 1st drive train torsion frequency from this delay is approximately 58 degrees (deg.), as seen in 

Figure 7. To correctly add damping to the first drivetrain torsion mode, the generator torque controller must account 

for this phase loss.

Figure 4. Time series plot of Low-Speed Shaft 

Torque, with data before and after gear-box 

failure.

0 200 400 600 800 1000 1200 1400 1600 1800 2000 -80

-60

-40

-20

0

20

40

60

80

100

Time

LSS Torque

State-space

Baseline

Before gearbox replacement After gearbox replacement

Time Series Plots of shaft torq

840 841 842 843 844 845 846 847 848 849 850

-40

-30

-20

-10

0

10

20

30

40

50

Time

LSS Torque

State-space 

Baseline

Figure 5. Plot of power spectral density of LowSpeed Shaft Torque, from data collected using the 

baseline and state-space controller.

0 1 2 3 4 5 6 10-2

10-1

100

101

102

103

104

Frequency, Hz

PSD of LSS Torque

Baseline

State-space

PSD Plots of Shaft Torque

1P 1st

drive-train

torsion
9

To further study the effects of delay, we examined Bode plots of the models of the closed loop system, which 

consisted of the generator torque controller (before any modification to account for the delay) and linearized plant 

models, modified to include a 0.05 sec. delay between commanded generator torque and the actual torque input to 

the plant (red), seen in Figure 8. When placed in closed-loop with a plant having time delay, the generator torque 

controller tends to destabilize this mode, increasing the magnitude of the peak and increasing the natural frequency. 

This helps explain the behavior of the field test results in Figure 5, which show a shift in natural frequency and an

increase in magnitude of the first drivetrain torsion mode from the state-space controller.

We also targeted another modification to the generator torque control loop. In reviewing PSD plots of generator 

torque from data collected from the state-space controller, we noted undesirable control actuation at frequencies 

corresponding to once and twice the rotor rotation rate (1P and 2P). We desired to eliminate this unnecessary control 

actuation. The proximity of the tower first side-side mode (0.87 Hz) and the 1P rotor frequency (0.7 Hz) prevented 

us from simply filtering the generator torque controller output. It is clear that the generator torque controller must be 

re-designed to include the effects of filtering to remove this actuation at 1P and 2P.

We now show these generator torque control loop modifications.

Figure 6. Plot of time series of commanded and 

measured generator torque showing delay.

0.05 sec.

 demanded 

 measured 
10

Control Design Modifications and New Test Results

The generator torque controller was re-designed to include the effects of the time delay and to eliminate 

unnecessary control actuation at 1P and 2P. The re-design to account for delay consisted of augmenting the linear 

plant state-space model, with a single state representing the delay, from the Pade approximation model.

13

In addition, we refined the generator torque control design to reduce generator torque control actuation at the 1P 

and 2P rotor harmonics. We followed an approach that adds a frequency-dependent state and control weighting 

matrices to the standard LQR optimal control problem.14 Using frequency shaped cost functionals, a control designer 

may explicitly shape the loop dynamics, by selecting state and/or control weighting functions. To reduce control 

actuation at 1P and 2P, we penalized control actuation at these frequencies using inverted notch filters centered at 

these two frequencies. We calculated the full state feed-back and state estimator gain matrices by casting this 

augmented system as an H2 optimization problem and solving the resulting matrix Riccati equations in MATLAB.15

We implemented the revised generator torque control loop in the CART2 and resumed field tests. We again 

tested both the revised state-space controller and the baseline controller. MCrunch again was used to calculate 

statistics and DEL fatigue loads from the test data. Table 2 shows statistics of the results, from both the baseline 

controller and the revised state-space controller. Figure 9 shows component load fatigue DELs, comparing the 

performance of the revised state-space, original state-space, and baseline controllers. Of significance is the large 

reduction in the Low-speed Shaft Torque DEL, with the revised state-space controller (state-space new). This load 

reduction reflects the re-design to account for the effects of the generator torque time delay. It also reflects the 

modified LQR design, with frequency weighting on the control output at the 1P and 2P rotor harmonics – preventing 

unnecessary control actuation at those frequencies. Preventing this unnecessary control actuation also reduces torque 

loads that the generator applies to the drivetrain at 1P and 2P. 

We also compared psd’s of the Low-speed Shaft Torque from the baseline and revised state-space controllers, as 

shown in Figure 10. This figure shows a dramatic reduction in the magnitude of the peak at the first drivetrain 

torsional frequency, from the revised state-space controller, showing that active damping is successfully being 

applied to this mode using generator torque control. 

Figure 7. Bode plot of Pade Approximated Delay 

Transfer Function.

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

Magnitude (dB)

100 101 102

225

270

315

360

Phase (deg)

Bode Diagram

Frequency (rad/sec)

58 deg

1st drive-train torsion

frequency (22.4

rad/sec)

Figure 8. Bode plot of linear plant models in 

closed-loop with original generator torque 

controller showing effects of a delay.

-120

-110

-100

-90

-80

-70

-60

10 20 30 40 50 60 70 80 90 100

-90

0

90

180

270

Bode Diagram

Frequency (rad/sec)

freq = 22.43 rad/s

mag = -72 dB

freq = 22.43 rad/s

phase = 192 deg.

freq = 23.72 rad/s

phase = 122 deg.

freq = 23.72 rad/s

mag = -65 dB

Bode Diagram

Magnitude

(dB)

Phase

(deg)

 30

Frequency (Hz)

- plant w/o delay

- plant w/ delay
11

TABLE 2. COMPARISON OF BASELINE AND

STATE-SPACE CONTROLLER (BLADE INDEPENDENT PITCH AND GENERATOR TORQUE) DATASET STATISTICS FOR 

REGION 3 OPERATION (LOCKED TEETER)

Statistics and 

Performance Measure

Baseline

PID Control (1460 sec.)

Revised State-Space 

(1220 sec.)

Wind speed (m/s) mean 14.92

std 2.33

mean 15.83

std 1.90

Rotor speed (rpm) mean 41.61

std 0.30

mean 41.28

std 0.44

Blade pitch (deg) mean 6.21

std 4.18

mean 9.06

std 2.02

Blade pitch rate (deg/s) std 2.1

max 13.9

min -11.4

std 3.2

max 14.9

min -15.3

Generator torque (kNm) Std 158.4

Max 3524

min 2526

std 124.8

max 4002

min 3048

The revised state-space controller also reduces loads in the other components, as seen in the red bars in Figure 9. 

The collective blade pitch controller is reducing tower fore-aft fatigue DELs by adding active damping to the 

tower’s first fore-aft mode. The generator torque controller is successfully reducing tower side-side fatigue DELS 

through active damping of the tower’s first side-side mode. The independent blade pitch is reducing the blade flapbending fatigue DEL’s through mitigation of the effects of shear across the rotor disk. This is a result of including a 

model of the shear disturbance into the augmented plant model used for the design of the independent blade pitch 

controller. All of the objectives in blade pitch control are accomplished, with just a modest increase in blade pitch 

rates, compared to the baseline controller, as seen in Table 1 (blade pitch rates). 

Figure 9. PID, old state-space, and new state-space controller field-test results component 

DELs.

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

Low-speed Shaft 

Torque DEL (Nm)

Tower s-s bending 

DEL (kNm)

Tower f-a bending 

DEL (kNm)

Blade flap-bending 

DEL (kNm)

Normalized Value

Load Category

PID

State-space old

State-space new
12

In Figure 11, we see that the new generator torque state-space controller eliminates control actuation at 1P and 

2P, when compared to the old generator torque state-space controller. Both controllers provide actuation at the 

tower’s first side-side (s-s) mode, meeting the control objective of adding damping to this very lightly damped 

structural mode. Elimination of actuation at these per-revolution harmonics of rotor-speed also contributes to the 

fatigue DEL reduction, from the revised controller, seen in Figure 10.

Conclusions 

A two-loop control architecture for mitigating wind turbine fatigue loads in Region 3 has been described. The 

blade pitch control loop is designed to regulate turbine speed and add active damping to the tower’s first fore-aft 

mode, with a collective pitch component, and to mitigate the effects of asymmetric wind distributions across the 

rotor, with an independent blade pitch component. A separate generator torque control loop adds active damping to 

the tower’s first side-side mode, as well as, to the drivetrain's first torsion mode. These control loops are designed 

using standard LQR theory. The blade pitch control algorithm is designed using Disturbance Accommodating 

Control, wherein a model of the disturbance using disturbance waveforms is included in the linear model for control 

design. 

After implementation and the initial field tests of this advanced controller, the CART2 machine gearbox failed 

due to a gear tooth misalignment. After replacement of this gearbox with a spare, field tests were resumed. The 

generator torque control loop seemed to increase drivetrain torque loads dramatically, when compared to the limited 

data gathered before the gearbox failure. The cause of this instability was determined to be a small time delay (0.05 

sec.) between the time that the generator torque was demanded and application of the torque to the real plant. The 

original generator torque control loop did not account for this delay.

The generator torque control loop was re-designed to account for this delay, as well as, to reduce unnecessary 

control actuation at certain harmonics in the rotor-speed. Initial field tests of the re-designed controller demonstrated

improved control performance due to these modifications. Initial field tests demonstrated that the two loop blade 

pitch controller and generator torque controller successfully reduced fatigue loads in the Low-speed shaft, blade 

roots, and tower.

Initial test results, with the state-space controller, show promise for the load mitigation capability of this 

controller, compared to a standard baseline PID controller in Region 3 turbine operation. Further testing of this 

revised state-space controller is needed to further validate the performance of this controller. Additional tuning of 

the gains may be attempted to obtain further load reductions, especially in the blade flap-bending loads, by tuning 

of the shear disturbance gain. These gain increases will have to be balanced, with the resulting increase in blade 

pitch rates. We believe that more significant blade load reductions may be possible through increasing this 

disturbance gain.

Figure 10. Plot of power spectral density of LowSpeed Shaft Torque, from data collected with the 

baseline and state-space controller.

2 2.5 3 3.5 4 4.5 5 10-2

10-1

100

101

102

103

104

Frequency, Hz

PSD of LSS Torque

Baseline

Revised State-Space

PSD Plots of Shaft Torque

Figure 11. Power spectral density of generator 

torque from old and revised state-space 

controllers.

0 0.5 1 1.5 2 2.5 3 101

102

103

104

105

106

Frequency, Hz

PSD of GenTorque

Revised SS

old SS

PSD Plots of Generator Torque

1st s-s

1 P

2 P
13

Future Work

We plan to continue field-tests of this two loop control architecture on the NREL CART2 machine. These final 

tests will conclude the work on this two-loop advanced controller and the results will be documented through a final 

report.

References

1

Bossanyi, E., “Developments in Individual Blade Pitch Control,” EWEA Conference, The Science of Making Torque from 

Wind, DUWIND, Delft University of Technology, The Netherlands, April 19-21, 2004.

2

Bossanyi, E., Wright, A., and Fleming, P., “Controller field tests on the NREL CART2 turbine,” UPWIND report 

11593/BR/08, 22nd June 2010, available at http://www.upwind.eu/Shared%20Documents/WP5%20-

%20Publications/D%205.6.1.pdf.

3

van Engelen, T.G., “Design Model and Load Reduction Assessment for Multi-rotational Mode Individual Pitch Control,” 

Presented at the 2006 European Wind Energy Conference and Exhibition, Athens, Greece, 27 February – 2 March, 2006.

4

Geyler, M., and Caselitz, P., “Individual Blade Pitch Control Design for Load Reduction on Wind Turbines,” Presented at 

the 2007 European Wind Energy Conference and Exhibition, Milan, Italy, 7-10 May, 2007.

5

Wright, A., Fingersh, L., and Stol, K., “Testing Further Controls to Mitigate Loads in the Controls Advanced Research 

Turbine,” 29th ASME Wind Energy Conference, Orlando, Fl., January 2010.

6 Balas, M.J., Lee, Y.J., and Kendall, L., “Disturbance Tracking Control Theory with Application to Horizontal Axis Wind 

Turbines,” 17th ASME Wind Energy Conference, Reno, Nevada, 12-15 January, 1998.

7

Fingersh, L., and Johnson, K., “Baseline Results and Future Plans for the NREL Controls Advanced Research Turbine,” 23rd

ASME Wind Energy Conference, Reno, NV, 2004, pp. 87-93.

8

Jonkman, J. M., and Buhl, M. L., “FAST User’s Guide,” National Renewable Energy Laboratory, NREL/EL-500-38230, 

Golden, CO, 2005.

9

Wright, A. D., “Modern Control Design for Flexible Wind Turbines,” National Renewable Energy Laboratory, NREL/TP500-39154, Golden, CO, 2004 at http://www.nrel.gov/docs/fy06osti/39154.pdf.

10Kwakernaak, H., and Sivan, R., Linear Optimal Control Systems, Wiley Interscience, NY, 1972, pp. 207-237.

11

Buhl, M. L. Jr.., “MCrunch User’s Guide for Version 1.00,” NREL Report No. TP-500-43139, National Renewable Energy 

Laboratory, Golden, Colorado, May, 2008.

12

Sutherland, H. J., “On the Fatigue Analysis of Wind Turbines,” Sandia National Laboratories, SAND99-0089, 

Albuquerque, New Mexico, June 1999.

13 Baker, G. A. Jr. "The Theory and Application of The Pade Approximant Method." In Advances in Theoretical Physics, 

Vol. 1 (Ed. K. A. Brueckner). New York: Academic Press, pp. 1-58, 1965. 

14 Gupta, N. K., “Frequency-Shaped Cost Functionals: Extension of Linear-Quadratic-Gaussian Design Methods,” Journal of 

Guidance and Control, Vol. 3, No. 6, Nov.-Dec. 1980.

15 Skogestad, S., and Postlethwaite, I., Multivariable Feedback Control Analysis and Design, John Wiley & Sons, Ltd, West 

Sussex, England, 2005. 
F1147-E(10/2008)

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Refinements and Tests of an Advanced Controller to Mitigate 

Fatigue Loads in the Controls Advanced Research Turbine: Preprint

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14. ABSTRACT (Maximum 200 Words)

Wind turbines are complex, nonlinear, dynamic systems forced by aerodynamic, gravitational, centrifugal, and 

gyroscopic loads. The aerodynamics of wind turbines are nonlinear, unsteady, and complex. Turbine rotors are 

subjected to a complicated 3-D turbulent wind inflow field, with imbedded coherent vortices that drive fatigue loads 

and reduce lifetime. Design of control algorithms for wind turbines must account for multiple control objectives. Future 

large multi-megawatt turbines must be designed with lighter weight structures, using active controls to mitigate 

fatigue loads, while maximizing energy capture. Active damping should be added to these dynamic structures to 

maintain stability for operation in a complex environment. At the National Renewable Energy Laboratory (NREL), we 

have designed, implemented, and tested advanced controls to maximize energy extraction and reduce structural 

dynamic loads. These control designs are based on linear models of the turbine that are generated by specialized 

modeling software. In this paper, we present field test results of an advanced control algorithm to mitigate blade, 

tower, and drivetrain loads in Region 3. 

15. SUBJECT TERMS

wind turbines; fatigue loads; 3-D turbulent wind inflow field

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