Baseline Results and Future 

Plans for the NREL Controls 

Advanced Research Turbine

Preprint 

November 2003 • NREL/CP-500-35058 

L.J. Fingersh and K.E. Johnson 

To be presented at the 23rd ASME Wind Energy 

Symposium 

Reno, Nevada 

January 5–8, 2004 

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Baseline Results and Future Plans for the NREL Controls Advanced Research Turbine 

Lee Jay Fingersh, Kathryn E. Johnson 

lee_fingersh@nrel.gov, kathryn_johnson@nrel.gov

National Renewable Energy Laboratory 

National Wind Technology Center 

1617 Cole Blvd., Golden, MS 3811, Colorado 80401

ABSTRACT

The National Renewable Energy Laboratory (NREL) 

has commissioned a highly modified Westinghouse 600 

kW wind turbine as its Controls Advanced Research 

Turbine (CART). The capabilities of the original 

turbine have been increased through the installation of a 

high-speed data-acquisition and control system, a widebandwidth variable-speed generation system, and a 

high-speed independent blade pitch system. This 

turbine’s purpose is to field-validate new control 

paradigms developed by NREL, universities, and 

industry and to provide data to validate new codes that 

have been developed for controls design. 

Testing on the turbine began in late 2001, and baseline 

data were collected during the 2001 – 2002 wind 

season. During this time, the turbine was operated in 

both constant and variable-speed modes. Baseline 

performance and loads data are presented here. Results 

indicate that the new turbine systems are performing 

well and sufficient data have been collected so that 

comparisons to more sophisticated control algorithms 

can be conducted. 

During the 2002 – 2003 wind season, several new 

algorithms were tested on the turbine. These include an 

“Optimally Tracking Rotor” algorithm proposed before, 

an adaptive power tracking algorithm and several fullstate feedback systems. General results from these 

algorithms are presented here with detailed results 

presented elsewhere. 

TEST BED

The test bed turbine for this project is called the 

Controls Advanced Research Turbine or “CART” 

(Figure 1). This 2-bladed, upwind Westinghouse 600 

kW wind turbine began its life at Kahuku on the island 

of Oahu in Hawaii and was moved to the National Wind 

Technology Center in 1996. Its primary purpose is to 

enable the field-testing and evaluation of advanced 

control paradigms. To accomplish this purpose, it had 

to be extensively modified. 

Figure 1 - Controls Advanced Research Turbine in 

operation at the National Wind Technology Center. 

Modifications to the turbine included replacing the 

stock synchronous generator and fluid coupling with a 

squirrel cage induction machine and full power 

electronics set. This new system has the capability to 

control torque from minus rated (motoring) to plus rated 

(generating) at any speed. The torque loop has a very 

high rated bandwidth of 500 radians per second. 

The original Westinghouse machine used a rotating 

hydraulic system to provide pitch control. Because this 

system proved to be very slow and unreliable, it was 

replaced with a high-speed electro-mechanical system 

composed of low-inertia electric servo motors 

connected to a large bull gear through a gearbox. The 

system is capable of pitching the blades independently 

at over 19° per second with pitch accelerations 

exceeding 150° per second per second. 

Instrumentation on the machine includes a full 

complement of instruments that gather upwind 

meteorological data at four heights, temperature data at 

two heights, and a sonic anemometer. Blade root flap 

and edge strain gauges, tower bending gauges, a lowspeed shaft torque transducer, and a high-speed shaft 
2 

torque transducer gather loads data. Two nacellemounted tri-axial accelerometers and a front-bearing 

mounted inertial motion unit that includes three 

accelerometers and three rate gyros complement the 

strain gauges. Absolute position encoders gather 

position data on pitch, yaw, low-speed shaft, and highspeed shaft. By measuring both low-speed and highspeed shaft positions, it is possible to directly measure 

shaft torsional deflection. 

All of this data is sampled at 100 Hz. A custom-built 

control system both collects this data and controls the 

turbine at a control loop cycle rate of 100 Hz. This 

system is PC-based and is therefore very flexible. 

OPERATION

The CART can be operated in either of two basic 

modes: constant-speed and variable-speed. In constantspeed operation mode, the power electronics set is 

bypassed and the induction generator is directly 

connected to the grid. In variable-speed mode, all 

power flows through the power electronics system. 

In constant-speed mode, starts are accomplished by 

wind starting the machine. This means that the blades 

are pitched in such a way as to cause the rotor to speed 

up to synchronous speed. When synchronous speed is 

reached, rapid pitch motions are used to hold the 

machine at that speed until a very constrained group of 

conditions are met. These conditions are designed to 

reduce the torque and current transients that are 

triggered when the machine is connected to the grid. 

Once this connection is made, pitch is used to extract 

maximum power from the wind or to control this power 

to the rated power of the machine. Figure 2 shows an 

example of this type of operation. 

Figure 2 – Constant speed mode startup. 

The wind is also used to start the machine in variablespeed mode. However, once the machine is turning, the 

power electronics can be brought on-line at any time. 

There are no torque or current transients involved with 

synchronizing the power electronics and so very few 

conditions must be met for this to occur. Once the 

machine is online, torque is used to control rotor speed. 

If sufficient wind is available, rated speed will be 

reached at which time torque is at its maximum value 

and pitch is used to control rotor speed. This torque 

control algorithm is shown in Figure 3. 

Variable Speed Torque Vector

0

20

40

60

80

100

120

140

160

180

200

0 10 20 30 40 50

RPM

Torque (kN-m)

Region 1

T=0

Region 2

T=kω2

Region 3

T=max

Region 2 1⁄2 

T=5% slip

Figure 3 – Torque vector and regions for 

variable-speed control. 
3 

The torque vector chart in Figure 3 includes four 

distinct operational modes. The first mode (region 1) is 

before or during startup, during which time the turbine 

may be rotating but no power is extracted. The second 

mode (region 2) is designed to keep tip-speed ratio 

(TSR) constant by controlling torque to a constant 

(more on the choice of this constant later) times 

rotational speed squared (T=kω2

) [1]. The third mode 

(region 3) is entered when the machine reaches rated 

rotor speed, torque is maximum, and speed is controlled 

by blade pitch. 

The fourth mode (region 2 1⁄2) is only there because the 

region 2 controller does not reach rated torque at the 

rated speed of the machine. If region 2 1⁄2 did not exist, 

the rated power of the machine would be reduced 

because the region 3 torque would simply be the region 

2 torque when the machine hit rated speed. To ensure 

that the machine has the desired rated power, region 3 

must have rated torque and a new region must be 

inserted to connect region 2 to region 3. In the case of 

this turbine, these two regions are connected by a 

straight line with a slope that corresponds to the torque 

slope of an induction machine with a rated slip of 5%. 

This relatively high slip number was chosen because it 

provides a soft cushion between modes that prevents 

excessive torque oscillations. Although rotor power 

coefficient (Cp) is reduced in this region, the total 

energy lost on an annual basis is usually less than 0.5% 

compared with a machine in which T=kω2

 could be 

maintained all the way to region 3. Figure 4 shows an 

example of variable-speed turbine operation through 

these modes. 

Figure 4 – Variable-speed startup and operation in 

regions 2, 21⁄2, and 3. 

In either constant-speed or variable-speed control 

modes, more sophisticated algorithms can be employed 

to control blade pitch in such a way as to reduce loads 

[2] or improve energy capture [1, 3]. In variable-speed 

mode, torque control can also be employed for the same 

purposes [1, 2, 3]. 

DATA COLLECTION

During the 2001 – 2002 wind season, at least 50 hours 

of data were collected in each of the two basic operating 

modes, constant-speed and variable-speed. During the 

2002 – 2003 wind season, more than 50 hours of data 

were collected in two more modes, optimally tracking 

rotor [1] (OTR) and adaptive control [3]. Additionally, 

smaller amounts of data were collected in two 

additional modes. The first mode is designed to use 

torque to avoid a tower resonance mode at which rotor 

2 per revolution (2-P) interacts with the tower’s first 

natural frequency. The second is designed to use fullstate feedback to reduce blade and/or tower loads [2]. 

DATA ANALYSIS

Constant-Speed and Variable-Speed

The baseline mechanical power curves for the two basic 

modes are plotted in Figure 5 with each point 

representing a 10-minute average. As can be seen in the 

figure, for wind speeds above 8 m/s, the constant-speed 
4 

power curve is higher than the variable-speed power 

curve. This is curious because variable-speed operation 

should, in theory, provide the rotor the ability to run at 

the peak of the Cp-TSR curve at all wind speeds below 

rated power. 

Power Curves

0

100

200

300

400

500

600

700

4 8 12 16 20

Windspeed (m/s)

Power (kW)

Variable Speed Constant Speed

Figure 5 - Baseline mechanical power curves. 

The reason for this discrepancy is shown in Figure 6. 

The predicted Cp curve and the actual measured curve 

are quite different. Since the constant (k in T=kω2

) was 

derived from the predicted curve, k did not match the 

actual rotor performance and the rotor was therefore not 

being properly controlled to the correct tip-speed-ratio. 

The result of this sub optimal operation is a reduced 

power curve. Energy losses from this type of error can 

be dramatic as was shown in [1]. 

Cp versus TSR

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0 5 10 15 20

TSR

Cp

Constant Speed Predicted Constant Speed Fit

Figure 6 - Cp - TSR curves, power limited to rated. 

Adaptive Control

There are essentially two ways to avoid this problem. 

One is to avoid relying on predictions and use actual 

measured Cp curves to derive the value of k. This is a 

tedious and sometimes impossible process because 

many variable-speed turbines are not capable of 

operating in a constant-speed mode. Additionally, 

turbine-to-turbine variability and changes in rotor 

performance over time would not be accounted for 

using this method. The other approach is to derive an 

adaptive scheme that would always be seeking the 

correct value for k. A thorough discussion of this 

approach is in [3]. 

Optimally Tracking Rotor Control

Even if the value of “k” in the variable-speed control 

law is set at the perfect value, the rotor speed will not 

perfectly track the wind. The relatively large rotational 

inertia of the rotating system in a wind turbine prevents 

the rotor from accelerating and decelerating as quickly 

as the wind speed can change. This leads to energy 

capture losses caused by failure of the machine to 

operate at the peak of the Cp-TSR curve. 

In [1], a new strategy for reducing variable-speed 

tracking losses was described. This method, called 

optimally tracking rotor control (OTR), is implemented 

by adding a second term to the variable-speed control 

law. 

( ) 2 2 T = kω − G Taero − kω

where •

Taero = Tshaft + J ω

The new term in the control law consists of the actual 

aerodynamic torque minus the torque that would be 

extracted by the standard variable-speed control law. 

This can be thought of as the “net torque” available to 

accelerate or decelerate the rotor. This value is then 

multiplied by a gain “G” and subtracted from the 

original control law. When the net torque is positive (at 

low values of TSR), the term will be negative and the 

control torque will be reduced. Conversely, when the 

net torque is negative, the torque will be increased. 

Both situations would lead the rotor to more rapidly 

approach the desired operating point. 

The OTR strategy is difficult to analyze from measured 

turbine data. Because expected energy gains from this 

approach are expected to be on the order of 1%, 

extremely accurate, long-term measurements would be 

required. Rather than attempting this type of 

experiment, a detailed analysis of simulations is 

provided in [3]. Figure 7 gives an example of the way 

the algorithm is supposed to work. When the rotor is 

accelerating, torque is lower than with the standard 

(T=kω2

) algorithm, which helps the rotor to accelerate. 

Conversely, when the rotor is decelerating, torque is 

higher thereby helping the rotor to decelerate. 
5 

Figure 7 – Optimally tracking rotor (OTR) example. 

Note how OTR uses torque to help the rotor to 

accelerate and decelerate. 

Resonance avoidance

Within the normal operating speeds of the CART, rotor 

2-P excitation can drive the tower at its first natural 

frequency. To alleviate this problem, a strategy was 

developed to avoid operating at a rotor speed that can 

cause this resonance. The tower resonance avoidance 

strategy employs the simple torque controller found in 

Figure 8. This curve is designed to multiply the 

standard control law (depicted as the horizontal line) to 

give a final torque demanded vector. As rotor speed 

increases, the controller is designed to prevent the rotor 

speed from reaching the resonance point until 

aerodynamic torque is sufficient to allow the rotor to 

run through the resonance rapidly. When speed is 

decreasing, the opposite strategy is employed. Speed is 

kept above the resonance point until aerodynamic 

torque has fallen sufficiently to allow the machine to 

apply shaft torque of sufficient magnitude to rapidly 

slow the machine through the resonance point. 

 

Resonance point

Figure 8 - Tower resonance avoidance strategy. 

Demanded torque with speed hysteresis is used to 

avoid operation at or near the resonance point. 

Although this controller is quite simple, its operation 

has been very successful as shown in Figure 9. In this 

example, the wind happened to be at just the right level 

such that, without the resonance avoidance controller, 

the rotor speed would have been at or near the 

resonance point throughout the data segment shown. 

Because of the controller, this did not occur. Rather, 

the rotor speed was held above or below the resonance 

point and then forced to rapidly transition through the 

resonance point by a positive or negative torque 

excursion. 

Figure 9 – Tower resonance avoidance example. 

Resonance is centered at 25.8 RPM. 
6 

The primary purpose for the resonance avoidance 

controller is to reduce cyclic loading on the tower. 

Figure 10 shows a magnified view of the same section 

of data as in Figure 9 and a section of data from another 

data set. The second data set (shown on the right) did 

not employ the resonance avoidance strategy. 

Comparing the tower base bending loads between the 

two sets of data indicates that transitioning through the 

resonance quickly rather than dwelling at the resonance 

point greatly reduces the excitation of the tower. 

Figure 10 – Effect of resonance avoidance strategy 

on tower loads. 

Full-State Feedback

By employing full-state feedback, it is possible to 

utilize many of the available turbine sensors as inputs to 

a controller that can be designed to perform many 

functions. For example, it is possible to use blade load 

feedback to independently control blade pitch in such a 

way as to reduce blade loading. Since tower fore-aft 

motion is largely driven by out-of-plane blade loading, 

tower loads may also be reduced by such a strategy. 

A full analysis of this type of control is not provided 

here because we have not yet collected enough data to 

complete such an analysis. However, the controllers are 

working well and stable operation has been 

demonstrated. An example of the performance of a 

state-space controller is shown in Figure 11. In this 

example, the controller is controlling the pitch angle of 

each blade independently in response to blade root 

bending loads. For a more detailed description of this 

control technique, see [2]. 

Figure 11 – Full-state feedback example. Note 

independent pitching in response to different blade 

loads. 

FUTURE WORK

During the 2003 – 2004 wind season, we intend to 

continue testing the adaptive controller discussed in [3]. 

We also intend to collect larger volumes of data on the 

state-space controllers described in [2]. 

We are also acquiring a three-bladed hub and blade set 

for this project. This will enable us to test additional 

algorithms that are unique to three-bladed machines. 

Three-bladed and two-bladed machines possess 

fundamental differences that affect control design. 

These include symmetrical versus asymmetrical yaw 

inertia and lacking or possessing a teeter hinge. This 

new hub will either be installed on the CART or onto a 

nearly identical turbine a few hundred meters away. If 

the second option is selected, that turbine will be 

modified from its initial configuration in the same way 

as the CART. These modifications will not be difficult 

because many of the instrumentation modifications 

necessary have already been made to prepare the 

machine for a project called the Long-Term Inflow 
7 

Structural Test (LIST) [4]. This project is now 

completed but much of the instrumentation remains on 

the machine or is readily available. 

ACKNOWLEDGMENTS

The U. S. Department of Energy is credited for its 

funding of this document through the National 

Renewable Energy Laboratory under contract number 

DE-AC36-99GO10337. 

REFERENCES

[1] Fingersh, L., Carlin, P., 1998, “Results from the 

NREL Variable-Speed Test Bed,” Proceedings of the 

Wind Energy Symposium, Reno, NV. Reston, VA: 

American Institute for Aeronautics and Astronautics. 

[2] Stol, K. A., Fingersh, L. J., 2004, "State-Space 

Control Implementation on the Controls Advanced 

Research Turbine (CART)" NREL/SR-500-35061. 

Golden, CO: National Renewable Energy Laboratory. 

 [3] Johnson, K., Fingersh, L., Balas, M., Pao, L., 

2004, “Methods for Increasing Region 2 Power Capture 

on a Variable Speed HAWT.” Proceedings of the Wind 

Energy Symposium, Reno, NV. Reston, VA: American 

Institute for Aeronautics and Astronautics. 

[4] Kelley, N., Hand, M., Larwood, S., McKenna, E., 

2002, “The NREL Large-Scale Turbine Inflow and 

Response Experiment – Preliminary Results.” 

Proceedings of the Wind Energy Symposium, Reno, 

NV. Reston, VA: American Institute for Aeronautics 

and Astronautics. 

[5] Carlin, P., 1997, “Some Analytic Expressions for 

Upper Limits on the Mean Annual Wind Turbine Power 

in a Rayleigh Wind Regime.” Proceedings of the Wind 

Energy Symposium, Reno, NV. Reston, VA: American 

Institute for Aeronautics and Astronautics. 

[6] Carlin, P., Fingersh, L., 1997, “Some Preliminary 

Results from the NWTC Direct-Drive, Variable-Speed 

Test Bed.” Proceedings of the Wind Energy 

Symposium, Reno, NV. Reston, VA: American Institute 

for Aeronautics and Astronautics. 

 [7] Fingersh, L., Robinson, M., 1997, “The Effects of 

Variable Speed and Drive Train Component 

Efficiencies on Wind Turbine Energy Capture.” 

Proceedings of the Wind Energy Symposium, Reno, 

NV. Reston, VA: American Institute for Aeronautics 

and Astronautics. 

[8] Fingersh, L., Johnson, K., 2002, “Controls 

Advanced Research Turbine (CART) Commissioning 

and Baseline Data Collection.” NREL/TP-500-32879. 

Golden, CO: National Renewable Energy Laboratory. 
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Baseline Results and Future Plans for the NREL Controls Advanced Research Turbine: 

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During the 2002 – 2003 wind season, several new algorithms were tested on the Controls Advanced Research Turbine 

(CART) at the National Renewable Energy Laboratory. These include an “Optimally Tracking Rotor” algorithm 

proposed before, an adaptive power tracking algorithm and several full-state feedback systems. General results from 

these algorithms are presented here with detailed results presented elsewhere. 

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