Usin

Learning 

Turbine Performan

Senior Engineer, NREL National Wind 

andrew.clifton@nrel.go

Presented to Power Curve Workin

Brande, Denm

NREL is a national laboratory of the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, operated by 
Yesterday’s approximation – strea

Air Density

Wind Speed

The Betz Limit

Efficiency

Rotor Diameter

In low shear and low turbu

hub‐height wind speed is a 
Today’s need – include the real atmo

How can we include the a

layer in turbine perf

Illustrat
   

Stability is just one part of the pu

There’s no clear link between stability and

power production.

Wind Speed (ms‐1)

S. Wharton and J. K. Lundquist, Atmospheric stability

affects wind turbine power collection, Environmental

Research Letters 7 (2012)

Normalized Power (%) Strongly convective Strongly stable Convective

Data stratified by

Ti(w’)

Is it better to look at shear and turbulence?

Data stratifieby shear andRichardson N

Convec

Conv

Sta

B. Vanderwende and J. K. 

of wind turbine performa

atmospheric regimes, ERL 
Research question

• How do we include the effect of she

turbulence on the performance of a 

Do we

o Remove it – find a zero turbulence powo Acknowledge it – provide power curves 

different turbulence intensities and she

o Embrace it – figure out ways to include 

variables?
Tools for exploring turbine behavi

TurbSim – flow simulator FAST – turbine sim

Wind

Rotor Axis 

Yaw Axis 

TipRad 

Precone 

(negative as shown) 

Apex of Cone 

of Rotation 

ShftTilt 

(negative as s

OverHang 

Hub C.M. 

HubCM 

(negative as shown) 

Pitch Axis 

HubRad 

(negative as shown) 

Yaw Bearing 

C.M. 

http://wind.nrel.gov/designcodes/preprocessors/ http://wind.nrel.gov/des
The WindPACT 1.5MW baseline tu

Parameter Value2Hub height 84 m

Rotor diameter 70 m

Cut‐in 3 m/sRated speed 11.5 m/s

Power power 1500 kWRated RPM 20.5 RPM

Cut‐out 27.6 m/s

[1] R. Wiser and M. Bollinger, 2011 Wind technologies market report, DOE, 2011

[2] R. Poore and T. Lettenmaier. Alternative design study report: WindPACT

advanced wind turbine drive train designs study. SR‐500‐33196, NREL, 2003.

More than 50% of turbines installed in the

USA from 2002‐2011 were 1‐2 MW1
For

•

Simulating a power performance 

cing = log‐law win

o Hub‐height horizon

U = [3 ... 25] ms‐1

o Hub‐height turbule

Ti = [5 ... 40] %

o Power‐law speed p

exponentα = [‐0.5 ... 0.5]

o Constant density

ρ = 1.225 kg m‐3

Output = 10‐minut
Subsets of the data

7‐8 m s‐1

12‐13 m s‐1 (rated = 11.5 m s‐1)

Power output depen

– Below rated (Regi

• Ti increases pow• Shear increases 

– Above rated (Regi

• Ti decreases pow• Shear less impor

– Is this actionable?

16‐17 m 
Modeling power output

‘Classification and R

• Need continuous

predictions of power

• Must be ‘trainable’ using

observations

o Simulationso Field testing

• Limited inputs

o Wind speed & operating

region

o Rotor‐disk shearo Turbulence intensity

• One output

o 10‐minute power

One branch of a reg
Regression tree model results

7.5 m s‐1 Power output is a fu

region, Ti and she

• What were the c

the turbine was t

• What are the con

new site?

12.5 m s‐1 (rated = 11.5 m s‐1) 16.5 m s‐

Contour lines show power normalized by zero‐turbulencecase
A note on high turbulence intensi

• Assume u is normally

distributed*

o 15.8 % of observations will be

less than U‐ߪ)u)

o 2.2 % of observations will be

less than U‐2ߪ)u)

o All nicely defined by the

normal distribution

• Even at high mean wind

speeds, wind speeds can be

below rated if Ti is high

enough

o Chance of wind speeds below

rated if Ti > 10% at 16.5 m s‐1

o Compare with plots of

power versus shear and Ti on

previous page

Wind speeds during a 

Mean wind spee

Rated

wind

speed

*this is an approximation, used to show the possible effects of Ti
Higher‐fidelity model is more accu

RMSE (kW) MAE (kW) 

150 100 

50 

0 

60 40 20 

0 

Power Curve Regression Tree 

940 950 960 970 980 990 1000 

Mean Power (kW) 

Randomly split si

training and te

• 898 random d

(50% of simu

• Predict 10‐mi

• Power curv• Regression 

• Quantify erro

• Root‐mean• Mean abso

• Repeat 50 tim
Creating a site‐specific power esti

• Use time series of wind

speed, shear and Ti

o Plug numbers in to the

regression tree

o Get power estimateo Easy to do with a time

series...

o ...needs some thought

on how to do it with awind rose.

0 

0.2 0.4 0.6 0.8 

1 

1.2 1.4 

No turbuleSite speci

01/03 01/10 01/17 01

Da

Normalized Energy Capture 

Plots are preliminary and for illustration only. From: Clifton, A., Daniels,M. H., and Lehning, M. Causes of mountain pass winds and their effectson wind turbines. Wind Energy. In revision, 2/2013
What I learned about Machine‐Le

Pros• Doesn’t require you to know

all of the physics

• Lets you use a lot more of the

data you have

o Wind speed, shear, Ti, regiono Could add veero Could divide shear into top

and bottom of rotor

o Could use rotor‐equivalent

wind speed

• Can be very fast

• Can give you site‐specific

power estimate

Cons

• Needs a good da

the model

• Training conditio

bracket site con
a

Conclusion: multivariate power curves a

• Simulations show strong

sensitivity to Ti

• Regression trees offer

lower error

• Needs testing with more

7.5 m s

dat

16.5 m 
References

Clifton, A., Kilcher, L., Lundquist, J., and Fleming, P.

Using machine learning to predict wind turbine power output.Environmental Research Letters. Accepted 3/2013

Clifton, A., Daniels, M. H., and Lehning, M.

Causes of mountain pass winds and their effects on wind turbines.

Wind Energy. In revision.