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.

Accounting for the Effect of Turbulence 

on Wind Turbine Power Curves

Andrew Clifton (NREL) and Rozenn Wagner (Danish Technical University) 

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The information contained in this poster is subject to a government license. 

The Science of Making Torque From Wind 

Copenhagen, Denmark 

June 18–20, 2014 

NREL/PO-5000-62234 

Wind turbines operate in a wide range of atmospheric 

conditions. It is essential to be able to model turbine power 

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using the method of binning can accommodate shear using 

the equivalent wind speed (Wagner et al. 2011). However, 

turbulence also affects turbine performance (Figure 1). This 

poster presents lessons 

learned using methods 

that can include the 

effect of turbulence. 

OBJECTIVES

Figure 1. The effect of 

turbulence intensity (Ti) 

on the power produced 

by the WindPACT 1.5-

MW turbine, simulated 

with TurbSim and FAST

TWO METHODS TO ACCOUNT FOR TURBULENCE

The turbulence renormalization method of Albers (2010) 

assumes that wind speed in a 10-minute period follows a 

Gaussian distribution described by the mean wind speed 

and the turbulence intensity, and that the mean turbine 

power in that interval is a function of the distribution of wind 

speeds in the interval. These assumptions allow a model to 

be created from power performance test data (Figure 2). 

After calibration, the model can be used to estimate turbine 

power output at any wind speed and turbulence intensity. 

This method is proposed for inclusion in the next 

International Electrotechnical Commission (IEC) standard 

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61400-12-1). For details, see Clifton et al. 2013. 

This machine learning tool uses power performance data to 

train an ensemble of decision-tree models of power versus 

wind speed, shear, and turbulence (Figure 3). Other 

variables can be added to the model as well. 

TURBULENCE RENORMALIZATION RANDOM FORESTS

Figure 3. An example of 

part of one of the 100 

decision trees that form the 

random forest model of the 

turbine’s performance

USING THE TURBULENCE RENORMALIZATION METHOD

To test the turbulence renormalization method, the data set 

is split into two subsets according to the hub-height 

turbulence intensity. One subset is used to train the model, 

and results are tested against the other subset (Figure 4 

and Figure 5). 

Figure 4. Power 

predicted for highturbulence wind using 

the renormalization 

method trained on lowturbulence data

Figure 5. Power 

predicted for lowturbulence wind using 

the renormalization 

method trained on 

high-turbulence data

Although the method captures the deviation in the power 

output as the turbulence changes, the magnitude of the 

effect is overpredicted. This leads to large errors around 

rated speed when this method is used with the :LQG3$&7

turbine data. 

USING THE RANDOM FOREST METHOD

For more information, e-mail andrew.clifton@nrel.gov or 

rozn@dtu.dk. See also: 

Albers, A. (2010). “Turbulence and shear normalisation of wind 

turbine power curves.” European Wind Energy Conference 

Proceedings. 

Clifton, A.; Kilcher, //XQGTXLVW-.)OHPLQJ338VLQJ

machine learning to predict wind turbine power output.” 

Environmental Research Letters. 

Clifton, A.; Wagner, R. (2014). “Accounting for the effect of 

turbulence on wind turbine power curves.” The Science of Making 

Torque Proceedings; June 18–20, 2014. Copenhagen, Denmark. 

Wagner, R.; Courtney, M.; Gottschall, J.; Lindelöw-0DUVGHQ3

(2011). “Accounting for the speed shear in wind turbine power 

performance measurement.” Wind Energy. 

The random forest method can only predict data that lie 

within the bounds of the conditions that the model was 

trained on. An example of their use is to reduce a large 

power-performance data set to power curves for specific 

turbulence intensities (Figure 6).

Ideally, a random forest model of turbine performance 

would be trained with data from a site that experiences a 

wide range of wind speed, shear, and turbulence. This 

model could then be used at many other locations. 

Figure 6. Power 

predicted for different 

turbulence intensity 

levels using the 

random forest method 

trained on the whole 

power-performance 

data set 

IS THERE A “BEST” METHOD?

All of the methods have advantages and disadvantages: 

 The method of binning is well-known and is today’s 

standard approach. However, it cannot account for 

the effect of turbulence on performance. 

 The turbulence renormalization method is physicsbased and can predict performance outside the range 

of previously seen conditions. However, this model 

overestimates the effect of turbulence. 

 Machine learning tools can be used with very large 

data sets and account for many variables at once; 

however, they do not work well when new conditions 

are very different than the training data set. 

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ns 

s

3. An example of

of one of the 100 

es that form the 

est model of the 

e’s performance

Figure 7. Comparison of actual power with predicted power for a single data 

point. The rest of the data set is used to train the performance model.

Figure 2. In a power 

performance test, an inflow 

meteorological tower is used to 

quantify the wind speed, shear, 

and turbulence that the wind 

turbine experiences. 

Photo by Andrew Clifton, NREL 

More research is required to develop models that 

combine the flexibility of physics-based modeling with the 

ability of machine learning to analyze large data sets.