### Abstract

The state of the art power performance measurement

method refers to the IEC61400-12-1 standard from 2005

[1]. A method for faster power curves was proposed by

researchers at Oldenburg university in 2004. The method

was called Langevin power curve method and advantages

was claimed to be that power curves could be made faster

with 1Hz dataset. In the FastWind project the Langevin

power curve method was used on real power curve

measurement datasets with the purpose to evaluate the

method for practical use.

A practical guide to application of the method to real power

curve measurement data was made. The study showed

that the method has a range of parameter settings that the

user must consider. Additionally to the wind speed binning

power binning is needed but power binning size is not

specified. Determination of drift in each bin is described

with a general formula but in practice several additional

tools have been developed by authors to try to make the

drift field and fixed point determination more robust.

A sensitivity analysis with nacelle lidar data showed drift

determination was not very dependent on the time steps

applied, leading to use of time steps of 2-3 points for each

dataset. Power bin size should be fixed. Data averaging

with 5 sec data was more distinct for determination of the

fixed points than 2 and 1 sec data. With the nacelle lidar the

Langevin method seemed to produce a power curve that

was comparable to the IEC power curve.

Analysis of the Langevin method with spinner anemometer

data showed that fixed points were very sensitive to bin

size and to requirement of minimum amount of data in each

bin. The Langevin method failed to produce acceptable

robust power curves comparable to the IEC power curve.

Simple binned averaging of data with shorter time averages gave

better results than the Langevin power curve method.

method refers to the IEC61400-12-1 standard from 2005

[1]. A method for faster power curves was proposed by

researchers at Oldenburg university in 2004. The method

was called Langevin power curve method and advantages

was claimed to be that power curves could be made faster

with 1Hz dataset. In the FastWind project the Langevin

power curve method was used on real power curve

measurement datasets with the purpose to evaluate the

method for practical use.

A practical guide to application of the method to real power

curve measurement data was made. The study showed

that the method has a range of parameter settings that the

user must consider. Additionally to the wind speed binning

power binning is needed but power binning size is not

specified. Determination of drift in each bin is described

with a general formula but in practice several additional

tools have been developed by authors to try to make the

drift field and fixed point determination more robust.

A sensitivity analysis with nacelle lidar data showed drift

determination was not very dependent on the time steps

applied, leading to use of time steps of 2-3 points for each

dataset. Power bin size should be fixed. Data averaging

with 5 sec data was more distinct for determination of the

fixed points than 2 and 1 sec data. With the nacelle lidar the

Langevin method seemed to produce a power curve that

was comparable to the IEC power curve.

Analysis of the Langevin method with spinner anemometer

data showed that fixed points were very sensitive to bin

size and to requirement of minimum amount of data in each

bin. The Langevin method failed to produce acceptable

robust power curves comparable to the IEC power curve.

Simple binned averaging of data with shorter time averages gave

better results than the Langevin power curve method.

Original language | English |
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Publisher | DTU Wind Energy |
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Number of pages | 91 |

ISBN (Print) | 978-87-93278-28-8 |

Publication status | Published - 2016 |

Series | DTU Wind Energy E |
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Volume | 0082 |

### Bibliographical note

Projektrapport på EUDP projekt FastWind### Cite this

Friis Pedersen, T., Wagner, R., & Demurtas, G. (2016).

*Wind Turbine Performance Measurements by Means of Dynamic Data Analysis*. DTU Wind Energy. DTU Wind Energy E, Vol.. 0082