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A NEW TURBULENCE PREDICTION METHOD FOR TURBINE SUITABILITY ANALYSISALEX CLERC, PETER STUART AND PETER DUDFIELD

5 FEBRUARY 2013

University of

Cambridge

CONTENTS

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• Motivation

• Vertical changes in turbulence

• Horizontal changes in turbulence

• Example calculation

EXAMPLE MAST AND TURBINE

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ENERGY YIELD – MAJOR COMPONENTS

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Inputs:•Measured Wind Climate(s)•Topography

Outputs:•Wind Climate at each turbine including Turbulence Intensity

ENERGY YIELD – MAJOR COMPONENTS

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Net Yield

Wind Flow Model

Turbine Model

Inputs:•Wind Speed•Air Density•Turbulence Intensity

Wind Speed (m/s)

10

-min

ute

Mean P

ow

er

(kW

)1120kW at 13m/s, 30% TI

1320kW at 13m/s,

5% TI

ENERGY YIELD – MAJOR COMPONENTS

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Net Yield

Wind Flow Model

Turbine Model

Wakes Loss

Inputs:•Turbine Details•Wind Speed•Turbulence Intensity

CAN’T WE JUST USE CFD?

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• Yes!

• But wouldn’t you like a second opinion?

• This presentation uses an industry-standard flow model very similar to WAsP (MS3DJH + empirical roughness and obstacle models)

• Running this model takes only minutes on a PC

• The accuracy of this flow model is well understood

CONTENTS

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• Motivation

• Vertical changes in turbulence

• Horizontal changes in turbulence

• Example calculation

VERTICAL CHANGES IN TI

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TIL

TIU

VERTICAL CHANGES IN TI

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• Dataset: 190 masts with at least two boom-mounted anemometers and 1 year of data.

• Mean wind speed and TI calculated for upper and lower anemometer (concurrent data only).

• Challenge is to predict TI at upper anemometer using other measurements

VERTICAL CHANGES IN TI

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• Model 1: Persistence

The upper TI is predicted to be the same as the lower TI

• Model 2: Ratio of Wind Speed (RoWS)

The upper TI is predicted to be the same as the lower TI times the lower wind speed divided by the upper wind speed.

In other words, assume the standard deviation of wind speed does not change with height.

LU TITI

U

LLU U

UTITI

VERTICAL CHANGES IN TI

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• RoWS model is better for 186 out of 190 masts

• Under-predictions of turbulence are small with both models

• A few bad over-predictions for sites in the UK (a flat site in England and a hilly forested site in Wales)

CONTENTS

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• Motivation

• Vertical changes in turbulence

• Horizontal changes in turbulence

• Example calculation

HORIZONTAL CHANGES IN TI

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TIB

TIA

HORIZONTAL CHANGES IN TI

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• Dataset: 53 direction sectors from 7 mast pairs, max distance 2km, same height above ground level

• Wind speed, shear and TI calculated by 30° sector (concurrent data only).

• Challenge is to predict TI at second mast (Mast B) using measurements at first mast (Mast A)

HORIZONTAL CHANGES IN TI: DERIVATION OF NEW MODEL

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After some manipulation:

with C = 0.1

A

z

zUzU A

00)(

B

z

zUzU B

00)(

Two locations with simple vertical profiles and the same geostrophic wind: 22

z

UkCP

Use k-epsilon and at both points, assume:

AABBAABB UCUCUTIUTI

B

AAB

B

AAB U

UCC

U

UTITI

HORIZONTAL CHANGES IN TI: MODEL VALIDATION (using measurements at Mast B)

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• Mast B measured wind speed and shear used as an input to validate the RES model.

• Accounting for wind speed variation is the main reason for RES model’s better accuracy

• Using shear gives a small additional improvement to RES model accuracy

HORIZONTAL CHANGES IN TI: MODEL VALIDATION

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• Here the RES TI model is integrated with the flow model (no measurements from Mast B)

• Decrease in RES model accuracy due to flow model prediction error

• RES model improves the TI estimate in 74% of cases and has better overall accuracy

RES Model Limitations

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• Flow separation

• Non-neutral conditions

• Extrapolations beyond a few km

• Local equilibrium is assumed (turbulent energy production balances dissipation)

CONTENTS

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• Motivation

• Vertical changes in turbulence

• Horizontal changes in turbulence

• Example calculation

EXAMPLE MAST AND TURBINE

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16% TI at turbine location

10% TI at mast

measurement

EXAMPLE CALCULATION: ENERGY YIELD

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10% TI

16% TI

• AEP of 10% curve: 9.02GWh

• AEP of 16% curve: 8.89GWh

• 1.5% loss in available energy

• Potentially more energy loss due to turbine performance in non-standard conditions?

Calculation uses method of A. Albers, “Turbulence and Shear Normalisation of Wind Turbine Power Curve”

EXAMPLE CALCULATION: SITE SUITABILITY

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Knowledge of ambient TI is essential to assessing suitability of turbine locations

10% ambient TI + wakes

16% ambient TI + wakes

CONCLUSIONS

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• Turbulence intensity can vary tremendously across a site, posing a challenge to wind farm development

• The presented TI model can give accurate and reliable predictions about the variation of turbulence

• The model is easy to understand, easy to use and very fast to compute

• The results are easy to apply to energy yield and suitability calculations

• The presented model is useful both on its own and in combination with a CFD model

ACKNOWLEDGEMENTS

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• Colleagues at the University of Cambridge:– Dr. C.P. Caulfield

• Colleagues at RES:– Dr. Mike Anderson– Maciej Drahusz– Alan Duckworth– Alice Ely– Devin Gurbuz– Michelle James

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