on the impact of non-gaussian wind statistics on wind ... · wind turbines - an experimental...

© ForWind

On the impact of non-Gaussian wind statistics on wind turbines - an experimental approach

Jannik Schottler, N. Reinke, A. Hölling, J. Peinke, M. Hölling

ForWind, Center for Wind Energy ResearchUniversity of Oldenburg, Germany

[email protected]

1

mailto:[email protected]

mailto:[email protected]

© ForWind

Motivation

2

source: youtube.com

© ForWind

Motivation

2

source: youtube.com

• wind turbines are subjected to atmospheric turbulence!

• potential impact on...

• ...power output: grid fluctuations

• ...torque: drive train failure

• ...loads: lifetime

[Carrasco et al., 2006; Sørensen et al., 2007]

[Musial et al., 2007; Feng et al., 2013]

[ Burton et al., 2001]

© ForWind

Motivation

2

$€ - cost of energy

source: youtube.com

• wind turbines are subjected to atmospheric turbulence!

• potential impact on...

• ...power output: grid fluctuations

• ...torque: drive train failure

• ...loads: lifetime

[Carrasco et al., 2006; Sørensen et al., 2007]

[Musial et al., 2007; Feng et al., 2013]

[ Burton et al., 2001]

© ForWind

Motivation

3

Field measurements

• expensive• limited availability• uncontrolled boundary conditions

© ForWind

Motivation

3

Field measurements


Numerics

• turbulence models• computational costs• validation?

© ForWind

Motivation

3

Field measurements


• inexpensive• controlled environment• tunable boundary conditions• upscaling?

ExperimentsNumerics


© ForWind

Motivation

3

Field measurements


• inexpensive• controlled environment• tunable boundary conditions• upscaling?

ExperimentsNumerics


validation

© ForWind

Describing turbulence

4

• industry standard for wind field description:

10 min mean values, turbulence intensity TI =�u/hui

© ForWind


4

0 2 4 6 8 104

6

8

10

12

14

time [min]

velo

city

[m/s

]

T i = 19.7%



© ForWind


4

0 2 4 6 8 104

6

8

10

12

14

time [min]

velo

city

[m/s

]

T i = 19.7%



0 2 4 6 8 104

6

8

10

12

14

time [min]

velo

city

[m/s

]

T i = 19.7%

© ForWind

Increment statistics

5

time series

© ForWind


5

⌧

time series

© ForWind


5

u⌧ := u(t+ ⌧)� u(t)velocity increment

⌧

time series

© ForWind


5

t [s]0 0.5 1 1.5 2

uτ[m

s−1]

-0.3

-0.2

-0.1

0

0.1

0.2

time series of increments


⌧

time series

© ForWind

increment PDF

uτ/στ

-5 -2.5 0 2.5 5

P(u

τ)(a.u.)

10-4

10-2

100


5

t [s]0 0.5 1 1.5 2

uτ[m

s−1]

-0.3

-0.2

-0.1

0

0.1

0.2

time series of increments


⌧

time series

© ForWind

Industry standards

turbulence: Mann model (1998) / Kaimal model (1972)

6

IEC 61400-1-ED3, 2005 wind turbines, design requirements

© ForWind

Industry standards


6


uτ/στ

-5 -2.5 0 2.5 5

P(u

τ)(a.u.)

10-4

10-2

100

τ = 3 s

Gauss

Increment PDF according to IEC-Norm (TurbSim, Kaimal model)

© ForWind

Industry standards


7


[Wächter et al. 2012]

⌧ = 3 s • offshore wind data

• non-Gaussian, intermittent increments

• underestimation of extreme events

© ForWind

Industry standards


7


[Wächter et al. 2012]

⌧ = 3 s • offshore wind data

• non-Gaussian, intermittent increments

• underestimation of extreme events

once a year every 5 minutes!

© ForWind

Field data vs model

8

• datasets nearly equal acc. to mean + TI

© ForWind

uτ/στ

-15 -10 -5 0 5 10 15

p(uτ)(a.u.)

10-6

10-4

10-2

100

102

104

106

108

1010

1012

FINOKaimalGauss

1s

5s

10s

30s

60s

Field data vs model

8

• strongly different regarding increment PDF

• intermittency not reflected correctly by Kaimal model


© ForWind

uτ/στ

-15 -10 -5 0 5 10 15

p(uτ)(a.u.)

10-6

10-4

10-2

100

102

104

106

108

1010

1012

FINOKaimalGauss

1s

5s

10s

30s

60s

Field data vs model

8

• strongly different regarding increment PDF

• intermittency not reflected correctly by Kaimal model

Impact on wind turbines?


© ForWind

Setup

9

[Schottler et al., 2017]

© ForWind

Turbulence generation

10

© ForWind


10

0.8m

1 m

© ForWind


10

0.8m

1 m• 16 axes w/ stepper motors

• individually tunable

• defined, turbulent flows

• reproducible:

• time series

• statistics

© ForWind

Setup

11

• model wind turbine

• D=58cm

• active load control

• hot wire measurements upstream of rotor

• TSR = 7

• turbine data:

• thrust (load cell)

• torque (generator current)

• power (electric)

© ForWind

Setup

11

0.8m

1 m• model wind turbine

• D=58cm

• active load control

• hot wire measurements upstream of rotor

• TSR = 7

• turbine data:

• thrust (load cell)

• torque (generator current)

• power (electric)

© ForWind

Main idea

12

Inflow A) Inflow B)

© ForWind

Main idea

12

Inflow A) Inflow B)

equal according to mean+ TI

© ForWind

Main idea

12

Inflow A) Inflow B)


intermittent flowGaussian increments

Does the turbine ,see‘ the difference?

© ForWind

Inflow

14

uτ/στ

-10 -5 0 5 10

p(uτ)(a.u.)

10-10

10-5

100 Gauss

A (Gaussian)B (intermittent)

25ms

67ms

80ms (~rotor diameter)

2s

© ForWind

Inflow

14

uτ/στ

-10 -5 0 5 10

p(uτ)(a.u.)

10-10

10-5

100 Gauss

A (Gaussian)B (intermittent)

25ms

67ms


2s

• discrepancy between Gaussian assumption and intermittency reproduced in the lab!

• effect of properties beyond mean + TI (intermittency) isolated

© ForWind


15


67ms


2s

!Gaussian inflow

Gaussian thrust

!intermittent inflow

intermittent thrust

no ‘filtering’ of intermittency by the turbine

© ForWind

Turbine reaction - all quantities

16

xτ / στ

-15 -10 -5 0 5 10 15

p(x

τ)/[a.u.]

100

105

1010

Gaussinflowthrustpowertorque

25ms

67ms


2s

© ForWind

Turbine reaction - all quantities

16

Intermittent characteristics remain present in turbine data !

xτ / στ

-15 -10 -5 0 5 10 15

p(x

τ)/[a.u.]

100

105

1010

Gaussinflowthrustpowertorque

25ms

67ms


2s

on the impact of non-gaussian wind statistics on wind ... · wind turbines - an experimental...

Documents