the w ind

Óbuda UniversityPower System Department

The wind

Dr. Péter KádárÓbuda University, Power System Department, Hungary

[email protected]


Wind basics - Patra, 2012

Draft

• Wind basics• Drivers of the wind energy application• The energy of the wind• Dynamic simulation• Wind forecast

2


The wind… … forms the surface

Wind basics - Patra, 2012 3


The wind… … blows our hair



The wind… … brakes the signes



The wind… … moves the sailboats



The wind… … destroys the forests



The wind… … forwards the snow



The wind… … blows the flag



The wind… … lifts our kite



The wind… … dries our cloths



And the wind… …bends the trees



And the wind… …turns our propeller



…but nobody can see it!




Windrose

15



Windrose

16



Wind turbine and measurement system

anemometer

1 nap szélirány-időtartamai (perc)

0

100

200

300ÉSZAK

ÉÉKÉK

KÉK

KELET

KDK

DK

DDKDÉL

DDNYDNY

NYDNY

NYUGAT

NYÉNY

ÉNY

ÉÉNY

Eloszlás sűrűségfüggvény

02468

10121416

0 0,5 1 1,5 2 2,5 3 3,5 4 4,5 5 5,5 6

szélsebesség m/s

Mér

ések

dar

absz

áma


Measurements



Simple windrose


1 nap szélirány-időtartamai (perc)

0

100

200

300ÉSZAK

ÉÉKÉK

KÉK

KELET

KDK

DK

DDKDÉL

DDNYDNY

NYDNY

NYUGAT

NYÉNY

ÉNY

ÉÉNY


Speed-Weighted windrose

• Direction?• Average speed?• Energy?


0

50

100

150

200ÉSZAK

ÉÉK

ÉK

KÉK

KELET

KDK

DK

DDKDÉL

DDNY

DNY

NYDNY

NYUGAT

NYÉNY

ÉNY

ÉÉNY

Átlagos szélsebesség súlyozva 2006.09.07.



Main winter directions

21



Main yearly directions

22



Wind (speed) map for 10 m heights



Wind (speed) map for 75 m height

24



Daily wind course in diff. heights

A szélsebesség átlagos napi menete különböző magasságokbanSzeged, SODAR

0

1

2

3

4

5

6

7

8

9

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

óra

m/s

30 m 45 m 60 m 75 m 90 m 105 m 120 m 135 m 150 m

25



Upscaling

• Measurements or calculations on different heights• Upscaling – continuous formula to define the windspeed in

other heights• e.g. Hellmann equation

26



Local wind profile

20 0

17 5

22 5

25 0

27 5

30 0

32 5

35 0

37 5

5 10 15 20

T á vo lság [k m ]

Teng

ersz

int f

elet

ti m

agas

ság

[m]

N Y

H eg yh átsá l

20 0

22 5

25 0

27 5

30 0

35 0

37 5

32 5

Teng

ersz

int f

elet

ti m

agas

ság

[m]

3 m s-1

3 m s-1

4 m s-1

4 m s-1

5 m s-1

5 m s-1

6 m s-1

6 m s-1

É

H eg yh átsá l



Global windforecast services


www.met.hu



On-line: http://www.idokep.hu



Historical data(http://www.met.hu/megfigyelesek/index.php?v=Budapest)




Global models

• Supercomputing• 27 km -> 2,5 km cubes• Differential equations

system

But• Different measurement points• Different application points

Forecast

Application

measurement

Terrestrial forms



Professional services

• Numerical weather forecasts• Horizontal and vertical interpolation• Wind Atlas Analysis and Application Program + PARK

modell • Statistical elements• Meteorological models (e.g. ALADIN, MEANDER), other

sources (ECMWF, MM5, HIRLAM, stb.)• Result presentation by heights or by isobar?



Statistical approach

34


Weibull distribution


http://en.wikipedia.org/wiki/File:Weibull_PDF.svg



Local direction changes



Local speed changes

Speed changes + direction changes = turbulence


Turbulencies


A szélirány és -nagyság percenkénti változása (turbulencia)

-3,00

-2,00

-1,00

0,00

1,00

2,00

3,00

4,00

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58

perc

m/s

és

rad

Szélsebesség változás (m/s)

Szélirány változás (rad)


Local wind speed tower measurements in a wind park, during 4 min, in the range 6-10 m/s

(data from: Mov-R H1 Szélerőmű Kft., Hungary)



Local wind speed tower measurements in a wind park, during 4 min, in the range 3-6 m/s




Local wind speed tower measurements in a wind park, during 4 min, over 10 m/s




Spread over of the wind energy application




Fig.Global cumulative installed wind capacity 1996-2010 Global Wind Energy Council 2010 (GWEC)

MW 43



Windpower capacity in Europe, 2006, MW

Forrás: www.ewea.org

0

10 000

20 000

30 000

40 000

50 000

60 000

70 000

80 000

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

MW

44


Wind energy application in Europe

45

EWEA, 2010



Yearly built in wind capacities in Europe

46

EWEA, 2010

20099581MW onshore582MW offshore



Repowering

47Wind basics - Patra, 2012


Some drivers of the windenergy business

• Growing demand for electricity• EU directives• Subventions• Sustainability• Reduction of CO2 emisson• Green investment boom (ROI 4-5 years)• Employment, etc.



Catching the wind energy



Simple energy modells


1. Tube modelv - speed, V - volume do not changesP – pressure, T – temperature decreeases

Reality: v, V, P, T - changes

50

2. Deccelerating massv - speed, V - volume decreasesP – pressure, T – temperature do not changes



Power of the wind (moving mass model)

P = 0,5 ρ A v3 ηwhere • P = mechanical (~electrical) power of the wind turbine, • ρ = 1,29 kg/Nm3 – density of the air, • A = r2 π = d2 π / 4 area swept by the rotor blades (r is the

length of the blade, d = 2 r diameter of the rotor), • v = wind speed, • η = efficiency of the rotor (theoretical max. is 60 %,

practically 10-30 % ).



Obstacle and the flowing air

• a - turbulent• b - laminal• c – turbulent (stall)

52



The possible energy conversion

• Fix bladed rotor• TipSpeedRatio: v blade edge / v air

• 100 %? no



Different rotors and blades

• P vs wind speed• Different rpm

54



Practical characteristics of wind turbine with control

Electronic control of• Rotor speed• Pitch



Factory characteristics


Typical characteristics


Szélturbina karakterisztikák

-500

0

500

1 000

1 500

2 000

2 500

0 5 10 15 20 25 30

Szélsebesség (m/s)

Kim

enő

telje

sítm

ény

(kW

)

V-90-2MWMM82G90V-90-1.8MWE-48MD77E-70



Performance measurements


Dynamic simulation



Simulation logic

• Basic questions: „What happened if…”• No yearly averages but• Real wind measurements + • Defined wind park locations + • Wind turbine characteristics• Result: MW curve during a long period (a year)



Investigated places, parks



Frequency of the output changes


Szélfarmok összesített kimenő teljesítmény-változásának gyakorisága (10 perces)

0,00%

5,00%

10,00%

15,00%

20,00%

25,00%

30,00%

170 0

00

150 0

00

130 0

00

110 0

00

90 00

0

70 00

0

50 00

0

30 00

0

10 00

0 0

-20 00

0

-40 00

0

-60 00

0

-80 00

0

-100 0

00

-120 0

00

-140 0

00

-160 0

00

Teljesítmény változás 10 percenként

Gya

koris

ág


Average wind speed vs monthly production


Havi szélsebesség átlag, és havi megtermelt energia kapcsolata

0,00

5000,00

10000,00

15000,00

20000,00

25000,00

30000,00

1,50 2,50 3,50 4,50 5,50 6,50 7,50

m/s

MW

h

AgárdFolyásGyőrMosonTúrkeve


Daily energy production vs built in capacity


A napi átlagteljesítmény gyakorisága a beépített teljesítményre vonatkoztatva

0,00%

5,00%

10,00%

15,00%

20,00%

25,00%84

%

80%

76%

72%

68%

64%

60%

56%

52%

48%

44%

40%

36%

32%

28%

24%

20%

16%

12% 8% 4% 0%

Beépített teljesítmény %-a

Gya

koris

ág


Monthly energy production


Havonta megtermelt energia

0

20 000

40 000

60 000

80 000

100 000

120 000

Janu

ár

Febru

ár

Március

Április

Május

Júniu

sJú

lius

Augus

ztus

Szeptember

Októbe

r

Novembe

r

Decembe

r

Hónap

MW

h


Small wind


Kimenő teljesítmény a minimális energiatermelésű napon - 2005.11.02 (és előtte/utána 1 nappal)

0

20000

40000

60000

80000

100000

120000

140000

160000

180000

0:00

:00

1:40

:00

3:20

:00

5:00

:00

6:40

:00

8:20

:00

10:0

0:00

11:4

0:00

13:2

0:00

15:0

0:00

16:4

0:00

18:2

0:00

20:0

0:00

21:4

0:00

23:2

0:00

1:00

:00

2:40

:00

4:20

:00

6:00

:00

7:40

:00

9:20

:00

11:0

0:00

12:4

0:00

14:2

0:00

16:0

0:00

17:4

0:00

19:2

0:00

21:0

0:00

22:4

0:00

0:20

:00

2:00

:00

3:40

:00

5:20

:00

7:00

:00

8:40

:00

10:2

0:00

12:0

0:00

13:4

0:00

15:2

0:00

17:0

0:00

18:4

0:00

20:2

0:00

22:0

0:00

23:4

0:00

Idő

kW

Túrkeve

Moson

Győr

Folyás

Agárd


Large wind


Kimenő teljesítmény a maximális energiatermelésű (és átlagteljesítményű) napon - 2005.12.30(és előtte/utána 1 nappal)

0

100000

200000

300000

400000

500000

600000

0:00

:00

1:40

:00

3:20

:00

5:00

:00

6:40

:00

8:20

:00

10:0

0:00

11:4

0:00

13:2

0:00

15:0

0:00

16:4

0:00

18:2

0:00

20:0

0:00

21:4

0:00

23:2

0:00

1:00

:00

2:40

:00

4:20

:00

6:00

:00

7:40

:00

9:20

:00

11:0

0:00

12:4

0:00

14:2

0:00

16:0

0:00

17:4

0:00

19:2

0:00

21:0

0:00

22:4

0:00

0:20

:00

2:00

:00

3:40

:00

5:20

:00

7:00

:00

8:40

:00

10:2

0:00

12:0

0:00

13:4

0:00

15:2

0:00

17:0

0:00

18:4

0:00

20:2

0:00

22:0

0:00

23:4

0:00

Idő

kW

Túrkeve

Moson

Győr

Folyás

Agárd


Meteorological front in and out


Kimenő teljesítmény a front megérkezésekor

0

50000

100000

150000

200000

250000

300000

350000

400000

450000

Idő

kW

Túrkeve

Moson

GyőrFolyás

Agárd

Kimenő teljesítmény a front elvonulásakor

0

50000

100000

150000

200000

250000

300000

350000

400000

450000

Idő

kW

TúrkeveMoson

Győr

FolyásAgárd


The „problematic” days


Kimenő teljesítmény egy átlagos energiatermelésű napon - 2005.05.17 (és előtte/utána 1 nappal)

0

50000

100000

150000

200000

250000

300000

350000

400000

450000

0:00

:00

1:40

:00

3:20

:00

5:00

:00

6:40

:00

8:20

:00

10:0

0:00

11:4

0:00

13:2

0:00

15:0

0:00

16:4

0:00

18:2

0:00

20:0

0:00

21:4

0:00

23:2

0:00

1:00

:00

2:40

:00

4:20

:00

6:00

:00

7:40

:00

9:20

:00

11:0

0:00

12:4

0:00

14:2

0:00

16:0

0:00

17:4

0:00

19:2

0:00

21:0

0:00

22:4

0:00

0:20

:00

2:00

:00

3:40

:00

5:20

:00

7:00

:00

8:40

:00

10:2

0:00

12:0

0:00

13:4

0:00

15:2

0:00

17:0

0:00

18:4

0:00

20:2

0:00

22:0

0:00

23:4

0:00

Idő

kW

Túrkeve

Moson

Győr

Folyás

Agárd


Correlation analysis of wind measurements




The problem

• Wind production forecast = wind forecast + turbine caharactereistics

• There is no exact wind forecast for the wind turbine sites• The forecast is crucial for the integration large wind parks

into the network• The question: Can we forecast the generated energy based

on remote wind forecast?

71



The real task

• Not in that place• Not in that time• Not correct wind forecast

Forecast

Application

measurement

Terrestrial forms



The factory characteristics

• In the project we investigate a V-27 turbine at “Bükkaranyos” and wind measurement ant “Folyás” meteorological station.

• The figure shows a typical characteristics of wind turbines (V27). This curve is measured in stationary mode, it does not contain the effect of local turbulences, direction changes and wind speed differences between the upper and lower part of the (spinning) rotor measurements.

73



Characteristics based on pure measurements

?

74



Correlation?

The causes of the lack of correlation are• The distance between the wind turbine and wind

measurement• The local wind turbulences that create difference

in the wind blow at the two measurement points.

75



Other causes: turbulence

• The local wind turbulences that create difference in the wind blow at the two measurement points.

• fast (1-6 sec), the medium (1-6 min) and the slow (1-6 hour) changes.

• The fast and medium wind speed and direction changes are not handled (followed) by the turbine, it causes deviances.

• Turbine dynamics and measurement errors, etc.– Wind speed changes– Wind direction changes– Wind speed changes measurement on minute scale

76



Distributional reorganization: a functional transformation

An ideal wind speed and power output measurement at the same tower should give the factory characteristics of the wind turbine, the two measurements correlate on the factory curve. If we prepare the cumulative distribution function of both measurements, the previous correlation is still valid and we get the same curve.

77



Back to the measurements (Bükkaranyos – Folyás: 33km)

78



Characteristics matching

Based on the above mentioned, the locally differently running curve is substituted by a globally similarly cumulated distribution function. We investigate not the specific synchronized moments but the same period, so we integrate the power into generated energy. This is an energy based characteristics retrieval. Figure shows characteristics similar to the factory characteristics (marked by dots).

79



Meaurement distances

Name of wind measurement place

Distance of the wind turbine “Bükkaranyos”

Folyás 33 km

Agárd 187 km

Túrkeve 98 km

Mosonmagyaróvár 263 km

Győr 238 km

80



Wind power generation forecast

See WinDemo!

81


Wind power generation forecast

Rough:• Windspeed• CharactereisticFine• + temperature• + pressure• + humidity• + direction

CORRELATION FACTOR!




Upscaling

• The previously shown remote upscaling factor is defined by the energy production of a time period. Applying the Hellmann equation (1) for the same tower (height 33 m, measurements height 10 m), the exponent is 0,445, that is a good experimental result.

83



Remote scaling

84



Conclusion

• It is not possible to retrieve the vendor given stationary winds speed–generation characteristics of the wind turbine based on the real-time measurements.

• The calculations above show that for real-time generation forecast purposes only close measurement/estimation points could be used.

• The wind forecasts work on worldwide global models, these are theoretically not capable of forecasting local turbulences – which cause the 0,5 - 5 min deviations in the power output.

• In spite of this fact, based on further measurements quite good energy production estimations can be done. We used the cumulative distribution function to define the ratio between remote wind speed measurement and the possible local wind speed at the turbine.

85



Thanks for the attention!

86

the w ind

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