Measured wake losses

By Per Nielsen

Wake losses

Cannot be measured directly, but by setting up

a calculation model and comparing to

measurements, with proper data filtering, the

wake losses can be identified quite precisely.

Here I look both at many arrays (deep array

losses), and a simple 1 row windfarm.

The windPRO Performance Check tool is used

with the time step PARK calculation.

Horns Rev 1, offshore The one everybody knows. 7 RD spacing,

10 x 8 rows, V80 2MW, 70m hub height.

N.O.Jensen model – with modifications

First tuning; adding linear combination of wind

speed deficit, this helps, but over compensate

especially in row 3-5 from main wind direction.

WDC decrease by number of upwind turbines

regulates this:

0,95

0,96

0,97

0,98

0,99

1

1,01

1,02

1 2 3 4 5 6 7 8 9 10 avg

HR-1 measured/calculated

STD WDC 0.04 Lin 25% WDC+25% lin

Calculation is based on 2008 EMDConWx meso scale data and 10-min. scada production data for each turbine, filtered for downtime. <- here row by row, 1 is west most.

N.O.Jensen model – with modifications

More adjustments tested, best fit is 35%

linear weight in combination model + WDC

decrease by number of up wind turbines by

this formula:

Y is the factor multiplied on WDC, x the number of up-wind turbines. <- Back rows lifted 1.5% on totals. Test of 2012 data in addition to 2008. Including HR2 wind farm in 2012 calculation makes this perform almost exactly as the 2008 calculations.

0,95

0,96

0,97

0,98

0,99

1

1,01

1,02

1 2 3 4 5 6 7 8 9 10 avg

HR-1 measured/calculated

STD WDC 0.04 WDC+35%lin

Last but 2012 WDC+35%lin-2012-incl.HR2

<-EMD 2002

DTU 2012->

Measured wake loss HR-1

Based on the STD calc. WDC 0.04 and the

overprediction, the measured wake loss is 10.25%;

9% higher than STD calc. - but only 1% on AEP.

-4%

-2%

0%

2%

4%

6%

8%

10%

12%

14%

16%

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75 77 79

Wake loss calc. STD Overpred. Measured

Measured wake loss = calculated all data + over prediction based on all approved (filtered) concurrent measured and calculated data.

Row by row loss, 360o

STD calc. 9.4%

Tuned calc. 10.5%

-2,0%

0,0%

2,0%

4,0%

6,0%

8,0%

10,0%

12,0%

14,0%

1 2 3 4 5 6 7 8 9 10

Average of Wake loss calc.STD

Average of Overpred.

Average of Measured

-2,0%

0,0%

2,0%

4,0%

6,0%

8,0%

10,0%

12,0%

14,0%

1 2 3 4 5 6 7 8 9 10

Average of Wake loss calc.Corr

Average of Overpred.

Average of Measured

Not a big difference, but the tuning gives more confidence and is easy to use – and tested on several other large arrays.

Row number

Row number

Directional tests, HR-1

The calculations are also tested by direction:

0,8

0,85

0,9

0,95

1

1,05

1,1

1,15

1 2 3 4 5 6 7 8 9 10

Measured/calculated

Average of N

Average of NNE

Average of ENE

Average of E

Average of ESE

Average of SSE

0,8

0,85

0,9

0,95

1

1,05

1,1

1,15

1 2 3 4 5 6 7 8 9 10

Measured/calculated

Average of S

Average of SSW

Average of WSW

Average of W

Average of WNW

Average of NNW

Note the bias is probably related to meso scale data inaccuracies – what is important here is that the lines are horizontal, meaning wake loss calculation is handled well in all directional sectors.

ElZayt, Egypt, desert

200MW,

100 Gamesa

G80, 60m hub

height, 7 rows

All wind from

NW.

Spacing:

Row: 14 RD

In-row: 3 RD

1 2

3

4

5

6

7

ElZayt, Egypt

Red line: Same tuning as HR-1, but “base” WDC 0.052 make all rows within +/- 2%. Within few months much better data will be available

Short operation period, still not in “full” operation, therefore data quality not perfect yet. But the trends appear quite clear. Over prediction of back rows due to wake issues.

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0,92

0,94

0,96

0,98

1,00

1,02

1,04

1 2 3 4 5 6 7

Average of WAsP ORG Wakemodel

Average of WAsP 35% lin. +WDC red (deep array Wake)

Average of WasP lin 35% NOWDC red.

Row number

Measured/calculated

ROW perf: STD WDC 0.052

Tuned 35% lin. + WDC red (deep array Wake)

Tuned 35% lin. NO WDC red.

Stdev: 3,8% 1,5% 2,1%

Max - min 9,1% 4,0% 6,0%

WTG number

ElZayt, ”measured” wake loss

Based on same approach as for HR-1, the

measured wake loss is 11.3%, std. calc.: 8.4%

We see an increase in wake loss by row up to ~20% for 5’th row. The short operation period and that the windfarm still are in the start up phase makes these calculations uncertain. The reason for the drop in wake losses for the two back rows is the wind farm configuration.

-5,0%

0,0%

5,0%

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15,0%

20,0%

25,0%

1 2 3 4 5 6 7

Average of Calc. wake loss

Average of Overpred.

Average of Measured

Simple 1 row project, KE We are part owners and have extremely good data – more

than 2 years 10-min data and high quality operation. ( 4 x

V112, 94m hub height (northern 84m but on a 10m hill)

+ 2 years 10 min data 4 degree avg.: Thin line: Calculated, Thick line: Measured – only concurrent data

3 RD spacing

Krogstrup Enge (KE) WTG1 and WTG4 wind in combination used as input for

calculations. Fine-tuning of the directional calibration is a

very important part of the calculation setup!

Looking at the ratios measured/calculated we get the “hands down”, up to 30% errors in centre wake angle and a very clear picture: First wake turbine is over predicted Second under predicted Third more under predicted Can this be solved by tuning wake model parameters? Now you probably expect a YES, but it is a NO – our tunings so far increases calculated wake losses for back rows, here we need the opposite.

Tuning wake model for a single row We have of course tried: First the STD, fixed WDC 0.075 First wake turbine (1 in front): Last wake turbine: (3 in front)

Few data from NW

Much data from SE

Tuning wake model for a single row Let the turbulence control the WDC – look better, but do not

solve the identified problem First wake turbine (1 in front): Last wake turbine: (3 in front)

Tuning wake model for a single row Varying the direction in calculation within each time step –

helps a little, but cannot fully solve the problem First wake turbine (1 in front): Last wake turbine: (3 in front)

Conclusions • The only real solver when based on N.O. Jensen model, will be

a combination model that adds the deficits with a root sum square where the exponent is higher than 2.

• A bell shaped single wake model would make the reproduction of the measurements more precise, but not solve the issue that the back turbines in a single row are under predicted (wake loss predicted too high) relative to the turbines with fewer up wind turbines.

• Remaining problem: How do we construct a combination model that ”punish” multiple rows harder and single rows less?

• When this is said, it must also be added that we with present N.O. Jensen model and the presented tunings are calculating VERY close to measurements on 360 degree basis – and more precise than all other tested wake models.

Practical useable experience 1. WDC controlled by turbulence – solves hub height problem

2. Turbulence correct power curve – works, but have no real impact

on AEP (maybe if the site has extreme turbulence (high or low))

3. Use the new “deep array” tuning options in windPRO, it works!