Measured wake losses By Per Nielsen

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: 1,02 1,01 1 0,99 0,98 0,97 0,96 0,95 HR-1 measured/calculated 1 2 3 4 5 6 7 8 9 10 avg 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: HR-1 measured/calculated 1,02 1,01 1 0,99 0,98 0,97 0,96 0,95 1 2 3 4 5 6 7 8 9 10 avg STD WDC 0.04 WDC+35%lin Last but 2012 WDC+35%lin-2012-incl.HR2 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.

DTU 2012-> <-EMD 2002

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. 16% 14% 12% 10% 8% 6% Measured wake loss = calculated all data + over prediction based on all approved (filtered) concurrent measured and calculated data. 4% 2% 0% -2% 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 79-4% Wake loss calc. STD Overpred. Measured

Row by row loss, 360 o STD calc. 9.4% Tuned calc. 10.5% 14,0% 12,0% 10,0% 8,0% 6,0% 4,0% Average of Wake loss calc. STD Average of Overpred. Average of Measured 14,0% 2,0% 12,0% 10,0% 0,0% -2,0% 1 2 3 4 5 6 7 8 9 10 Row number 8,0% 6,0% 4,0% 2,0% 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. 0,0% -2,0% 1 2 3 4 5 6 7 8 9 10 Row number

Directional tests, HR-1 1,15 1,1 The calculations are also tested by direction: Measured/calculated 1,15 1,1 Measured/calculated 1,05 1 0,95 0,9 0,85 1,05 Average of N Average of NNE 1 Average of ENE Average of E 0,95 Average of ESE 0,9 Average of SSE 0,85 Average of S Average of SSW Average of WSW Average of W Average of WNW Average of NNW 0,8 1 2 3 4 5 6 7 8 9 10 0,8 1 2 3 4 5 6 7 8 9 10 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, 1 2 3 4 100 Gamesa G80, 60m hub height, 7 rows All wind from NW. 5 Spacing: 6 Row: 14 RD In-row: 3 RD 7

ElZayt, Egypt 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. 1,04 1,02 1,00 0,98 Measured/ calculated Average of WAsP ORG Wake model Average of WAsP 35% lin. + WDC red (deep array Wake) Average of WasP lin 35% NO WDC red. WTG number 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 0,96 0,94 0,92 0,90 Row number 1 2 3 4 5 6 7 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%

ElZayt, measured wake loss 25,0% 20,0% 15,0% 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. 10,0% 5,0% Average of Calc. wake loss Average of Overpred. Average of Measured The reason for the drop in wake losses for the two back rows is the wind farm configuration. 0,0% 1 2 3 4 5 6 7-5,0%

+ 2 years 10 min data 4 degree avg.: Thin line: Calculated, Thick line: Measured only concurrent data 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) 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) Much data from SE Few data from NW

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!