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Influence of wind shear and seasonality on the power curve and annual energy production of wind turbines

Estíbaliz Montes, Alberto Arnedo, Ruth Cordón, Rafael ZubiaurBarlovento Recursos Naturales S.L.Hall 3, Stand #3748

Logroño, Spainbrn@barlovento-recursos.com

European Wind Energy Conference and Exhibition 2009CS3 Resource assessment and siting

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• Introduction• Influence of wind shear on AEP• Use of lidar for wind profile assessment• Results• Conclusion

OUTLINE

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• Power curve function not only of horizontal wind speed P(ws).

• Other parameters influence the performance:– Wind shear, turbulence, …

• Some of these parameters depend on atmospheric stability.• The influence of wind shear and seasonality on power curve

has been investigated.• Experimental works: Power curve tests at different sites

have been carried out, including wind shear assessment.• The influence of wind shear and seasonality on AEP results

is assessed.• Lidar has been evaluated as complementary equipment for

power curve tests.

INTRODUCTION

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SITE 2. WIND SHEAR

0

0.05

0.1

0.15

0.2

0.25

Oct'

07

Nov'07

Dec'07

Jan'0

8

Feb'08

Mar

'08

Apr'0

8

May

'08

Jun'0

8

Jul'0

8

Aug'08

Sep'08

Seasonality of wind shear is well known

SITE 3. WIND SHEAR

0.125

0.13

0.135

0.14

0.145

0.15

0.155

0.16

0.165

0.17

Spring Summer Autumn Winter

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EXPERIMENT

• Site 1. Flat terrain, near sea level, South of Spain.• Site 2. Complex terrain, about 1000 m a.s.l., Centre of Spain.• Site 3. Near flat terrain, about 300 m a.s.l. North of Spain.

Power curve tests at different sites:

Turbine power: < 1MW, 1-2 MW, > 2MWTests according IEC 61400-12-1.Additional equipments for the assessment of wind shear.Site 3 includes also lidar measurements.

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EXPERIMENT

• Category 1. α < 0.12• Category 2. 0.12 ≤ α < 0.17• Category 3. 0.17 ≤ α

Datasets have been split into subsets:

Power curves and AEP have been calculated for eachSite, dataset and subset.

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SITE 3. STANDARD DENSITY

0

20

40

60

80

100

120

0 2 4 6 8 10 12 14 16HUB HEIGHT WIND SPEED

(m/s)

NO

RM

AL

IZE

D P

OW

ER

(%

)

α < 0.120.12 < α < 1.17α > 0.17

POWER vs WIN SPEED

20%

30%

40%

50%

60%

7.5 7.6 7.7 7.8 7.9 8.0 8.1 8.2 8.3 8.4 8.5

WIND SPEED

PO

WE

R %

OF

RA

TE

D P

OW

ER

)

Small wind shear

High wind shear

Lineal (Small windshear)Lineal (High windshear)

AEP SITE 3

92

94

96

98

100

102

104

106

4 5 6 7 8 9 10 11

AVG. WIND SPEED (m/s)

% A

EP

To

tal

α < 0.12

0.12 < α < 0.17

α > 0.17

SITE 3. AEP

98.0

98.5

99.0

99.5

100.0

100.5

101.0

101.5

102.0

102.5

103.0

4 5 6 7 8 9 10 11

WS (m/s)

%A

EP

To

tal

Winter

Autumn

Summer

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Similar AEP differences for three sites.Influence of wind shear on power curve is similar.

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EXPERIMENT

Results:

• Better power performance for lower α: Category 1.• Similar differences in AEP for three sites.• Similar results for different turbine sizes.• Wind Shear not the only cause of seasonal AEP differences.

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LIDAR MEASUREMENTS

• Validation of measurements (wind speeds, wind profile).• Filtering criteria development.• Assessment of results.

The measurement plan at Site 3 includes:

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LIDAR MEASUREMENTS

• Many uncorrelated data => lidar filtering necessary.

Validation of measurements:

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LIDAR MEASUREMENTS

• Cloudiness. • Rain.• Data availability at all levels.

Filtering criteria:

DATA RESTRICTION Nº 10’ DATA

Total measurements valid sector

3226 100%

Measurements (data availability >75%)

2540 78.7%

Measurements (data availability >75%, low cloudiness, not rain)

1893 58.7%

Measurements (low cloudiness, not rain, data availability all levels>75%)

1741 54.0%

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LIDAR MEASUREMENTS

• High correlation of wind speeds (lidar-mast).• Slope aprox. 1• Same wind shear results,

But• Low data availability.

Assessment of results:

VMAST,TH = 1.002 VLIDAR,TH

R2=0.94

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CONCLUSIONS

• Wind shear influences the power curve, • For the same hub height average wind speed,

AEP is lower for big α values.• Wind shear variations can be found at test sites:

by sector, by season, then:• Measurements of wind shear shall be included in the tests.

• Lidar measurements can be helpful, but lidar valid data availability need to be improved.• Site specific power curves are needed for wind resource assessment.

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CONSIDERATION

Power curve is used as a wind turbine performance characteristic. The improvements in the Power curve concept need to be consistent with the wind resource assessment practices: measurements, wind field models, wake models.