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Wind Characteristics Analyses andDetermination of Appropriate WindTurbine for Amasra—Black Sea Region,TurkeyS. A. Akdağ a & Ö. Güler a

a Energy Planning and Management Division, Energy Institute,Istanbul Technical University , Maslak, Istanbul, TurkeyPublished online: 19 Jul 2010.

To cite this article: S. A. Akdağ & Ö. Güler (2010) Wind Characteristics Analyses and Determinationof Appropriate Wind Turbine for Amasra—Black Sea Region, Turkey, International Journal of GreenEnergy, 7:4, 422-433, DOI: 10.1080/15435075.2010.493819

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WIND CHARACTERISTICS ANALYSES ANDDETERMINATION OF APPROPRIATE WIND TURBINE FORAMASRA—BLACK SEA REGION, TURKEY

S. A. Akdag and O. GulerEnergy Planning and Management Division, Energy Institute, Istanbul Technical

University, Maslak, Istanbul, Turkey

In this study, wind characteristics of Amasra were analyzed with hourly wind data collected

between 1997 and 2006. Wind characteristics such as monthly average mean speeds, power

densities, turbulence intensities, maximum gust, and prevailing wind directions were

identified. Weibull distribution model was used to determine energy output of thirty

commercial wind turbines ranging from 335 to 3000 kW. Estimated mean capacity factors

were calculated between 26.8% and 43.9%.

Keywords: Capacity factor; Turkey; Wind energy; Wind turbine

INTRODUCTION

Electricity has played a key role in the living quality of people, industrialization, and

development because it is the input to nearly all products. Like many developing countries,

electricity consumption of Turkey increases considerably because of industrialization

activities, rapid urbanization, increase in the living standards, population, and economic

growth. Installed electrical power capacity of Turkey was 41,802.6 MW and the electrical

energy generation was 198.32 TWh in 2008 (Energy Market Regulatory Authority 2009a).

Turkey’s energy source does not meet her energy demand, and therefore it has been

purchasing energy from foreign countries. According to the statistics of 2007, share of

indigenous primary energy source was only 25.5% (Ministry of Energy and Natural

Resources 2009). Since most part of the energy was imported, tiny increase in energy

prices strongly affects economy. It is vital for Turkey to supply reliable and sustainable

energy from her indigenous renewable energy sources such as hydro and wind.

Wind energy is one of the most promising renewable energy sources in Turkey. Wind

Energy Potential Atlas of Turkey (REPA) was prepared by using satellite data and

numerical weather prediction methodologies in 2006. According to the REPA (Wind

Energy Potential Atlas), wind energy potential at 50 m height above the ground in land

regions was calculated to be 131,756 MW, which is equivalent to the wind power density

larger than 300 W/m2 (Malkoc 2007). Therefore, wind energy can significantly contribute

to decreasing dependency of imported fuels, increasing self-sufficiency, and improving

International Journal of Green Energy, 7: 422–433, 2010

Copyright # Taylor & Francis Group, LLC

ISSN: 1543-5075 print / 1543-5083 online

DOI: 10.1080/15435075.2010.493819

Address correspondence to S. A. Akdag, Energy Institute, Istanbul Technical University, Ayazaga

Campus, 34469 Maslak, _Istanbul, Turkey. E-mail: akdagse@itu.edu.tr

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national energy security of Turkey. However, contribution of wind energy in electricity

production mix is very low, but is increasing rapidly. Turkey has twenty-two operating

wind power plants with 727.45 MW installed capacity. Capacity of wind power plants has

varied between 0.85 and 90 MW (Energy Market Regulatory Authority 2009b).

As stated by Akdag and Guler (2009), a large number of researches have been carried

out about wind energy potential, characteristics, and feasibility analysis of various regions in

Turkey. However, no detailed academic research has been carried out about wind energy

potential and characteristic of Amasra, located in the NW of Turkey (41� 440 latitude, 32� 230

longitude). Because of the lack of research about wind energy potential and characteristics of

the region, the present article attempts to determine wind characteristics and wind energy

potential of this region from hourly mean wind data between 1997 and 2006, which was

recorded by State Meteorological Works General Directorate. Weibull distribution model

was used to determine wind energy potential of the region. To determine wind turbines

capacity factors and energy output, a wide set of commercial wind turbines were used with

various hub heights. Analysis was carried out among thirty commercial wind turbines, ranged

from 335 to 3000 kW. Monthly and annual electrical energy production and capacity factors

were calculated using Weibull distribution and Wasp Turbine Editor Software.

WEIBULL DISTRIBUTION

Several distribution models have been proposed for wind energy studies. Weibull

distribution model was the most commonly used model for wind energy potential assess-

ment. Probability density function of Weibull distribution can be expressed as

f vð Þ ¼ k

c

v

c

� �k�1

e� v=cð Þk ; (1)

where v is wind speed, f vð Þ is the probability of observing wind speed, k is the nondimen-

sional shape parameter, and c is the scale parameter with the same unit of wind speed. The

cumulative probability function of Weibull distribution can be expressed as

F vð Þ ¼ 1� e� v=cð Þk : (2)

Mean wind speed and power density value of Weibull distribution can be calculated

as follows:

vwm ¼ c� � 1þ 1

k

� �; (3)

Pw ¼1

2rc3� 1þ 3

k

� �; (4)

where r is the standard air density and � is the Gamma function.

Graphic method, maximum likelihood method, and moment methods are commonly

used to estimate Weibull parameters. According to the Genc et al.’s studies (2005),

maximum likelihood method gives more convenient results, therefore maximum likelihood

method was selected.

WIND CHARACTERISTICS AND WIND TURBINE ANALYSES 423

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Weibull distribution’s goodness of fit was evaluated according to the R2 and root

mean square error (RMSE) error analysis and differences of power densities derived from

Weibull distribution and time series to check the suitability of proposed distribution. Table

1 shows the monthly and annual base Weibull parameters, mean speeds, power densities

with R2, and RMSE results. As can be seen from Table 1, monthly value of R2 changes

between 0.9151 and 0.9815 and RMSE ranged from 0.0058 to 0.0166. Annual value of R2

and RMSE are 0.9560 and 0.009906, respectively. Cumulative distribution for observed

and Weibull distribution are shown in Figure 1.

Figure 2 shows the differences of power densities derived from the Weibull distribution

and observed time series. According to the observed time series, the maximum time series

power density was obtained for December and it was 343.154 W/m2, minimum was 152.409

W/m2 for June. Similarly, maximum Weibull power density was calculated for December and

Table 1 Weibull parameters, mean speeds, power densities, and error analysis results.

Months k C (m/s) vwm (m/s) vtsm (m/s) Pw (W/m2) Pts (W/m2) R2 RMSE

January 1.613 6.296 5.641 5.627 269.7 268.7 0.9575 0.0090

February 1.690 6.597 5.888 5.878 288.7 283.9 0.9377 0.0106

March 1.582 6.321 5.673 5.655 281.7 286.6 0.9566 0.0092

April 1.470 5.284 4.783 4.761 187.8 194.3 0.9208 0.0148

May 1.325 4.798 4.415 4.389 174.9 196.3 0.9151 0.0166

June 1.444 4.781 4.337 4.305 144.0 152.4 0.9227 0.0161

July 1.401 5.125 4.671 4.636 188.6 217.3 0.9165 0.0158

August 1.445 5.270 4.781 4.754 192.5 205.7 0.9461 0.0123

September 1.590 5.202 4.666 4.633 155.8 167.9 0.9528 0.0121

October 1.497 5.801 5.238 5.209 240.0 252.4 0.9346 0.0120

November 1.517 5.780 5.211 5.190 231.9 231.8 0.9424 0.0112

December 1.574 6.618 5.943 5.923 326.1 343.2 0.9815 0.0058

Annual 1.484 5.616 5.077 5.052 221.4 231.3 0.9560 0.0099

1 Observed Wind Distribution

Weibull Distribution

0.8

0.6

0.4

Cum

ulat

ive

freq

uenc

y di

stri

butio

n

0.2

0

1 2 3 4 5 6 7 8 9 10 11 12 13 14

v15 16 17 18 19 20 21 22 23 24 25 26 27 28 29

Figure 1 Weibull and observed cumulative distribution.

424 AKDAG AND GULER

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it was 326.103 W/m2 and minimum was 143.959 W/m2 for June. Yearly average mean power

density of observed time series and Weibull distribution was 231.261 and 221.41 W/m2,

respectively. Power density error analysis was carried out using the following:

Errorð%Þ ¼ Pw � Pts

Pts

; (5)

where Pts is the power density calculated from the time series.

WIND DATA ANALYSIS

It is necessary to measure wind speeds at hub heights to estimate wind turbine energy

output correctly. However, in Turkey, wind speed measurements are generally carried out

10 m above the ground level. Hence, it is necessary to estimate wind speed at the hub height

of wind turbine. For this reason, wind speed measurements are extrapolated to the wind

turbine hub height. The shape parameter of Weibull distribution is not affected by extra-

polation of wind speeds. But the scale parameter of the distribution changes at the same

ratio with the wind speed according to the maximum likelihood method and the variation

can be represented by as follows:

c2 ¼ c1 �h2

h1

� �a

: (6)

Hourly, monthly, and yearly average wind speeds and power densities have been

calculated for various wind turbine hub heights. Mean wind speeds of monthly observed

time series and Weibull distribution parameters and turbulence intensity are presented in

Table 2 at 50 m above the ground.

Hourly variation of the mean wind speed and power density at 10 m above ground

level is presented in Figure 3. It is also clear from this figure that wind blows more severely

between 10:00 and 19:00 h. Maximum mean wind speed reached to 6.15 m/s. Differences

between minimum and maximum wind speed was not higher than 30% and there were no

–18

–16

–14

–12

–10

–8–6

–4

–2

0

2

4

1 2 3 4 5 6 7 8 9 10 11 12

Months

Err

or (

%)

Figure 2 Power density error analysis.

WIND CHARACTERISTICS AND WIND TURBINE ANALYSES 425

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considerable wind speed differences. For this reason, it can be regarded as diurnal reg-

ularity. Hourly mean wind speed decreases sharply after 16:00 h. Hourly variation of mean

power density has nearly same trend with wind speed variation.

Figure 4 shows annual variations of observed mean wind speed. Maximum annual

mean wind speed occurred in 2001, minimum was in 2003, and they were 5.53 and 4.59 m/s,

respectively. Mean wind speed for ten-year observed data was 5.05 m/s and maximum wind

speed in this period was 28.8 m/s.

Mean wind speeds and power densities according to the directions are vital for wind

turbine sitting. Table 3 shows mean wind speeds, power densities, and frequencies of these

directions at 10 m above ground level. Prevailing wind direction was found to be S with

17.44% frequency, but E wind direction was also dominant. Furthermore, the value of

highest mean wind speed and power density for E wind direction was 4.87 m/s and 570.36

W/m2, respectively. It is interesting to note that if mean wind speed is higher for one

direction, it does not mean that it has higher power density. For instance, mean wind speeds

in NE and ESE directions were 4.79 and 4.54 m/s, but their power densities were 169.46 and

206.41 W/m2, respectively. As stated earlier, wind speed distribution is vital for wind

Table 2 Weibull and observed time series parameters for 50 m.

Months k c (m/s) vwm (m/s) vtsm (m/s) Turbulence

January 1.613 8.687 7.783 7.763 0.633

February 1.690 9.102 8.124 8.110 0.604

March 1.582 8.721 7.827 7.803 0.650

April 1.470 7.291 6.599 6.568 0.703

May 1.325 6.620 6.091 6.055 0.799

June 1.444 6.596 5.984 5.939 0.724

July 1.401 7.072 6.444 6.396 0.764

August 1.445 7.272 6.596 6.559 0.722

September 1.590 7.177 6.438 6.393 0.662

October 1.497 8.004 7.227 7.188 0.693

November 1.517 7.975 7.190 7.161 0.672

December 1.574 9.132 8.200 8.172 0.655

Annual 1.484 7.749 7.005 6.970 0.697

0

1

2

3

4

5

6

7

Hours

Mea

n W

ind

Spee

d (m

/s)

0

50

100

150

200

250

300

350

400

450

Pow

er D

ensi

ty (W

/m2 )

Power Density Wind Speed

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

Figure 3 Hourly mean wind speed and power density variation.

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energy applications. So, mean wind speed, power density, and frequency distribution are

important to locate wind turbines. Optimum direction to produce more energy can be

determined using these parameters. In other word, not only the frequency of wind direction

is crucial to place wind turbines but also mean wind speed, power density, and direction of

wind speed distribution are important.

ENERGY OUTPUT ANALYSIS

One of the most vital indicators of wind turbine performance is the capacity factor,

which can be defined as the ratio between average power output and rated power. However,

capacity factor may not be the only parameter for selection of wind turbines; moreover,

economical analyses results should be considered for wind turbines. In the literature, there

are various studies in order to determine most suitable wind turbine (Hu and Cheng 2007;

Jangamshetti and Rau 1999; Pallabazzer 2003, 2004b). But to design and produce most

0

1

2

3

4

5

6

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006

Years

Mea

n W

ind

Spee

d (m

/s)

Figure 4 Yearly mean wind speed variation.

Table 3 Parameters according to the directions.

Direction vtsm (m/s) Pts (W/m2) Frequency (%)

N 4.50 180.10 3.20

NNE 3.93 150.85 0.77

NE 4.79 169.46 3.49

ENE 3.66 120.70 1.01

E 7.26 570.36 16.89

ESE 4.54 206.41 4.22

SE 2.79 32.20 9.13

SSE 1.92 10.15 3.83

S 4.87 156.93 17.44

SSW 4.78 144.32 4.94

SW 5.80 192.79 10.70

WSW 3.22 73.57 1.32

W 5.41 246.87 7.17

WNW 5.36 272.53 5.76

NW 5.01 202.38 8.52

NNW 3.68 144.21 1.62

WIND CHARACTERISTICS AND WIND TURBINE ANALYSES 427

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suitable wind turbine for one region is costly. For this reason, it is more convenient to select

suitable wind turbine among commercial ones. In this study, energy output analysis was

carried out for thirty commercial wind turbines. Cut in, rated, and cut out wind speeds of

wind turbines are generally change between 2 and 4, 13 and 14, and 21 and 25 m/s,

respectively. Detailed information about technical data of wind turbines can be obtained

from DeWind (2008), ENERCON (2008), Gamesa (2008), GE Power (2008), Nordex

(2008), Suzlon (2008), VESTAS (2008), and WASP (2008).

Selected wind turbines are divided into three groups, which are lower than 1000 kW,

between 1000 and 2000 kW, and greater than 2000 kW. Monthly and annual capacity

factors were calculated using Weibull distribution and Wasp Turbine Editor Software.

Results of the calculations are listed in Tables 4–6.

In the first group, the largest calculated annually capacity factor was 0.415 and the

maximum monthly capacity factor was 0.509 in February at 75 m hub height for Suzlon

S64/950. While hub height decreased from 75 to 65 m, capacity factor decreased from

0.415 to 0.402. In other words, energy output decreased 108,186 kWh yearly. When hub

height of turbine increased from 55 to 65 m, capacity factor increased to 0.016, which was

equivalent to 133,152 kWh yearly estimated energy output. Variation of energy output per

meter was related with the turbine power curves and wind distribution of the region.

In the second group, Vestas V90 1800 kW at 95-m hub height had the largest annually

capacity factor with 0.439. The maximum monthly capacity factor was obtained as 0.533 in

February for the same turbine with the same hub height. While hub height decreased from

95 to 80 m, capacity factor decrease from 0.439 to 0.423, which was equivalent to 252,288

kWh yearly estimated energy output.

In the third group, the largest annual capacity factor was 0.426 at 100 m hub height

for Gamese G90 2000 kW wind turbine. The maximum monthly capacity factor calculated

as 0.519 in February for the same turbine with same hub height. While hub height decreased

from 100 to 80 m, capacity factor decreased from 0.426 to 0.407, which was equivalent to

333,880 kWh yearly estimated energy output.

Wind turbine’s kilowatt price changes according to the manufacturer, location, and size

range. Ultimate price of wind turbines is determined according to the negotiation between

client and manufacturer (Pallabazzer 2004a). Besides, it is difficult to obtain a price because

of market conditions. For this reason, no economic analysis is carried out in this study.

CONCLUSION

Wind energy potential of Amasra was neglected and no wind farm has been installed.

However, this study shows that there is significant wind energy potential. Monthly average

mean wind speeds varied between 4.34 and 5.94 m/s at 10 m above the ground level. Yearly

mean wind speeds changed from 4.59 to 5.53 m/s. Mean wind speed was 5.05 m/s for ten-

year data. Monthly mean power densities were between 152.41 and 286.63 W/m2. Power

density was 231.26 W/m2 and maximum wind speed was 28.8 m/s for ten-year data.

Prevailing wind direction was S with 17.44% frequency, but E direction was also dominant.

As a result of calculations, wind turbines capacity factors based on ten year changed

between 0.268 and 0.439. World’s estimated mean capacity factor is 0.20. For this reason,

installation of wind farm in Amasra will be very profitable.

Energy output of wind turbine not only depends on mean wind speed but also power

density, shape of wind distribution, and wind turbine’s power curve. In conclusion, even if a

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mod

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rbin

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(kW

)

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b

hei

gh

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(m)

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.F

eb.

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pr.

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ep.

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0.3

61

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0.3

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88

00

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0.3

88

0.4

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0.3

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0.3

02

0.2

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0.2

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0.3

61

0.3

17

0.3

15

0.3

46

0.3

60

0.3

55

0.4

06

0.4

05

0.4

71

0.3

90

Ves

tas

V5

28

50

85

04

50

.36

30

.388

0.3

64

0.2

80

0.2

46

0.2

38

0.2

69

0.2

80

0.2

68

0.3

22

0.3

21

0.3

86

0.3

08

50

0.3

73

0.3

99

0.3

74

0.2

90

0.2

54

0.2

47

0.2

78

0.2

89

0.2

79

0.3

32

0.3

31

0.3

96

0.3

17

55

0.3

83

0.4

09

0.3

83

0.2

98

0.2

62

0.2

55

0.2

86

0.2

98

0.2

88

0.3

41

0.3

39

0.4

05

0.3

26

65

0.3

99

0.4

26

0.3

99

0.3

13

0.2

75

0.2

69

0.3

01

0.3

12

0.3

04

0.3

56

0.3

55

0.4

20

0.3

41

75

0.4

13

0.4

40

0.4

13

0.3

26

0.2

86

0.2

82

0.3

13

0.3

25

0.3

18

0.3

69

0.3

68

0.4

32

0.3

54

EN

ER

CO

NE

44

90

04

50

.31

80

.341

0.3

20

0.2

42

0.2

13

0.2

03

0.2

33

0.2

42

0.2

29

0.2

82

0.2

79

0.3

41

0.2

68

55

0.3

37

0.3

62

0.3

39

0.2

59

0.2

28

0.2

19

0.2

49

0.2

59

0.2

47

0.2

99

0.2

98

0.3

60

0.2

85

Su

zlo

nS

.64

/95

09

50

55

0.4

49

0.4

79

0.4

49

0.3

57

0.3

13

0.3

09

0.3

42

0.3

55

0.3

49

0.4

02

0.4

02

0.4

70

0.3

86

65

0.4

66

0.4

95

0.4

65

0.3

73

0.3

28

0.3

25

0.3

57

0.3

71

0.3

67

0.4

18

0.4

18

0.4

85

0.4

02

75

0.4

79

0.5

09

0.4

78

0.3

86

0.3

40

0.3

39

0.3

70

0.3

84

0.3

82

0.4

31

0.4

31

0.4

97

0.4

15

429

Dow

nloa

ded

by [

Yor

k U

nive

rsity

Lib

rari

es]

at 0

6:10

10

Oct

ober

201

3

Ta

ble

5M

onth

lyan

dan

nu

alca

pac

ity

fact

ors

.

Man

ufa

ctu

rer

Tu

rbin

e

mod

el

Po

wer

(kW

)

Hu

b

hei

gh

t(m

)Ja

n.

Feb

.M

ar.

Ap

r.M

ayJu

ne

July

Au

g.

Sep

.O

ct.

No

v.

Dec

.A

nn

ual

Gen

eral

Ele

ctri

cG

EV

HP

10

00

60

0.4

17

0.4

45

0.4

17

0.3

30

0.2

90

0.2

85

0.3

16

0.3

29

0.3

21

0.3

73

0.3

72

0.4

38

0.3

58

70

0.4

32

0.4

60

0.4

31

0.3

44

0.3

02

0.2

99

0.3

30

0.3

42

0.3

36

0.3

87

0.3

86

0.4

51

0.3

72

DeW

ind

D6

12

50

65

0.3

88

0.4

14

0.3

87

0.3

06

0.2

67

0.2

63

0.2

92

0.3

04

0.2

98

0.3

46

0.3

45

0.4

05

0.3

32

Su

zlo

nS

.64

/12

50

12

50

55

0.3

79

0.4

06

0.3

80

0.2

94

0.2

58

0.2

50

0.2

82

0.2

93

0.2

83

0.3

37

0.3

35

0.4

01

0.3

22

65

0.3

95

0.4

23

0.3

96

0.3

09

0.2

71

0.2

65

0.2

97

0.3

08

0.3

00

0.3

52

0.3

51

0.4

16

0.3

37

75

0.4

09

0.4

37

0.4

09

0.3

22

0.2

82

0.2

78

0.3

09

0.3

21

0.3

14

0.3

65

0.3

64

0.4

29

0.3

50

S.6

6/1

25

01

25

05

50

.38

70

.414

0.3

87

0.3

01

0.2

64

0.2

57

0.2

89

0.3

00

0.2

90

0.3

44

0.3

42

0.4

09

0.3

29

65

0.4

03

0.4

31

0.4

03

0.3

16

0.2

77

0.2

72

0.3

03

0.3

15

0.3

07

0.3

60

0.3

58

0.4

24

0.3

44

75

0.4

17

0.4

45

0.4

17

0.3

29

0.2

89

0.2

85

0.3

16

0.3

28

0.3

21

0.3

73

0.3

71

0.4

37

0.3

57

No

rtex

S7

0/1

50

01

50

06

50

.40

30

.431

0.4

03

0.3

15

0.2

75

0.2

69

0.3

02

0.3

14

0.3

05

0.3

58

0.3

57

0.4

24

0.3

43

80

0.4

23

0.4

51

0.4

22

0.3

34

0.2

92

0.2

88

0.3

20

0.3

32

0.3

26

0.3

77

0.3

77

0.4

42

0.3

62

85

0.4

29

0.4

57

0.4

28

0.3

39

0.2

97

0.2

94

0.3

25

0.3

38

0.3

32

0.3

83

0.3

82

0.4

47

0.3

68

90

0.4

34

0.4

63

0.4

33

0.3

45

0.3

02

0.2

99

0.3

30

0.3

43

0.3

38

0.3

88

0.3

88

0.4

52

0.3

73

10

00

.44

40

.473

0.4

42

0.3

54

0.3

10

0.3

08

0.3

39

0.3

52

0.3

49

0.3

97

0.3

97

0.4

61

0.3

82

S7

7/1

50

01

50

06

50

.42

70

.456

0.4

27

0.3

38

0.2

96

0.2

92

0.3

24

0.3

37

0.3

30

0.3

82

0.3

81

0.4

48

0.3

67

80

0.4

47

0.4

77

0.4

47

0.3

57

0.3

13

0.3

11

0.3

42

0.3

55

0.3

51

0.4

01

0.4

01

0.4

66

0.3

86

85

0.4

53

0.4

82

0.4

52

0.3

63

0.3

18

0.3

16

0.3

47

0.3

61

0.3

57

0.4

07

0.4

06

0.4

71

0.3

91

90

0.4

58

0.4

88

0.4

57

0.3

68

0.3

23

0.3

22

0.3

52

0.3

66

0.3

63

0.4

12

0.4

11

0.4

76

0.3

96

10

00

.46

80

.497

0.4

66

0.3

78

0.3

31

0.3

31

0.3

61

0.3

75

0.3

74

0.0

40

0.4

21

0.4

84

0.4

06

Ves

tas

V8

2-1

65

01

65

08

00

.44

00

.467

0.4

36

0.3

61

0.3

16

0.3

20

0.3

44

0.3

58

0.3

62

0.3

97

0.3

98

0.4

49

0.3

84

V9

0-1

80

01

80

08

00

.48

70

.518

0.4

86

0.3

94

0.3

46

0.3

47

0.3

77

0.3

92

0.3

91

0.4

39

0.4

39

0.5

05

0.4

23

95

0.5

03

0.5

33

0.5

01

0.4

10

0.3

61

0.3

63

0.3

92

0.4

07

0.4

09

0.4

54

0.4

55

0.5

18

0.4

39

430

Dow

nloa

ded

by [

Yor

k U

nive

rsity

Lib

rari

es]

at 0

6:10

10

Oct

ober

201

3

Ta

ble

6M

onth

lyan

dan

nual

capac

ity

fact

ors

.

Man

ufa

ctu

rer

Tu

rbin

em

od

elP

ow

er(k

W)

Hu

bh

eigh

t(m

)Ja

n.

Feb

.M

ar.

Ap

r.M

ayJu

ne

July

Au

g.

Sep

.O

ct.

No

v.

Dec

.A

nn

ual

DeW

ind

D8

.22

00

08

00

.420

0.4

49

0.4

20

0.3

33

0.2

91

0.2

88

0.3

19

0.3

31

0.3

25

0.3

76

0.3

75

0.4

40

0.3

61

10

00

.441

0.4

70

0.4

40

0.3

53

0.3

09

0.3

08

0.3

38

0.3

51

0.3

48

0.3

95

0.3

95

0.4

58

0.3

80

EN

ER

CO

NE

82

20

00

80

0.4

65

0.4

95

0.4

64

0.3

72

0.3

27

0.3

25

0.3

57

0.3

70

0.3

66

0.4

17

0.4

17

0.4

84

0.4

01

90

0.4

76

0.5

06

0.4

75

0.3

83

0.3

37

0.3

36

0.3

67

0.3

81

0.3

79

0.4

28

0.4

28

0.4

94

0.4

12

10

00

.486

0.5

16

0.4

84

0.3

93

0.3

46

0.3

46

0.3

76

0.3

91

0.3

90

0.4

37

0.4

37

0.5

02

0.4

22

Gam

esa

G80-2

.02000

60

0.3

90

0.4

17

0.3

90

0.3

04

0.2

67

0.2

61

0.2

92

0.3

04

0.2

94

0.3

47

0.3

46

0.4

11

0.3

32

65

0.3

98

0.4

25

0.3

98

0.3

12

0.2

73

0.2

68

0.2

99

0.3

11

0.3

02

0.3

55

0.3

53

0.4

19

0.3

40

80

0.4

18

0.4

46

0.4

17

0.3

31

0.2

90

0.2

86

0.3

17

0.3

29

0.3

23

0.3

73

0.3

73

0.0

37

0.3

58

10

00

.438

0.4

67

0.4

37

0.3

51

0.3

07

0.3

06

0.3

36

0.3

49

0.3

45

0.3

93

0.3

93

0.4

55

0.3

78

G8

3-2

.02

00

06

50

.408

0.4

36

0.4

08

0.3

20

0.2

81

0.2

75

0.3

07

0.3

19

0.3

11

0.3

64

0.3

63

0.4

29

0.3

49

80

0.4

28

0.4

56

0.4

27

0.3

39

0.2

97

0.2

94

0.3

25

0.3

38

0.3

32

0.3

83

0.3

82

0.4

47

0.3

67

G8

7-2

.02

00

06

50

.432

0.4

61

0.4

32

0.3

43

0.3

00

0.2

96

0.3

28

0.3

41

0.3

35

0.3

87

0.3

86

0.4

52

0.3

71

80

0.4

52

0.4

81

0.4

51

0.3

62

0.3

17

0.3

15

0.3

46

0.3

60

0.3

56

0.4

06

0.4

05

0.4

70

0.3

90

10

00

.472

0.5

02

0.4

71

0.3

82

0.3

35

0.3

36

0.3

66

0.3

80

0.3

79

0.4

25

0.4

25

0.4

88

0.4

10

G9

0-2

.02

00

08

00

.469

0.4

99

0.4

68

0.3

78

0.3

32

0.3

31

0.3

62

0.3

76

0.3

73

0.4

22

0.4

22

0.4

87

0.4

07

10

00

.489

0.5

19

0.4

87

0.3

98

0.3

51

0.3

52

0.3

81

0.3

96

0.3

96

0.4

42

0.4

42

0.5

05

0.4

26

Ves

tas

V8

0-2

00

02

00

06

00

.390

0.4

17

0.3

90

0.3

04

0.2

67

0.2

61

0.2

92

0.3

04

0.2

94

0.3

47

0.3

46

0.4

11

0.3

32

70

0.4

05

0.4

32

0.4

05

0.3

18

0.2

79

0.2

74

0.3

05

0.3

17

0.3

10

0.3

61

0.3

60

0.4

25

0.3

46

80

0.4

18

0.4

46

0.4

17

0.3

31

0.2

90

0.2

86

0.3

17

0.3

29

0.3

23

0.3

73

0.3

73

0.4

37

0.3

58

10

00

.438

0.4

67

0.4

37

0.3

51

0.3

07

0.3

06

0.3

36

0.3

49

0.3

45

0.3

93

0.3

93

0.4

55

0.3

78

V9

0-2

00

02

00

08

00

.468

0.4

98

0.4

67

0.3

76

0.3

30

0.3

29

0.3

60

0.3

74

0.3

71

0.4

21

0.4

20

0.4

86

0.4

05

95

0.4

84

0.5

14

0.4

82

0.3

92

0.3

44

0.3

45

0.3

75

0.3

90

0.3

89

0.4

36

0.4

36

0.5

00

0.4

20

(Co

nti

nu

ed)

431

Dow

nloa

ded

by [

Yor

k U

nive

rsity

Lib

rari

es]

at 0

6:10

10

Oct

ober

201

3

Ta

ble

6(C

on

tin

ued

).

Man

ufa

ctu

rer

Tu

rbin

em

od

elP

ow

er(k

W)

Hu

bh

eigh

t(m

)Ja

n.

Feb

.M

ar.

Ap

r.M

ayJu

ne

July

Au

g.

Sep

.O

ct.

No

v.

Dec

.A

nn

ual

Su

zlo

nS

88/2

10

02

10

08

00

.441

0.4

70

0.4

40

0.3

51

0.3

07

0.3

04

0.3

36

0.3

49

0.3

44

0.3

95

0.3

94

0.4

60

0.3

79

EN

ER

CO

NE

70

23

00

70

0.3

67

0.3

92

0.3

67

0.2

85

0.2

50

0.2

43

0.2

73

0.2

84

0.2

74

0.3

26

0.3

25

0.3

88

0.3

12

75

0.3

73

0.3

99

0.3

74

0.2

91

0.2

55

0.2

48

0.2

79

0.2

90

0.2

81

0.3

32

0.3

31

0.3

94

0.3

18

80

0.3

79

0.4

06

0.3

80

0.2

96

0.2

60

0.2

54

0.2

84

0.2

96

0.2

87

0.3

38

0.3

37

0.3

99

0.3

18

90

0.3

90

0.4

17

0.3

90

0.3

07

0.2

69

0.2

64

0.2

94

0.3

06

0.2

98

0.3

48

0.3

47

0.4

09

0.3

34

10

00

.400

0.4

27

0.3

99

0.3

16

0.2

77

0.2

73

0.3

03

0.3

15

0.3

08

0.3

57

0.3

56

0.4

18

0.3

43

No

rtex

N9

0/2

30

02

30

06

00

.409

0.4

37

0.4

09

0.3

21

0.2

81

0.2

75

0.3

08

0.3

20

0.3

11

0.3

65

0.3

64

0.4

30

0.3

49

70

0.4

24

0.4

53

0.4

24

0.3

35

0.2

94

0.2

89

0.3

21

0.3

34

0.3

27

0.3

79

0.3

78

0.4

44

0.3

64

80

0.4

37

0.4

66

0.4

36

0.3

47

0.3

04

0.3

01

0.3

33

0.3

46

0.3

41

0.3

91

0.3

91

0.4

56

0.3

76

10

00

.458

0.4

87

0.4

56

0.3

68

0.3

22

0.3

22

0.3

52

0.3

66

0.3

63

0.4

11

0.4

11

0.4

74

0.3

96

N1

00

/25

00

25

00

10

00

.449

0.4

75

0.4

45

0.3

71

0.3

24

0.3

31

0.3

53

0.3

67

0.3

75

0.4

06

0.4

07

0.4

55

0.3

93

N8

0/2

50

02

50

06

00

.346

0.3

71

0.3

47

0.2

66

0.2

32

0.2

24

0.2

55

0.2

65

0.2

53

0.3

07

0.3

05

0.3

68

0.2

92

70

0.3

61

0.3

87

0.3

62

0.2

79

0.2

44

0.2

37

0.2

68

0.2

78

0.2

68

0.3

20

0.3

19

0.3

82

0.3

06

80

0.3

74

0.4

00

0.3

74

0.2

91

0.2

54

0.2

48

0.2

79

0.2

90

0.2

81

0.3

32

0.3

31

0.3

94

0.3

18

10

00

.394

0.4

22

0.3

94

0.3

10

0.2

71

0.2

67

0.2

07

0.3

09

0.3

03

0.3

52

0.3

51

0.4

13

0.3

37

Ves

tas

V9

0-3

00

03

00

08

00

.391

0.4

18

0.3

91

0.3

07

0.2

69

0.2

63

0.2

94

0.3

06

0.2

98

0.3

49

0.3

48

0.4

11

0.3

34

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region has high mean wind speed and power density, it is not a universal rule that this

location has higher energy output.

NOMENCLATUREf ðvÞ probability of observing wind speed

v wind speed

k Weibull shape parameter

c Weibull scale parameter

FðvÞ cumulative probability function

vwm Weibull distribution mean wind speed

Pw Weibull power density

� Gamma function

r air density

vtsm time series mean wind speed

Pts time series mean power density

h height

a power law coefficient

REFERENCES

Akdag, S.A., and O. Guler. 2009. Calculation of wind energy potential and economic analysis by

using Weibull distribution a case study from Turkey. Part 2: Economic analysis. Energy

Sources, Part B: Economics, Planning and Policy 4: 9–16.

DeWind. 2008. http://www.compositetechcorp.com/windpower.htm (Accessed August 2009).

Energy Market Regulatory Authority. 2009a. Activity Report, http://www.epdk.gov.tr/yayin_rapor/

yillik/yillik.htm (Accessed November 2009).

Energy Market Regulatory Authority. 2009b. http://www.epdk.gov.tr/lisans/elektrik/yek/yek.html

(Accessed November 2009).

ENERCON. 2008. http://www.enercon.de/en/_home.htm (Accessed August 2008).

Gamesa. 2008. http://www.gamesa.es/en (Accessed August 2008).

Genc, A., M. Erisoglu, A. Pekgor, G. Oturanc, A. Hepbasli, and K. Ulgen. 2005. Estimation of wind

power potential using Weibull distribution. Energy Sources 27: 809–822.

GE Power. 2008. http://www.gepower.com/businesses/ge_wind_energy/en/index.htm (Accessed

August 2008).

Hu, S., and J. Cheng. 2007. Performance evaluation of pairing between sites and wind turbines.

Renewable Energy 32(11): 1934–1947.

Jangamshetti, S.H., and V.G. Rau. 1999. Site matching of wind turbine generators: A case study.

Energy Conversion, IEEE Transaction on 14(4): 1537–1543.

Malkoc, Y. 2007. Numerical Wind Energy Potential Map, 26th Energy Efficiency Weeks, Ankara

(in Turkish).

Ministry of Energy and Natural Resources. 2009. http://www.enerji.gov.tr (Accessed November 2009).

Nordex. 2008. http://www.nordex-online.com/en (Accessed August 2008).

Pallabazzer, R. 2003. Parametric analysis of wind siting efficiency. Journal of Wind Engineering and

Industrial Aerodynamics 91(11): 1329–1352.

Pallabazzer, R. 2004a. Effects of site wind properties on wind-electric conversion costs. Wind

Engineering 28(6): 679–694.

Pallabazzer, R. 2004b. Previsional estimation of the energy output of windgenerators. Renewable

Energy 29(3): 413–420.

Suzlon. 2008. http://www.suzlon.com/ (Accessed August 2008).

VESTAS. 2008. http://vestas.com/ (Accessed August 2008).

WASP. 2008. http://www.wasp.dk/Download/PowerCurves.html (Accessed August 2008).

WIND CHARACTERISTICS AND WIND TURBINE ANALYSES 433

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