Wind Energy Data Analysis and Resource Mapping of Geba Catchment, North Ethiopia Wind Energy Data Analysis and Resource Mapping of Geba Catchment, North Ethiopia

Wind Energy Data Analysis and Resource Mapping of Geba Catchment, North Ethiopia

by

Mulu Bayray, Anwar Mustefa, Ftwi Yohannes, Hailay Kiros, AsfafawHaileslasie, Petros Gebray, Mesele Hayelom and Addisu Dagne

REPRINTED FROM

WIND ENGINEERINGVOLUME 37, NO. 4, 2013

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Wind Energy Data Analysis and Resource Mappingof Geba Catchment, North Ethiopia

Mulu Bayray, Anwar Mustefa, Ftwi Yohannes, Hailay Kiros, Asfafaw Haileslasie,Petros Gebray, Mesele Hayelom and Addisu DagneDepartment of Mechanical Engineering, EiT – M, Mekelle University, Ethiopia

Received March 28, 2013;Accepted May 3, 2013

WIND ENGINEERING VOLUME 37, NO. 4, 2013 PP 333–346 333

ABSTRACTWind energy potential in Ethiopia is estimated to be enormous due to local peculiar

landscape situations. However, the country started to utilize the potential only very recently.

One of the reasons for low utilization of wind energy in Ethiopia is the absence of reliable and

accurate wind energy resource data. Development of reliable and accurate wind atlas helps

to identify candidate sites for wind energy applications and facilitates the planning and

implementation of wind energy projects. This paper presents wind energy data analysis and

wind atlas of Geba catchment in North Ethiopia.

The work reported in this paper is based on wind data collected over a period of one year

from measuring masts in six different sites in Tigray, Ethiopia. The data was analyzed using

various statistical software to evaluate the wind energy potential of the area. Average wind

speed and power density, distribution of the wind prevailing direction, turbulence intensity

and wind shear profile of each site were determined. Wind Atlas Analysis and Application

Programme (WAsP) was used to generate the wind atlas of the area and to develop the wind

speed and power density maps.

The data analysis indicates that the average wind speed at 10 m above ground level

(a.g.l.) varies from 3.7 m/s to 6.64 m/s. The mean power density at 10 m a.g.l varies from

64 W/m2 to 301 W/m2. The prevailing wind directions are East and South East directions.

The wind resource map developed by WAsP at 50 m indicated that the catchment has good

wind power potential having mean wind speed and power density of 6.5 m/s and 288 W/m2,

respectively.

1. INTRODUCTIONEthiopia’s current electrical energy supply is mainly from hydropower. Wind energy is an

alternative energy resource which can complement hydropower especially during the dry

season. Due to the fast economic development in the recent years, the country is looking for

more power plants to satisfy the high demand for electricity. Wind energy resource potential

in the country is estimated to be high. The objective of the study reported in this paper was to

conduct wind energy resource assessment of a specific catchment area of the river named

Geba located in Northern part of Ethiopia.

2. METHODOLOGY2.1. Description of the study areaGeba catchment is found in Tigray Region, North Ethiopia. The entire Geba catchment is

found between 13°18’ to14°15 N (latitude) and 38°38’ to 39°48’ E (longitude). It covers an area of

5133 km2. The elevation ranges from 955 m to 3315 m above sea level and the mean elevation

is 2146 m above sea level. Figure 1a) shows the base map of the catchment. The location of the

six measurement masts is indicated in Figure 1b). The details of the site location and available

date for analysis are indicated in Table 1. Masts of the sites number 1 to 4 are erected by the

Department while sites 5 and 6 are erected by the national utility corporation EEPCo. Details

of the masts and instrumentation are reported elsewhere [2−4].

2.2. Data collection, screening and validationAll the collected data was inspected for completeness and any erroneous records. The time

series of the data was checked to look for missing data values and a number of data validation

routines were used to screen all the data for suspect and erroneous values. General system

and parameter checks were used for data screening and validation. The validation checks

include: continuity test to identify missing records in the data, inspection of the average wind

speed at each 10-minutes interval records, inspection of negative and unrealistic high wind

speed and wind direction records and observation of vertical profile of wind speed on same

mast (negative and undefined wind shear coefficients).

2.3. Statistical data analysis2.3.1. Average Wind SpeedThe wind characterization in terms of speed, direction and wind power is the first step to

obtain the initial feasibility of generating electricity from wind power through a wind farm, in

a given region. The most critical factor influencing the power developed by a wind energy

conversion system is the wind speed. The average wind speed Vm is given as:

334 WIND ENERGY DATA ANALYSIS AND RESOURCE MAPPING OF

GEBA CATCHMENT, NORTH ETHIOPIA

Idaga HamusInda Tekle Haymanot

Senkata

Atsbi Inda SilaseWukro

May MekdanMekele

KwihaMay Keyah

Tigray Region

Dera Mast

Hagereselam Mast

Mosobo-Harena Mast

Mekelle University Mast

Ashegoda Mast

Mydehru Mast

Abiy adi

Figure 1: a) Base map of Geba catchment [1] b) location map of the six measurement masts.

Table 1: Location of the wind measuring masts in the catchmentSite Wind Location of wind Altitude Height Available data recordsNo. measuring measuring masts a.g.l

mast Latitude Longitude [m] [m] From Until1 Dera 13.99° N 39.73° E 2870 10, 30 01/07/2010 19/04/20122 Hagereselam 13.66° N 39.19° E 2628 10, 30 11/10/2010 17/04/20123 Mayderhu 13.29° N 39.40° E 2512 10, 30 15/01/2011 18/04/20124 Mekelle University 13.48° N 39.49° E 2208 10 09/04/2010 04/0520115 Ashegoda 13.42° N 39.60° E 2425 10, 40 08/06/2010 24/09/20116 Mosobo-Harena 13.57° N 39.51° E 2401 10, 40 01/01/2006 31/12/2007

(1)

Where Vi is the wind speed at interval i and n is the number of records. The average wind

speed is calculated at hourly, daily, monthly and annual interval.

2.3.2. Wind Power DensityThe wind power per unit area, P/A or wind power density at interval i is given by:

(2)

Where Vi is the wind speed at interval i, ρ is air density. Average power density is

calculated in similar way to the average speed shown previously.

The wind speed and power density at a certain height determines the wind power class of the

site. The wind power class of a site is determined as per the standard classification reported in [5].

2.3.3. Wind Shear CoefficientThe wind shear coefficient is calculated assuming power law relationship at the two heights.

The coefficient α is found from:

(3)

Where V1 is the wind speed at height z1 and V2 is the wind speed at height z2.

2.3.4. TurbulenceThe average turbulence intensity of the sites was calculated by taking the average of the

individual turbulence intensity values of 10 minutes records which were calculated by

dividing the standards deviation with the average speed of each record. The turbulence

intensity TI at interval i is given from [6]:

(4)

Where σi is the standard deviation of wind speed at interval i. The overall average

turbulence is found in similar way to that shown for wind speed. TI is a relative indicator of

turbulence with low levels indicated by values less than or equal to 0.10, moderate levels to

0.25, and high levels greater than 0.25.

2.4. Modeling with WAsP2.4.1. Observed Wind Climate (OWC)WAsP (Wind Atlas Analysis and Application Program) [7] was used to generate the Wind

Atlas and to develop the wind resource map of the Catchment. The flow modeling of WAsP is

discussed in [8] and application of the software for resource assessment may be found in

literature such as [9−11]. The various inputs needed in WAsP are Observed Wind Climate

(OWC) of sites, Vector Map of the catchment and Obstacle Groups to the measuring masts.

OWC is a tabular summary of the frequency of occurrence of wind speed and wind direction.

The OWC is produced from raw wind speed and direction measurements.

TIV

ii

i

=σ

α =−

−

In In

In In

( ) ( )

( ) ( )

V V

Z Z2 1

2 1

P

A nV

ii

=1 3ρ

Vn

Vm i

i

n

==

∑1

0


The OWC represents the data converted into Weibull probability density function. The

Weibull function is defined using two factors namely the scale parameter A and the shape

parameter k. The OWC also shows the wind direction distribution as wind rose. Wind rose

diagram shows the distribution of wind in different directions. The wind rose diagram is

generated by dividing into twelve equally spaced sectors. The frequency distribution for each

sector is calculated and plotted in the wind rose diagram.

2.4.2 Wind AtlasWind Atlas is a generalized wind climate of the observed wind climate. The data measured

from the wind measuring mast is a site specific data. The Wind Atlas data sets are site-

independent and the wind distributions have been reduced to certain standard conditions.

The Wind Atlas contains data for 5 reference roughness lengths (0.000 m, 0.030 m, 0.100 m,

0.200 m, 0.400 m) and 5 reference heights (10 m, 30 m, 50 m, 70 m, 100 m) a.g.l.

2.4.3 Resource MapResource grid is a rectangular set of points for which summary of predicted wind climate data

are calculated. WAsP uses data from one metrological mast to generate Wind Atlas and

Resource Grid of an area. However, it doesn’t support multiple masts. The area of Geba

catchment is too large that it is not recommended to use data from a single mast to generate

the Wind Atlas and Resource Grid of the catchment. In order to use data from different wind

measuring masts the study area was divided in to six zones equal to the number of masts. The

area was divided in such a way that each zone contains one wind measuring mast as shown in

Figure 2. Wind Atlas and Resource map of each area was generated using observed wind

climate of each wind measuring mast and vector map of each area. The wind resource map of

the catchment was found by the combination of the resource maps of each zone.



Dera Mast

Hagereselam Mast

Mosobo-Harena Mast

MU Mast

Ashegoda Mast

Myderhu Mast

Figure 2: Six zones of the study area.

3. RESULTS AND DISCUSSION3.1. Data recovery and validationData recovery and validation was conducted as per the procedure discussed in section 2.2.

The summary of results of the data screening and validation is shown in Table 2. The data

recovery rate was greater than 96% in all the measuring stations except in Hagereselam

which was 94%. The reason for low data recovery rate in Hagereselam mast was that the mast

was taken down for 20 days from 21-05-2011 to 11-06-2011 to replace a broken anemometer

accounting for 4% of the missing data. The data recovery rate in the other masts was good

except for the loss of some data during transfer from data loggers to laptops because the

memory card had to be removed.

Measures were taken to replace the missing and erroneous data records when

necessary. Data lost during transfer of data due to removal of memory card was filled with

average data record of the same hour where the data was missing. The missing data

records at 30 m in Hagereselam mast due to break down of the anemometer were filled by

interpolating from the 10 m records using the power low and the wind shear coefficient

calculated based on overall average wind speeds at 10 and 30 m. Data lost at both heights

for extended period of time was found in Hagereselam, Ashegoda and Mosobo-Harena

masts. Since the overall data recovery rate was above 94% in these sites this will not affect

the overall data quality of the sites. No measure was taken to fill the loss of data for

extended period of time. Negative and undefined wind shear coefficients occurred when

the wind speed at 30 or 40 m height is less than the 10 m record. This was corrected by

replacing the 30 or 40 m wind speed records by interpolating from the 10 m records using

the power law and the wind shear coefficient calculated based on overall average wind

speeds at 10 and 30 or 40 m.

3.2. Results of statistical data analysis3.2.1. Average Wind Speed and Power DensityThe overall average wind speed and average power density of the data during the period

were calculated based on the equations discussed in section 2.3. The results obtained for each

site are shown in Table 3. Included in this table is the maximum wind speed recorded averaged

in the ten minutes measurement interval for each site. The wind power density class at each

height for the respective sites is also shown in the table. The class ranges from class 6 in

Hagereselam site to class 1 at Dera and Mekelle University sites.


Table 2: Data screening and validationSite Data Available Total number of Gross Number of negative No. measuring data records missing data data and undefined wind

mast (10 minute records recovery shear coefficientsaverage) rate (%)

1 Dera 94,800 13 99.99 11,2522 Hagereselam 70,823 4,470 94.06 23,1813 Mayderhu 66,082 7 99.99 5,0244 Mekelle University 56,125 23 99.96 NA5 Ashegoda 65,592 2,544 96.26 9376 Mosobo-Harena 103,001 2,119 97.98 3,657

NA: not applicable, only one anemometer at 10 m.

3.2.2. Wind ShearThe wind shear coefficients are calculated based on the overall average wind speeds at 10 and

30 or 40 m using power law as per the equation shown in section 2.3. The wind shear

coefficient for each site is shown in Table 4. The coefficient varies from the lowest value of

0.042 at Hagereselam to the highest value of 0.168 in Dera. The wind shear profile is plotted

based on the calculated coefficient as shown in Figure 3. The profile of Dera site shows

significant difference from the others with low wind speed and high wind shear coefficient.

3.2.3. Turbulence IntensityThe average turbulence intensity for each site was calculated based on the formula discussed

in the methodology section 2.3. The results for each site at the two measurement heights are

shown in Table 5. Moderate turbulence (0.1−0.25) intensities are found at Hagereselam,



Table 3: Average wind speed and power density at the different wind measuring mastsWind measuring Average wind Maximum wind Average wind Wind power

mast speed (m/s) speed (m/s) power density (m/s) density class10 m 30/40 m 10 m 30/40 m 10 m 30/40 m 10 m 30/40 m

Dera 3.70 4.45 13.59 15.4 64.11 95.16 1 1Hagereselam 6.64 6.96 18.07 18.92 301.32 336.96 6 4

Mayderhu 5.68 6.19 15.78 16.78 180.58 228.86 3 2

MekelleUniversity 3.95 NA 12.12 NA 84.45 NA 1 NA

Ashegoda 6.19 7.21 18 20.3 211.10 318.44 4 3

Mosobo-Harena 5.12 6.41 14.5 18.2 138.55 276.52 2 3

Dera site

Hagereselam site

Myderhu site

Ashegoda site

MosoboH arena site

200

180

160

140

120

Hei

ght a

bove

the

grou

nd le

vel (

m)

100

80

60

40

20

00 1 2 3 4 5

Wind speed in m/s6 7 8 9

Table 4: Wind shear coefficientSite name Wind shear coefficientDera 0.168Hagereselam 0.042Mayderhu 0.077Mekelle University NAAshegoda 0.110Mosobo-Harena 0.163

Figure 3: Wind shear profile for the sites.


Mayderhu, Ashegoda and Mesebo-Harena sites. High turbulence (> 0.25) intensities are found

at Mekelle University and Dera sites.

3.3. Results of WAsP modeling and analysis3.3.1. Observed Wind ClimateObserved wind climate is a tabular summary of the frequency of occurrence of wind speed

versus wind direction. The time-series of wind speed and direction data were transformed into

a table which describes a time-independent summary of the conditions found at the measuring

site using the WAsP software. Figure 4 shows the results for each site based on the 10 m raw data.

Table 5: Average Turbulence intensityWind measuring Average turbulence

mast intensity10 m 30 /40 m

Dera 0.466 0.267Hagereselam 0.175 0.137Mayderhu 0.230 0.165Mekelle University 0.54 NAAshegoda 0.128 0.099Mosobo-Harena 0.176 0.136

Dera site Hagereselam site

Mayderhu site Mekelle University site

Ashegoda site Mosobo-Harena site

35.0 25.0

25.0% 0.00 u (m/s) 20.00

f(%/(m/s))

Sector: AllA: 4.4 m/sk: 2.18U: 3.92 m/sP: 64 W/m2

–– Emergent–– Fitted



f(%/(m/s))

0.020.0%0 u (m/s) 14.00

30.0Sector: AllA: 6.5 m/sk: 2.65U: 5.79 m/sP: 182 W/m2


f(%/(m/s))

0.040.0%0 u (m/s) 20.00



f(%/(m/s))

35.0

0.035.0%0 u (m/s) 20.00



f(%/(m/s))

35.0

0.030.0% 0 u (m/s) 16.00

60.0



f(%/(m/s))

0.035.0% 0 u (m/s) 14.00

Figure 4: OWC at measuring masts at 10 m a.g.l.



The figure has two parts: the wind rose indicating the wind direction distribution and the Weibull

function overlaid on the bar chart of the raw data. The Weibull parameters, the average wind

speed and power density of the sites are also shown on the side of the Weibull function plot.

The results shown as OWC are used as an input to determine the Wind Atlas in each site.

3.3.2. Wind AtlasBased on the OWC, site independent data sets of the Wind Atlas are found using WAsP

software. The results are tabulated for the five reference heights and five roughness lengths in

terms of the Weibull parameters A and k; average wind speed and power density. Table 6

shows the summary of the Wind Atlas data sets for each site.

3.3.3. Geba Catchment Resource Grid at 50 mWAsP software was employed to plot the resource grid of each zone as defined in section 2.4.

Inputs required in addition to the Wind Atlas are the vector map, roughness map and obstacle

groups around the measurement mast. All the above required data were prepared for each

zone carefully. The software then provided maps of the wind speed, power density and

ruggedness index. As a sample Dera zone is considered here, while the details of all the analysis

may be referred elsewhere [12]. The input vector map of the zone is shown in Figure 5 (a) and

maps of the ruggedness index, the wind speed and power density are shown in Figure 5 b, c, and

d respectively.

Similar analysis was conducted for all the six zones. Table 7 shows a summary of the

resource grid analysis for all the six zones.

3.3.4. Geba Catchment Wind Speed Map at 50 mWind speed resource grid files of each zone were exported and combined to get the wind

speed map of Geba catchment. The map is shown in Figure 6. The color map was done in such

a way that the change in color designates change in wind class i.e. Blue (Class 1), Cyan (Class

2), green (class 3), yellow (class 4), orange (class 5), red (class 6) and magenta (class 7).

Dera zone shows mainly class 2 and some areas of class 3 while the Mekelle University

zone, Mayderhu zone and Messobo-Harena zone show mainly class 2. Hagereselam zone and

Ashegoda zone show mainly class 3 and above with some areas reaching class 7.

4. CONCLUSIONWind data from six wind measuring masts in Geba Catchment was collected and analyzed.

Wind Atlas Analysis and Application Programme (WAsP) was used to generate the Wind

Atlas of the area and to develop the wind speed and power density maps of the catchment at

50 m above the ground level. The measured data analysis conducted indicates that the

average wind speeds at 10 m varies from 3.7 m/s to 6.64 m/s. The mean power density at 10 m

varies from 64 W/m2 to 301 W/m2. The prevailing wind directions are east and south east

directions. The wind resource map developed by WAsP at 50 m indicated that the catchment

has good wind power potential having mean wind speed and power density of 6.5 m/s and 288

W/m2, respectively.

ACKNOWLEDGEMENTThe authors would like to acknowledge the funding of the project from National Agricultural

Research Fund (NARF), Inter-University Collaboration (MU − IUC) between Flemish

Universities and Mekelle University. We would like also to acknowledge purchase of some of

the instrumentation through the NUFU project and our partner university NTNU, Norway.


Tabl

e 6:

(a)

Sum

mar

y of

Geb

a ca

tchm

ent

Win

d At

las:

Der

a,Hag

eres

elam

and

May

derh

u Si

tes

Hei

ght

Par

amet

erD

era

site

Hag

eres

elam

sit

eM

ayde

rhu

site

Rou

ghne

ss le

ngth

(m

)R

ough

ness

leng

th (

m)

Rou

ghne

ss le

ngth

(m

)0.

000.

030.

100.

200.

400.

000.

030.

100.

200.

400.

000.

030.

100.

200.

40

10.0

mW

eibu

ll A

[m/s

]7.

15.

14.

43.

93.

49.

16.

55.

65

4.4

6.8

4.9

4.2

3.8

3.3

Wei

bull

k2.

372.

12.

12.

082.

092.

52.

272.

272.

272.

273.

12.

72.

692.

682.

69M

ean

spee

d [m

/s]

6.34

4.5

3.9

3.5

3.04

8.04

5.73

4.95

4.44

3.86

6.1

4.34

3.76

3.37

2.94

Pow

er d

ensi

ty [

W/m

2 ]25

610

266

4832

502

196

127

9160

192

7549

3523

30.0

mW

eibu

ll A

[m/s

]8

6.3

5.7

5.2

4.8

10.1

87.

16.

66

7.6

65.

45

4.6

Wei

bull

k2.

462.

292.

282.

252.

242.

582.

462.

442.

432.

423.

222.

972.

932.

92.

9M

ean

spee

d [m

/s]

7.07

5.59

5.02

4.64

4.22

8.95

7.06

6.34

5.86

5.33

6.8

5.39

4.83

4.47

4.07

Pow

er d

ensi

ty [

W/m

2 ]34

518

013

110

579

678

345

251

199

150

261

135

9879

5950

.0 m

Wei

bull

A[m

/s]

8.4

76.

45.

95.

510

.68.

88

7.5

6.9

86.

76.

15.

75.

2W

eibu

ll k

2.5

2.48

2.44

2.4

2.38

2.63

2.63

2.59

2.57

2.55

3.27

3.24

3.16

3.11

3.09

Mea

n sp

eed

[m/s

]7.

466.

235.

655.

264.

849.

437.

837.

16.

636.

17.

186

5.44

5.08

4.68

Pow

er d

ensi

ty [

W/m

2 ]40

123

517

714

511

478

144

733

727

521

630

417

913

511

087

70.0

mW

eibu

ll A

[m/s

]8.

77.

66.

96.

56

119.

58.

68.

17.

58.

37.

26.

66.

25.

7W

eibu

ll k

2.47

2.67

2.61

2.55

2.52

2.62

2.81

2.75

2.72

2.68

3.24

3.5

3.38

3.32

3.28

Mea

n sp

eed

[m/s

]7.

756.

736.

135.

735.

319.

778.

437.

677.

196.

667.

466.

495.

95.

535.

13Po

wer

den

sity

[W

/m2 ]

453

282

216

179

144

873

535

410

340

272

343

218

166

138

111

100.

0 m

Wei

bull

A[m

/s]

9.1

8.3

7.6

7.1

6.6

11.5

10.3

9.4

8.8

8.2

8.7

7.9

7.2

6.8

6.3

Wei

bull

k2.

442.

632.

662.

652.

662.

592.

842.

852.

852.

833.

193.

443.

453.

453.

47M

ean

spee

d [m

/s]

8.09

7.37

6.72

6.31

5.87

10.1

79.

178.

377.

877.

337.

797.

16.

476.

085.

66Po

wer

den

sity

[W

/m2 ]

521

373

281

233

187

991

684

520

432

349

393

287

217

181

145



Tabl

e 6:

(b)

Sum

mar

y of

Geb

a ca

tchm

ent

Win

d At

las:

Mek

elle

Uni

vers

ity,

Ashe

goda

and

Mos

obo-

Har

ena

site

s.H

eigh

tP

aram

eter

Mek

elle

uni

vers

ity

site

Ash

egod

a si

teM

osob

o-ha

rena

Rou

ghne

ss le

ngth

(m

)R

ough

ness

leng

th (

m)

Rou

ghne

ss le

ngth

(m

)0.

000.

030.

200.

401.

000.

000.

030.

100.

200.

400.

000.

030.

100.

200.

4010

.0 m

Wei

bull

A[m

/s]

6.8

4.9

3.9

3.4

2.6

8.24

5.98

5.21

4.69

4.09

6.46

4.69

4.09

3.68

3.22

Wei

bull

k2.

182.

012.

011.

991.

962.

972.

712.

722.

732.

742.

242.

12.

122.

112.

12M

ean

spee

d [m

/s]

6.04

4.37

3.42

2.99

2.31

7.36

5.32

4.63

4.17

3.64

5.72

4.16

3.62

3.26

2.85

Pow

er d

ensi

ty [

W/m

2 ]23

797

4731

1534

413

791

6644

197

8053

3926

30.0

mW

eibu

ll A

[m/s

]7.

66.

15.

14.

74

9.19

7.4

6.68

6.2

5.66

7.21

5.83

5.26

4.88

4.46

Wei

bull

k2.

242.

132.

122.

092.

063.

062.

92.

892.

892.

892.

292.

212.

212.

212.

21M

ean

spee

d [m

/s]

6.74

5.42

4.53

4.14

3.51

8.21

6.6

5.96

5.53

5.04

6.39

5.16

4.66

4.32

3.95

Pow

er d

ensi

ty [

W/m

2 ]32

317

510

379

4947

125

218

614

811

326

914

610

886

6650

.0 m

Wei

bull

A[m

/s]

86.

85.

85.

44.

79.

698.

237.

57.

036.

497.

616.

495.

925.

545.

13W

eibu

ll k

2.26

2.24

2.21

2.18

2.14

3.09

3.07

3.05

3.04

3.03

2.31

2.3

2.3

2.29

2.29

Mea

n sp

eed

[m/s

]7.

116.

035.

154.

754.

148.

677.

356.

76.

285.

796.

745.

755.

254.

914.

54Po

wer

den

sity

[W

/m2 ]

376

231

145

116

7855

133

825

721

116

731

419

614

912

297

70.0

mW

eibu

ll A

[m/s

]8.

37.

46.

35.

95.

210

.07

8.87

8.12

7.64

7.1

7.9

7.01

6.42

6.04

5.62

Wei

bull

k2.

242.

352.

32.

262.

213.

073.

223.

193.

173.

152.

292.

392.

382.

362.

36M

ean

spee

d [m

/s]

7.38

6.52

5.61

5.21

4.59

97.

957.

276.

846.

357

6.22

5.69

5.35

4.98

Pow

er d

ensi

ty [

W/m

2 ]42

328

118

214

810

361

941

632

026

721

535

424

018

515

412

510

0.0

mW

eibu

ll A

[m/s

]8.

78.

17

6.5

5.8

10.5

29.

718.

98.

397.

828.

257.

677.

046.

636.

2W

eibu

ll k

2.22

2.33

2.35

2.34

2.31

3.04

3.19

3.24

3.26

3.28

2.28

2.37

2.4

2.4

2.43

Mea

n sp

eed

[m/s

]7.

717.

136.

175.

755.

129.

48.

77.

987.

527.

017.

316.

86.

245.

885.

5Po

wer

den

sity

[W

/m2 ]

485

371

238

193

138

709

547

419

351

284

405

317

242

202

164


(a) (b)

(c) (d)

1570000

1560000

1550000

1540000

1530000

1520000

1510000

540000

1570000

1560000

1550000

1540000

1530000

1520000

1510000

540000 550000 560000 570000 580000540000

1570000

1560000

1550000

1540000

1530000

1520000

1510000

550000 560000 570000 580000

7 (W/m2) 15091.91 (m/s) 11.66

550000 560000 570000 580000

0.0 (%) 28.3

Figure 5: Dera site resource grid analyses, (a) vector map b) ruggedness index (c) wind speed (d)

power density.

Table 7: Geba Catchment wind resource grid overall grid statistics Site/Zone Mean Power

speed density Weibull- Weibull-[m/s] [W/m2] A [m/s] K RIX [%] ∆RIX [%]

Dera 6.14 243 6.9 2.39 5.8 3.80Hagereselam 7.74 488 8.7 2.50 9.10 6.90Mayderhu 6.03 195 6.7 3.09 4.8 1.3Mekelle University 5.91 232 6.7 2.2 3.10 2.4Ashegoda 7.46 370 8.4 2.99 1.60 1.10Mosobo-Harena 5.71 202 6.4 228 2.60 −3.0

REFERENCES[1] Amanuel, Z. A., Assessment of spatial and temporal variability of river discharge,

sediment yield and sediment-fixed nutrient export in Geba River catchment, northern

Ethiopia [Report]. – Leuven, 2009.

[2] Mulu Bayray, et al., Wind and Solar Energy Resource Assessment in Geba Catchment,

North Ethiopia, Annual Technical Report, Department of Mechanical Engineering,

EiT – M, Mekelle University, 2010.







[5] Manwell, J. F., McGowan, J.G. and Rogers, A. L. Wind Energy Explained: Theory, Design

and Application, John Wiley & Sons Ltd., Chichester, 2002.

[6] Bruce H. Bailey and Scott L. McDonald, Wind Resource Assessment Handbook:

Fundamentals for Conducting a Successful Monitoring Program - CESTM, AWS

Scientific, Inc., New York, 1997.

[7] Wind Atlas Analysis and Application Program (WAsP), www.wasp.dk.

[8] Corbett, J.F., Ott, S., and Landberg, L., The new WAsP flow model: a fast,

linearized, Mixed Spectral Integration model applicable to complex terrain,

http://www.risoe.dk/rispubl/art/2007_124_paper.pdf.

[9] Acker, Tom, and Arshiya Hoseyni Chime, Wind modeling using WindPro and WAsP

software, Norther Arizon University, USA, 2011.



1560000

1540000

1520000

1500000

1480000

480000 500000 520000 540000 560000 580000

8.8

Wind speed(m/s)

8

7.5

7

6.4

5.6

0

Figure 6: Geba catchment wind speed map at 50 m.

[10] Ahmed Shata, A.S., and Hanitsch, R., Electricity generation and wind potential

assessment at Hurghada, Egypt, Renewable Energy, Volume 33, Issue 1, Jan. 2008,

p. 141–148.

[11] Shafiuzzaman Khan Khadem and Muhtasham Hussain, A pre-feasibility study of wind

resources in Kutubdia Island, Bangladesh, Renewable Energy, Volume 31, Issue 14,

November 2006, Pages 2329–2341.

[12] Addisu Dagne, Wind Data Analysis and Resource Mapping of Geba Catchment, M.Sc.

Thesis, Energy Technology Program, Department of Mechanical Engineering, EiT – M,

Mekelle University, 2011.


Wind Energy Data Analysis and Resource Mapping of Geba Catchment, North Ethiopia Wind Energy Data Analysis and Resource Mapping of Geba Catchment, North Ethiopia

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