International Journal of Mechanical Engineering and Technology (IJMET)
Volume 10, Issue 03, March 2019, pp. 262-272. Article ID: IJMET_10_03_027
Available online at http://www.iaeme.com/ijmet/issues.asp?JType=IJMET&VType=10&IType=3
ISSN Print: 0976-6340 and ISSN Online: 0976-6359
© IAEME Publication Scopus Indexed
Helga C.A. Silubun
Department of Physics Education, Faculty of Teacher Training and Education, Musamus
University, Merauke, Indonesia
ABSTRACT
Wind Energy is a part of promising renewable energy, in this article the author studies the potential of wind energy as a source of wind power in the city of Merauke.
The method used is to analyze the probability of wind speed distribution based on primary data and secondary data using the 2 parameter weibull distribution to find out the character of the wind after that then calculate the potential of available wind energy. The results of wind speed character analysis show β form parameter > 3, the wind energy potential available in Merauke, with different elevations producing wind energy potentials ranging from 2 kW to 56.83 kW.
Keywords : Wind velocity; Weibull Distribution; Wind Energy Potential
Cite this Article Helga C.A. Silubun, Study of Wind Energy Potential as Source of
Wind Power Plant in Merauke City, International Journal of Mechanical Engineering and Technology, 10(3), 2019, pp. 262-272. http://www.iaeme.com/IJMET/issues.asp?JType=IJMET&VType=10&IType=3
Renewable energy continues to be developed in various parts of the world. Even consistently and periodically in some developed countries renewable energy is a priority to meet the need for energy amid the fossil energy crisis and the increasing population of humans (Kahraman et al., 2010; Sanders Institute, 2019). The business is the same as providing renewable sources for environmental treatment (Mangkoedihardjo and April, 2012). Similar to the Indonesian state, enterprising governments continue to study, build and develop renewable energy-based power plants as a form of government commitment, to maintain national energy security
(Silubun and Ruslau, 2018). Merauke Regency is one of the regions in Indonesia that is rich in natural resources, but there is no more specific study to map the potential of renewable energy.
Wind energy is one of the many renewable energies, besides being cheap and environmentally friendly, this energy source is a promising long-term energy source. In the article "Analysis of Wind Speed Distribution at the Mopah Airport in Merauke" (Ruslau and
http://www.iaeme.com/IJMET/index.asp 262 editor@iaeme.com

Helga C.A. Silubun
Silubun, 2018) and "The Analysis of Wind Potential Based on Wind Speed Distribution at
Lampu Satu Beach, Merauke" (Silubun and Ruslau, 2018), the author analyze the probability of wind distribution based on wind speed according to primary and secondary data using various patterns of probability distribution. Secondary data was obtained from BMKG Mopah
Merauke Station, while Primary data was a direct measurement of wind data on the Lampu
Satu Merauke Beach area. In this article the author presents a complete review of the potential of wind energy as a source of Wind power plants in the city of Merauke-Papua, using primary and secondary data ranging from wind probability analysis using a 2-parameter weibull distribution to the potential energy generated by wind speed in the City Merauke.
Renewable energy is energy produced from natural resources such as sunlight, wind, rain, tidal water and geothermal heat (Kahraman et al., 2010; Silubun and Ruslau, 2018; Boonpa and Sharp, 2015). Wind energy is caused by warming uneven sunlight over the surface of the earth, hotter air will expand to light and move upward, while cooler air will be heavier and move to occupy the area. The difference in atmospheric pressure in an area caused by differences in temperature will produce a force. The difference in pressure expressed in terms of pressure gradient is the rate of change in pressure due to differences in distance. The gradient force is the force acting in the direction of the lower pressure of the lower pressure.
The direction of the pressure gradient force in the atmosphere perpendicular to the surface of the isobars.
The average wind speed calculation begins the analysis of wind energy potential, where the average wind velocity equation is as follows:
V

 i n

1
 i n

1
V t i t i where n = the amount of measurement data t = the time of the wind blows
V = average wind speed (m/s)
V i
= measurable wind speed (m/s)
(1)
The study of wind speed probability is often done to analyze wind energy, the study of the probability of wind speed that is often used by researchers to describe the behavior or character of wind in a particular region is to use the probability of Weibull distribution. The distribution of weibull was introduced by a physicist named Walodi Weibull in 1939. In the
Weibull function variations in wind speed are described in parameters of form and scale parameters (Ruslau and Silubun, 2018).
Weibull distribution density function 2 The parameters are defined as follows:


 
 
1 exp




 

(2) http://www.iaeme.com/IJMET/index.asp 263 editor@iaeme.com

Study of Wind Energy Potential as Source of Wind Power Plant in Merauke City
Whereas, the Weibull Distribution Cumulative Distribution Function (2 Parameters) is defined as:
 
1 exp

 
 

(3)
With wind speed, α is a scale parameter and β is a form parameter. Changes in parameter values in the form of weibull distribution can model various data. If β = 1, the weibull distribution is identical to the exponential distribution, if β = 2, the weibull distribution is identical to the Rayleigh distribution, if 3 <β <4, the weibull distribution is close to the normal distribution. In analyzing wind probabilities if the β form parameter is obtained> 3, then the character of the wind in the area tends to be stable, does not experience many variations, in figure 1. we can see examples of weibull distribution flexibility.
Figure. 1 . Weibull distribution's flexibility (Weibull Distribution, Online)
The power produced on the shaft of a turbine is a transformation of kinetic energy found in the flow of wind. Wind flow that moves at a certain speed has mass m moving at the speed v absorbed by the arrangement of blades of wind turbines having kinetic energy (wind energy potential) in the following equation:

1
E mv
2
2
(Nm)
Then the flow of wind power in watts can be calculated using equation (5):
(4)
P w

1
2

Av v
2
( )( )

1
2

Av
3 where
P w
= wind power (Watt);
ρ = air density (Kg/m 3
)
A = area of a turbine cross section (m
2
) v = wind velocity (m/s)
(5) http://www.iaeme.com/IJMET/index.asp 264 editor@iaeme.com

Helga C.A. Silubun
But not all of all the power can be utilized by the turbine because of the loss of friction on the aerodynamic transmission blades (η) and the rotor power coefficient (Cp) so that the power extracted by the turbine with rotor area A is:
P

Cp
 
2
Av
3
(6)
The ideal rotor coefficient value is 59% or Cp = 0.5, this value is the Betz boundary value
(obtained from German scientist Albert Betz) This figure in theory shows the maximum efficiency that can be achieved by turbine horizotal axis rotors (Wagner, 2018 ) While the efficiency of aerodynamic transmission at the ideal maximum price

= 0,5 for blades that are very well designed on turbines and air density values

=1.204 Kg/m
2
.
Then the average wind energy potential obtained from the wind data model with relative frequency probability functions
 
is:
With
P

Cp
2


3
( )
0
(7)
 
is a weibull distribution function, so that the average wind energy potential produced is:
P

Cp
2 v t
3
(1
3

)
A


 
1

)
 3
(8)
In Figure 2, it can be seen that changes in wind speed distribution affect the output power of a power plant, in other words the greater the wind speed in a particular region the greater the potential energy produced.
Figure. 2 . Relationship between wind velocity and power output (Wagner, 2018) http://www.iaeme.com/IJMET/index.asp 265 editor@iaeme.com

Study of Wind Energy Potential as Source of Wind Power Plant in Merauke City
The wind data studied in this study were obtained from primary data and secondary data, where secondary data was obtained from measurements of the Meteorology, Climatology and
Geophysics Agency (BMKG) Merauke Mopah Station at a measurement elevation 3 meters above the surface in the Mopah Merauke Airport area, namely data in 2017 and 2018 as well as measurement data dated 21 July 2018 to 21 August 2018, secondary data for a month is used as a comparison with the Primary data obtained during the measurement month. Primary data is obtained from the results of wind speed measurements on coastal areas, namely at
Menara Pantai Lampu Satu for 1 month, starting from July 21 to August 21 in 2018, summary can be seen in table 1. Measurement of wind speed data is carried out at 3 different elevations from above ground level which is at elevations of 4 meters, 10 meters and 12 meters are presented in table 3. (Silubun and Ruslau, 2018). Secondary data average wind speed in 2017 and 2018 as measured by BMKG can be seen in table 2 and table 4.
Figure.3.
Wind speed measurement location map
Primary
Secoundary 3
(BMKG )
4
10
12
3
Table 1.
Data source and location meansurement
Data Source Elevation Meansurement Time
(meter)
1 month (21 Juli - 21 Agustus
2018)
1 month (21 Juli - 21 Agustus
2018)
1 month (21 Juli - 21 Agustus
2018)
2 Years (2017 & 2018)
1 bulan (21 July - 21 Augst)
Location
Menara Lampu Satu
Beach
Station Bandar Udara
Mopah Merauke http://www.iaeme.com/IJMET/index.asp 266 editor@iaeme.com

Helga C.A. Silubun
Table 2.
Average wind speed in Mopah BMKG station 2017 (Ruslau and Silubun, 2018)
Month
January
February
March
April
May
June
July
August
September
October
November
December
Wind velocity (m/s) min max
3
3
3
2
4
5
4
4
5
4
4
3
10
10
7
8
8
8
8
8
8
7
7
7
Average
6
5
5
4
5
6
5
6
6
5
6
5
Table 3.
Speed of average wind with different elevations (Silubun and Ruslau, 2018)
Elevation (m)
4
10
12
Average speed (m/s)
10,56
13,69
15,08
In order to find out the potential of available wind energy, the first step is analyzing the probability of wind distribution, in our article entitled "Analysis of Wind Speed Distribution at the Mopah Airport in Merauke" (Ruslau and Silubun, 2018) and "The Analysis of Wind
Potential Based on Wind Speed Distribution at Lampu Satu Beach, Merauke "(Silubun and
Ruslau, 2018) previously discussed the probability of Wind Speed distribution with various distributions, the sample can be seen in Figure 4.
(a)
Figure.4. (a) Probability with a variety of 2017 BMKG Wind Speed Data Distribution Models
(b)
(b) Probability with a variety of distribution models for measurement data at a 10 meter elevation. http://www.iaeme.com/IJMET/index.asp 267 editor@iaeme.com

Study of Wind Energy Potential as Source of Wind Power Plant in Merauke City
But in this article, to analyze the character of the wind available in the city of Merauke, the author only uses the probability of weibull distribution to obtain scale parameters and form meters using equations (2) and (3) on secondary and primary data after which calculating wind energy potential average using equation (8).
Wind speeds that occur in the Merauke Mopah Airport area vary from time to time. A description of the wind speeds recorded during 2017 at BMKG is presented in table 2. shows that minimum wind speeds occur in April and maximum wind speeds occur in January and
February. However, on average, at the 99% significance level, the average wind speed each month ranges from 5.2 to 5.5 m / s (Ruslau and Silubun, 2018). Description of wind speed during 2018 is not much different from the previous year the wind speed is quite varied, presented in table 4. Showing that the maximum wind speed occurs in January and
September, the minimum wind speed occurs in April while the average speed ranges from 5, 2 to 5.6 m/s.
The results of the analysis of wind speed distribution during 2018 are presented in Figure
5. And the analysis of wind speed distribution results of measurement data for 1 month on the
Beach tower of one merauke lamp is presented in Figure 6.
Table 4.
Average wind speed in Mopah BMKG Station 2018
Month
January
February
March
April
May
June
July
August
September
October
November
December min
3
4
3
3
4
4
4
3
5
4
4
3
Wind velocity (m/s) max
12
7 average
4,9
4,6
8
7
8
8
5,2
4,8
5,6
5,4
8
8
7
7
8
10
5,7
5,5
6,5
5,9
5,8
4,5
Figure.5.
Wind speed distribution year 2018 http://www.iaeme.com/IJMET/index.asp 268 editor@iaeme.com

Helga C.A. Silubun
Figure.6.
Wind speed distribution at 3 different elevations
Furthermore, the results of pattern analysis and wind speed distribution characteristics in the city of Merauke using a weibull distribution for secondary and primary data are presented in Figure 7(a-b). Figure 7(a) is the result of a 2-parameter weibull distribution analysis using secondary data, ie data from BMKG station Mopah measurements for 2 years, 2017 & 2018 at an elevation of 3 meters above ground level, and sample images from the analysis of weibull distribution for data The primary measurement on the Pantai Lampu Satu Tower can be seen in Figure 7(b) measurement of wind speed at an elevation of 12 meters above ground level, more detailed data is presented in table 5.
Probability Density Function
0.12
0.08
0.2
0.16
0.04
0.28
0.24
0.36
0.32
0
2 3 4 5 6 8 9 10 11 12 7 x
Histogram Weibull
Figure.7(a).
Weibull Distribution 2 Parameters of Wind Speed data during 2017 and 2018 http://www.iaeme.com/IJMET/index.asp 269 editor@iaeme.com

Study of Wind Energy Potential as Source of Wind Power Plant in Merauke City
Probability Density Function
0.48
0.44
0.4
0.36
0.32
0.28
0.08
0.04
0
0.24
0.2
0.16
0.12
10 20 30 70 80 90 100 40 50 x
Histogram Weibull
60
Figure.7(b).
Weibull Distribution 2 Data parameters Wind Speed Measurement for 1 month at 12 meters elevation
Table 5.
Value of Average Speed and Weibull Parameters
Data Source
Primary
Secoundary
Elevation
(meter)
4
10
12
3 length of measurement
1 month
1 month
1 month
2 Years average
(m/s)
10,56
13,69
15,08
5,38
Weibull Parameters

β
4,15
1,99
2,41
5,35
11,58
15,42
16,97
5,82
3 1 bulan 5,59 4,96 6,02
The results of wind distribution analysis show that the average wind speed at an altitude of 3 meters above ground level in the area of Mopah Airport Station in 2017 and 2018 is 5.38 m / s, has a scale parameter of 5.35 and a form parameter of 5, 82, and the average wind speed data for one month of measurement, from July to August 2018 of 5.59 m / s, has a scale parameter of 4.96 and a form parameter of 6.02. Analysis of distribution in the Pantai Lampu
Satu Tower area, the average wind speed value at each different elevation is presented in table
3, and the value of the disbursed analysis results is shown in table 5, where at 4 meters elevation produces a scale parameter of 4.15 and form parameters at 11.59, at 10 meters elevation produces a scale parameter of 1.99 and its shape parameter is 15.42, at a 12 meter levitation it produces a scale parameter of 15.08 and its shape parameter is 16.97. The β form parameter values obtained based on the results of the wind speed analysis on the area of
Mopah Airport Station and on the Pantai Menara Lampu Satu area have a value greater than 3
(β> 3) which states that the available wind character is very stable or does not have many variations .
The potential of electrical energy produced varies with elevation and place of measurement, in the coastal area wind distribution is bigger and more stable when compared to Mopah airport area so that it has an impact on the potential of electricity produced, the results can be seen in table 6. At the Airport Station area Mopah with measurements of wind speed at an elevation of 3 meters above ground level produces approximately 2 kW of electrical energy while in the coastal area, namely the one-light Beach Tower, at 4 meters elevation produces electricity of 19.34 kW, at an elevation of 10 meters producing energy electricity is 42.45 kW and at 12 meters elevation produces electrical energy of 56.83 kW. http://www.iaeme.com/IJMET/index.asp 270 editor@iaeme.com

Helga C.A. Silubun
Table 6.
Value of potential wind energy
Data Source
Primary
Secoundary
(BMKG )
Elevation
(meter)
4
10
12
3
3 length of measurement
1 month
1 month
1 month
2 Years
1 bulan
Potential wind energy (KW)
19,34
42,45
56,83
2,44
2,76
5. CONCLUSION
Based on the analysis of the probability of wind speed distribution in the area of Mopah
Airport and Coastal Satu Pantai Merauke, the character of the available wind is very stable and does not experience many variations. It can be seen from the form parameter values β> 3 and the calculation results of potential wind energy obtained from 2 kW to with 56.83 kW at different elevations, this states that the potential of available wind energy is large enough to be considered for planning the construction of Wind Power Plants in Merauke City.
Acknowledgments are conveyed to the Ministry of Research, Technology and Education of the High Repoblik Indonesia, which has supported this research through the Simlitabmas PDP
Grant. And Musamus University Merauke has funded the publication of this article.
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