TEKNIK PENGUKURAN POTENSI ENERGI ANGIN
Malik Ibrochim
Bidang Konversi Energi DirgantaraLAPAN
WIND CAUSED
Wind is caused by differences in pressure. When a difference in pressure exists, the air is accelerated from
higher to lower pressure
Near the Earth's surface, friction causes the wind to be slower than it would be otherwise. Surface friction also
causes winds to blow more inward into low pressure areas.[1]
Overview: Wind
Wind speed measurements provide local data to estimate wind power available– “Local” means where the turbine will stand
Wind power/energy computations yield estimates of energy available at the anemometer
Statistical processing is required to estimate accurately for the long term
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12.1 About This Presentation
12.1.1 Anemometers12.1.2 Wind Data Processing12.1.3 Site Wind Variations12.1.4 Wind Power12.1.5 Wind Energy
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12.1.1 Anemometers Anemometers measure the speed and direction of
the wind as a function of timeSpinning cups or propeller
Ultrasonic reflection (Doppler)Sodar (Sound detection and ranging with a large
horn)Radar
Drift balloonsEtc.
Wind data are usually collected at ten-minute rate and averaged for recording
Gust studies are occasionally used, and require sampling at a higher rate to avoid significant
information loss (4 pts/gust) Spectral analysis indicates the frequency components of the wind structure and permits sampling frequency selection to minimize loss070212
PERALATAN UKUR POTENSI ENERGI ANGIN
DIAGRAM ALUR PENENTUAN KECEPATAN ANGIN DAN DURASI
OPTIMUM
Anemometer
AKUISISI DATA
MEMORY CARD
DATA MENTAH
PENGOLAHAN DATA
TABULASI DATAKURVA DAN
GRAFIK
12.1.2 Wind Data Processing
Serial data from a datalogger must be validated to detect errors, omissions, or equipment malfunctions
These data are usually produced in a text (.TXT) format Specialized computer codes may read the data or an export function
used to produce a txt output file Statistical analysis is used to detect anomalies, peaks and nulls (lulls in
wind jargon), and determine the distribution of the speeds and directions
Frequency analysis with the Fast Fourier Transform (FFT) will show where the energy lies and its probability
Cepstral analysis shows the periodicities Graphic analysis displays the results for visual interpretation
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12.1.3.1 Local Site Wind Availability
Once a region of persistent winds is located, an area of interest is defined by local reconnaissance, land inquiries made, etc.
Since trees act to block the wind or cause turbulence, a distance to the nearest tree of less than 200-300 feet will significantly impact the free wind
A wind rose for that area will define the principal directions of arrival; seek local advice as to storm history as well; look for flagging of vegetation
Place an anemometer or small temporary turbine about 20 ft away from the intended tower site so that the anemometer can be retained there when the main turbine is installed; choose the direction of least likely wind
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12.1.3.2 Wind Variation
Since wind velocity (speed and direction) varies over a year and over many years, long-term data are required
The velocities may be estimated using one year’s data or climate (long-term weather data) may be obtained from climate agencies
While wind direction varies, most wind turbines will track in azimuth (yaw) to maximize the energy extracted, and wind arrival direction knowledge is more important in determining upwind blockage or obstruction
The wind speed, average, one-minute gust, and extreme, is sufficient for most energy assessment purposes
The top 30% of the wind speed regime will provide ~70% of the energy
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12.1.3.3 Wind Speed Variation
In a time series of wind speed data, there will be many different values of speed
For convenience, the speeds are usually divided into “bins”, or ranges of speed, e.g., 0-1 mph, 1+ to 4 mph, . . . , 60-65 mph, etc.
The ranges vary, but since there are many samples in a year, there can be many ranges in the process
The number of samples that fall within a bin can be plotted as a histogram versus the wind speed ranges
A line drawn through the top of the histogram bars approximates a continuous function that is similar to a Weibull Function, or in a more simple case, a Rayleigh Function
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12.1.3.3 Wind Speed Variation
This Weibull probability curve shows the variation for a site with a 6.5 m/s mean wind and a shape factor of 2; the higher the factor, the more peaked or pointed
Notice that the mean is not the most common; that is the mode, and the median is in the middle of the data
The shape factor of 2.0 reveals that this is the Rayleigh probability as well
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http://www.windpower.dk/tour/wres/weibull.htm Usually it’s a little windy, sometimes it’s calm, and in storms, the wind blows hard
but not for long A probability curve (p.d.f.) is
just a way to express this mathematically
If the wind values are integrated, a distribution
curve results
12.1.4.1 Wind Speed Power Density
Not all wind power can be extracted or wind would stop The Betz Limit of 59.3% is the theoretical maximum Turbines approach 40% from the rotor, but the mechanical and electrical
losses may take 20% of the rotor output
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http://www.windpower.dk/tour/wres/powdensi.htm Grey = total power Blue = useable power Red = turbine power
output 0 to 25 m/s on abscissa
TINJAUAN PUSTAKA:
c = 1,12 *KEC.ANGIN RATA-RATA ( 1,5 ≤ k ≤ 4 )
h = f(u)t/2 ( Jam )
PARAMETER WEIBULL
uBulan
Nilai Kecepatan Angin (m/det)
σ
Lama waktu Pengamatan
(menit)
Parameter Weibull
Maks Min c k
Januari 3,3 16,4 1,1 0,9 44630
3,4 4,0
Februari 3,2 16,8 1,1 0,9 40310
Maret 3,4 17,2 1,2 0,9 44630
April 2,9 19,9 0,9 0,8 43190
Mei 2,8 14,1 0,7 0,8 44630
Juni 2,8 15,3 0,9 0,8 43190
Juli 3,2 16,8 0,9 0,8 44630
Agustus 3,3 18,7 1,1 0,9 44630
September 3,1 15,7 1,1 0,9 43190
Oktober 2,8 19,1 1,0 0,9 44630
Nopember 2,7 15,7 0,9 0,8 43190
Desember 3,0 18,3 0,8 0,8 25915
Rata-rata 3,0 17,0 0,8 0,8
Total lama waktu pengamatan (menit) 506765
Nilai kecepatan angin rata-rata, standar deviasi , lamanya waktu pengamatan dan nilai parameter distribusi Weibull c dan k untuk wilayah Palu Sulawesi Tengah
dengan ketinggian 30 meter
HASIL DAN PEMBAHASAN
KURVA
V (bin) Weibull f(u) % V Weibull f(u) %
0 0 8 1.07171E-11
0.5 0.172259056 8.5 1.77527E-15
1 1.416303692 9 5.07713E-20
1.5 4.735029421 9.5 2.02061E-25
2 10.50552351 10 8.90153E-32
2.5 17.47974796 10.5 3.40761E-39
3 22.25466583 11 8.78194E-48
3.5 21.12821011 11.5 1.16493E-57
4 14.17556315 12 6.00147E-69
4.5 6.240066946 12.5 8.94095E-82
5 1.644628952 13 2.83041E-96
5.5 0.233189771 13.5 1.3806E-112
6 0.01575195 14 7.4242E-131
6.5 0.000442678 14.5 3.1079E-151
7 4.45845E-06 15 7.0565E-174
7.5 1.36769E-08 15.5 5.974E-199
8 1.07171E-11 16 1.2793E-226
16.5 4.6382E-257
17 1.8803E-290
FUNGSI PROBABILITAS WEIBULL UNTUK MASING-MASING KEC.ANGIN
GRAFIK
0
5
10
15
20
25
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
Kecepatan Angin (meter/ detik)
Fu
ng
si
Dis
trib
usi
Weib
ull
(%
)
KURVA PROBABILITAS DISTRIBUSI WEIBULL vs KECEPATAN ANGIN
TABEL WEIBULL
GRAFIK DURASI
0
400
800
1200
1600
2000
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
Kecepatan Angin ( meter / det )
Du
rasi (J
am
)
12.1 Conclusion: Wind Theory The theory of wind energy is based upon fluid flow, so it also applies
to water turbines (832 times the density) While anemometers provide wind speed and usually direction, data
processing converts the raw data into usable information Because of the surface drag layer of the atmosphere, placing the
anemometer at a “standard” height of 10 meters above the ground is important; airport anemometer heights often historically differ from 10 meters
For turbine placement, the anemometer should be at turbine hub height
The average of the speeds is not the same as the correct average of the speed cubes!
The energy extracted by a turbine is the summation of (each speed cubed times the time that it persisted)
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TERIMA KASIH