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