# teknik pengukuran potensi energi angin malik ibrochim bidang konversi energi dirgantara lapan

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TEKNIK PENGUKURAN POTENSI ENERGI ANGIN Malik Ibrochim Bidang Konversi Energi Dirgantara LAPAN Slide 2 WIND CAUSED Wind is caused by differences in pressure. When a difference in pressure exists, the air is accelerated from higher to lower pressure difference in 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]friction [1] Slide 3 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 060217 Slide 4 12.1 About This Presentation 12.1.1 Anemometers 12.1.2 Wind Data Processing 12.1.3 Site Wind Variations 12.1.4 Wind Power 12.1.5 Wind Energy 060221 Slide 5 12.1.1 Anemometers Anemometers measure the speed and direction of the wind as a function of time Spinning cups or propeller Ultrasonic reflection (Doppler) Sodar (Sound detection and ranging with a large horn) Radar Drift balloons Etc. 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 loss 070212 Slide 6 PERALATAN UKUR POTENSI ENERGI ANGIN Slide 7 DIAGRAM ALUR PENENTUAN KECEPATAN ANGIN DAN DURASI OPTIMUM Slide 8 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 070212 Slide 9 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 070212 Slide 10 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 years 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 070212 Slide 11 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 030224 Slide 12 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 070212 http://www.windpower.dk/tour/wres/weibull.htm Usually its a little windy, sometimes its 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 Slide 13 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 060217 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 Slide 14 TINJAUAN PUSTAKA: c = 1,12 *KEC.ANGIN RATA-RATA( 1,5 k 4 ) h = f(u)t/2( Jam ) PARAMETER WEIBULL Slide 15 Bulan Nilai Kecepatan Angin (m/det) Lama waktu Pengamatan (menit) Parameter Weibull MaksMinc k Januari3,316,41,10,944630 3,4 4,0 Februari3,216,81,10,940310 Maret3,417,21,20,944630 April2,919,90,90,843190 Mei2,814,10,70,844630 Juni2,815,30,90,843190 Juli3,216,80,90,844630 Agustus3,318,71,10,944630 September3,115,71,10,943190 Oktober2,819,11,00,944630 Nopember2,715,70,90,843190 Desember3,018,30,8 25915 Rata-rata3,017,00,8 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 KURVAKURVA Slide 16 V (bin)Weibull f(u) %V 0081.07171E-11 0.50.1722590568.51.77527E-15 11.41630369295.07713E-20 1.54.7350294219.52.02061E-25 210.50552351108.90153E-32 2.517.4797479610.53.40761E-39 322.25466583118.78194E-48 3.521.1282101111.51.16493E-57 414.17556315126.00147E-69 4.56.24006694612.58.94095E-82 51.644628952132.83041E-96 5.50.23318977113.51.3806E-112 60.01575195147.4242E-131 6.50.00044267814.53.1079E-151 74.45845E-06157.0565E-174 7.51.36769E-0815.55.974E-199 81.07171E-11161.2793E-226 16.54.6382E-257 171.8803E-290 FUNGSI PROBABILITAS WEIBULL UNTUK MASING-MASING KEC.ANGIN GRAFIKGRAFIK Slide 17 KURVA PROBABILITAS DISTRIBUSI WEIBULL vs KECEPATAN ANGIN TABEL WEIBULL Slide 18 GRAFIK DURASI Slide 19 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) 070212 Slide 20 TERIMA KASIH