Presentation is loading. Please wait.

Presentation is loading. Please wait.

Wind Resource Assessment S.R.Mohanrajan Amrita Wind Energy Centre Department of Electrical and Electronics Engineering Amrita Vishwa Vidyapeetham.

Similar presentations


Presentation on theme: "Wind Resource Assessment S.R.Mohanrajan Amrita Wind Energy Centre Department of Electrical and Electronics Engineering Amrita Vishwa Vidyapeetham."— Presentation transcript:

1 Wind Resource Assessment S.R.Mohanrajan Amrita Wind Energy Centre Department of Electrical and Electronics Engineering Amrita Vishwa Vidyapeetham

2 Wind Resource Assessment 1.Wind Turbine – Power in the Wind – Power Curve 2.Prospecting for Wind Farm – Tree Flagging – Nearby weather stations 3.Preparation of Meteorological data – Wind Speed – Air density( Pressure, temperature) – Wind Direction 4.Estimation of Annual Energy Production – Wind Shear form Meteorological data – Wind Regime Modeling 5.Calculate Utilization Index Met Mast

3 Power in the Wind (Watts) Density = P/(RxT) P - pressure (Pa) R - specific gas constant (287 J/kgK) T - air temperature (K) = 1/2 x air density x swept rotor area x (wind speed) 3  A V3V3 Area =  r 2 Instantaneous Speed (not mean speed) kg/m 3 m2m2 m/s

4 Power in the Wind Wind Speed – Wind energy increases with the cube of the wind speed – 10% increase in wind speed translates into 30% more electricity – 2X the wind speed translates into 8X the electricity Height – Wind energy increases with height to the 1/7 power – 2X the height translates into 10.4% more electricity Air density – Wind energy increases proportionally with air density – Humid climates have greater air density than dry climates – Lower elevations have greater air density than higher elevations – Wind energy in Denver about 6% less than at sea level Blade swept area – Wind energy increases proportionally with swept area of the blades Blades are shaped like airplane wings – 10% increase in swept diameter translates into 21% greater swept area – Longest blades up to 413 feet in diameter Resulting in 600 foot total height Betz Limit – Theoretical maximum energy extraction from wind = 16/27 = 59.3% – Undisturbed wind velocity reduced by 1/3 – Albert Betz (1928)

5 Wind Turbine Spec. Rated Cut-in

6 Location for Wind Turbine

7 Climatic data form Meteorological Mast

8 © 2007 AWS Truewind, LLC Typical Met Mast Heights up to 120 m Tubular pole supported by guy wires Installed in ~ 2 days without foundation using 4-5 people Solar powered; cellular data communications

9 Wind Shear The change in horizontal wind speed with height A function of wind speed, surface roughness (may vary with wind direction), and atmospheric stability (changes from day to night) Wind shear exponents are higher at low wind speeds, above rough surfaces, and during stable conditions Typical exponent (  ) values: – : water/beach – : gently rolling farmland – : forests/mountains  = Log 10 [V 2 /V 1 ] Log 10 [Z 2 /Z 1 ] Wind Shear Profile Wind Shear Profile V 2 = 19.4 m/s V 1 = 18.4 m/s Z 2 = 70 m Z 1 = 50 m V 2 = V 1 (Z 2 /Z 1 )  Wind speed, and available power, generally increase significantly with height Hub

10 Wind Shear Calculation from Meteorological Data  = Log 10 [V 2 /V 1 ] Log 10 [Z 2 /Z 1 ] V 2 = V 1 (Z 2 /Z 1 )  70m50m α 80m100m78m75m90m65m60m55m44m

11 Time variation of wind Velocity

12 Average wind speed Wind speed distribution is a critical factor in wind resource assessment. Site 2 Site 1 Site 1 will generate power throughout the day with 15m/s wind speed. Site 2 the turbine will be idle throughout the day as the velocity is 30 m/s.

13 Frequency distribution of Wind Velocity in a month

14 Statistical models for wind data analysis Weibull distribution 1.The cumulative distribution function 2.The probability density function where, V = wind speed in m/s k = dimensionless weibull shape parameter c=weibull scale parameter in m/s

15 Statistical models for wind data analysis Probability functions were fitted with the field data to identify suitable statistical distributions for representing wind regimes.

16 Weibull probability density function for c =8 m/s. c c c c c c Weibull probability density function for k= 2

17 Methods for determining Weibull parameters k and c Least square linearisation method Standard Deviation method World Meteorological Organisation (WMO) method Justus approximation method Maximum likelihood method Graphical method

18 Determining k and c using least square linearisation method

19 By equating eqn(5) and eqn(6) We get,

20 Determining k and c using least square linearisation method Where x and y are the mean values of x i and y i respectively and w is the total number of pairs of values available. Then the Weibull parameters are,

21 Annual Energy Production Frequency of wind speed Annual Energy Production Where: E i =Energy per wind speed f(V) i =Frequency of wind speed P i =Power of WIG in a wind speed

22 Wind Utilization Index (WUI) WUI is an index of site- machine matching Varies from 20% – 40% (study on 110 sites) The higher is WUI the lower is cost of generation Better Site

23 References: Wind Energy - Gerhard J. Gerdes Wind Energy Fundamentals, Resource Analysis and Economics- Sathyajith Mathew Wind Energy-Cy Harbourt

24


Download ppt "Wind Resource Assessment S.R.Mohanrajan Amrita Wind Energy Centre Department of Electrical and Electronics Engineering Amrita Vishwa Vidyapeetham."

Similar presentations


Ads by Google