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

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

3. Power in the Wind (Watts)
= 1/2 x air density x swept rotor area x (wind speed)3  A V3
Density = P/(RxT)
P - pressure (Pa)
R - specific gas constant (287 J/kgK)
T - air temperature (K)
Area =  r2
Instantaneous Speed
(not mean speed)
kg/m3 m2
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. 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
© 2007 AWS Truewind, LLC

9. Wind Shear The change in horizontal wind speed with height
Profile
V2= 19.4 m/s
V1 = 18.4 m/s
Z2= 70 m
Z1= 50 m
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
Hub
Wind speed, and available power, generally
increase significantly with height
 = Log10 [V2/V1]
Log10 [Z2/Z1]
V2 = V1(Z2/Z1)

10. Wind Shear Calculation from Meteorological Data
 = Log10 [V2/V1]
Log10 [Z2/Z1]
V2 = V1(Z2/Z1)
70m
50m α
80m
100m
78m
75m
90m
65m
60m
55m
44m
18.8
17.9
18.9 18 19
19.1
18.1
19.512
19.2
18.2
18.4
19.4
18.5

11. Time variation of wind Velocity

12. Average wind speed
Site 1
Site 2
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.
Wind speed distribution is a critical factor in wind resource assessment.

13. Frequency distribution of Wind Velocity in a month

14. Statistical models for wind data analysis
Weibull distribution
The cumulative distribution function
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.
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. Determining k and c using least square linearisation method
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 xi and yi 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:
Ei=Energy per wind speed
f(V)i=Frequency of wind speed
Pi =Power of WIG in a wind speed

22. Wind Utilization Index (WUI)
Better Site
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

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