2011 International Conference on Alternative Energy in Developing Countries and Emerging Economies
- 337 -
Fig. 1. Geographical distribution of 18 wind energy research stations
with buffer zones along the Gulf of Thailand and the Andaman Sea in
southern Thailand.
Guyed Mast
Tower
Foundation
Lighting Arrester
3-Cup Anemometer and
Wind Vane
Ambient Temperature
Foundation
Foundation
Data Logger
Solar Cell Panel
9 m
18 m
20 m
27 m
30 m
36 m
45 m
47 m
40 m
2.5 m
Copper Clad
Ground
Guyed Wires
3-Cup Anemometer and
Wind Vane
3-Cup Anemometer and
Wind Vane
Fig. 2. Schematic diagram of a wind energy research station.
Fig. 3. Wind speed and direction sensors, ambient temperature and a
data logger with a PV battery charging system for power supply back-
up of 18 wind energy research stations.
B. Wind Data Analysis
Wind statistics was analyzed using Weibull
distribution which is a special case of Pierson class III
distribution. In Weibull distribution, the variations in
wind velocity are characterized by the two functions: (1)
the probability density function, and (2) the cumulative
distribution function. Weibull distribution is defined by
the following equation:
1
( )
exp
k
k
k V
V
f V
c c
c
ª
º
§ ·
§ ·
«
»
¨ ¸
¨ ¸
© ¹
© ¹
«
»
¬
¼
(1)
The cumulative distribution is the integration of the
probability density function. Thus,
( ) 1 exp
k
V
F V
c
ª
º § ·
«
» ¨ ¸
© ¹
«
»
¬
¼
(2)
where
V
is the wind speed (m/s),
k
is the shape
parameter (Dimensionless), and
c
is the scale parameter
(m/s).
With a double logarithmic transformation, the Eq. (2)
can be written as
c k V k VF
ln
ln
))] ( 1ln(
ln[
(3)
Plotting
V
ln
against
))] (
1ln(
ln[
VF
should
obtained a straight line as shown in Fig 4. The slope of
line is
k
and the y-intercept is
c k
ln
.
