MATLAB-Based Wind Data Analyzer to
Evaluate the Wind Potential of the Given Site
Shamkumar Iti #1, Prof.Uttam S. Satpute #2.
[email protected]
#1Department of Electrical and Electronics Engineering, KLS’s VDRIT, Haliyal-581329, India.
Abstract—In the present scenario, Wind Power Plants are
becoming the prominent, alternate source of energy for meeting
the increasing demand of electricity. A number of Wind Power
Plants have been installed in various regions of India. For the
investor, before installing Wind Plant at a particular site, it is
essential to know whether an adequate wind potential at that
particular site is available or not. This paper presents a
MATLAB-based simulation scheme for the evaluation of wind
potential at the given site. The monthly and yearly average
values of the wind speeds are calculated by the cubic mean root,
instead of commonly used arithmetic mean. The wind speeds are
statistically modeled using the Weibull probability density
function. The developed algorithm is executed for the wind data
obtained from the Data Acquisition System installed at B. E.C.
Bagalkot , Karnataka, India as a case study.
Index Terms—Matlab, Site matching, Wind Potential,
Weibull factor.
I. INTRODUCTION
Since a few decades, India is facing the problem of power
crisis, due to increasing demand of electricity. Meeting this
demand from the conventional power plants such as Hydroelectrical, Thermal or Nuclear, presents many disadvantages
such as environmental pollution and more cost etc Moreover,
the fossil fuels are depleting day-by-day. So to meet the
demand, alternate sources of energy are developing. Wind
Power Plant is one of the prominent sources. The Wind Plant
constitutes an aerodynamic-based rotor blade, which extracts
the energy from the wind and drives the generator through the
transmission link, for the generation of electricity.
A number of Wind Power Plants have been installed at
various regions of India. Experiences with the existing wind
plants have shown that some of the wind plants have failed
completely or are performing poorly because the installed
wind turbine generator systems do not optimally match the
characteristics of the site. Here, the characteristics of the site,
particularly refers to the mean wind speed that is available for
the particular period of time, i.e. month or year. A study
conducted in areas in Chennai [1] revealed that 106 of 177
wind mills in eight states in India were located in areas with
inadequate wind speeds. So before installing a wind power
plant at a particular site, it is essential to know whether an
adequate wind speed is available or not. In regard of this, in
this paper a MATLAB-based simulation scheme is being
developed to evaluate, the wind statistical parameters such as
mean, standard deviation, and variance on monthly and
yearly basis for the given yearly wind data measured at the
particular site. The developed algorithm also evaluates
Weibull statistical model to determine the wind speed of
highest probability.
The monthly or yearly mean is calculated by using cubic
mean of wind speed, instead of arithmetic mean as per the
reference [2].
Furthermore, the developed algorithm is executed for the
wind data, obtained from the Data Acquisition System
installed at B.E.C. Bagalkot, Karnataka, India as a case study.
II. METHODOLOGY
A. Problem formulation
1) Mean:
The average value of the wind speeds is computed by the
following equation,
Error! Reference source not found.
(1)
Where, Error! Reference source not found. is the wind
velocity (Error! Reference source not found..
Error! Reference source not found. is the frequency
of occurrence of wind speed.
Error! Reference source not found. =1 for
arithmetic mean.
Error! Reference source not found. =2 for root
mean.
Error! Reference source not found. =3 for cubic
mean.
B. Simulation Algorithm
The simulation procedure is divided into two parts.
2) Standard Deviation:
The Standard Deviation is referred as the deviation from
the mean. For the given wind data it is computed by the
following equation,
Error!
(2)
Reference
source
not
found.
3) Variance:
The variance is calculated by the following equation,
Variance=Error!
(3)
Reference
source
not
found..
1) Procedure-1
2) Procedure-2
1) Procedure-1:
The flow chart of this procedure is shown in Fig. 1. The
wind data recorded at the particular site is stored in one the
drive in the proper format. The computational task extracts
the wind data of each day and calculates the mean, standard
deviation and variance as per equations (1) – (3). Then, the
mean, standard deviation, variance and Weibull Probability
function of each month is calculated from the means of every
day and saved the results in proper format. Then, the mean,
standard deviation, variance and Weibull probability density
function of the year is calculated from the means of every
month and saved the results in proper format.
4) Probability Density Function:
The continuously changing wind speeds can be described
by statistical models. There are various probability density
functions defined in the literature, but the two important are,
i.
ii.
Weibull Probability Density Function.
Rayleigh Probability Density Function.
The Rayleigh Probability Density Function is an
approximation to the Weibull Probability Density Function
and it is less accurate. So Weibull Probability Density
Function is used in this paper. It is defined as follows,
Error!
(4)
Reference
source
not
found.
Start
Extracts the wind data of each day of the
year and calculate the mean, standard
deviation and variance.
Calculate the mean, standard deviation,
variance and p.d.f. of the every month of
the year, from the means of the individual
days of every month and save the results.
Where, k is the shape parameter, given by,
c is the scale parameter, given by
Calculate the mean, standard deviation,
variance and p.d.f. of the of the year, from
the means of very month and save the
results.
Wind data analysis of July month
14
End
at 50m heigth
12
at 30m heigth
8
(m/s)
2) Procedure-2:
at 10m heigth
10
wind velocity (m/s)
Fig. 1. Flow chart of the Procedure-1.
6
The flow chart of this procedure is shown in Fig. 2.
4
2
1 2 3 4 5 6 7 8 9 10111213141516171819202122232425262728293031
Start
Days of the month
Fig.3. Wind data results of July month.
Read the user requirement as, Monthly or
yearly analysis is required, if month read
the particular month.
Probability density of wind speeds in July month
0.11
at 50m height
Depending on the requirement open and
display the files which were stored in
procedure-1.
Probability of wind speed
0.1
at 30m height
0.09
at 10m height
0.08
0.07
0.06
0.05
End
0.04
2
3
4
5
6
7
8
9
10
11
12
Wind speed (m/s))
Fig. 2. Flow chart of Procedure-2.
Procedure-2 reads the user requirement such as whether
monthly or yearly wind data analysis and the particular
month. Depending on the requirement, the files are opened
and displayed.
III.
Simulation results and Discussions
The code is written in the MATLAB – 7.5 environments
and is executed for the wind data obtained from the Data
Acquisition System installed at B.E.C. Bagalkot, Karnataka,
India. The analyzing results of July and August months are
presented in Fig. 3, Fig. 4, Fig. 5, Fig. 6, Table. I. and Table.
II. Fig. 3. shows the variation of wind speed with respect to
days of July month. The wind speeds at 50m height are
higher as compared to that at 30m and 10m. Fig. 4. shows the
probabilities of mean wind velocities of individual day.
Fig.4. Probability densities of wind speed of July month
TABLE. I. MEAN, STANDARD DEVIATION AND VARIANCE DATA
OF JULY MONTH.
Height
(m)
50
Mean (m/s)
S.D.(m/s)
8.061
3.496
Variance
(m²/s²).
12.22
30
10
7.710
6.484
3.72
3.29
13.848
10.833
Wind data analysis of August month
12
at 50m height
The mean, standard deviation and variance of July month are
given in the Table. I.
11
at 30m height
wind velocity (m/s)
10
at 10m height
9
8
(m/s)
7
6
5
The mean wind speed at a particular height suggests the
Wind Turbine Generator that is suitable for optimal
generation.
4
3
2
1
Fig. 5. shows the variation of wind speed with respect to
days of August month. The wind speeds at 50m height are
higher as compared to that at 30m and 10m. Fig. 6. shows the
probabilities of mean wind velocities of individual day. The
mean, standard deviation and variance of July month are
given in the Table. II.
2
3
4
5
6
7
8
9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31
Days of the month
IV. CONCLUSION
Fig.5. Wind data results of August month.
0.2
2
Probability density of wind speeds in August
month
at 50m
height
at 30m
height
at 10m
height
0.
2
Probability of wind speed
The MATLAB-based simulation scheme, developed in
this paper can be effectively used to find the wind potential at
any given site. The results provide, the complete illustration
of the wind statistics i.e. mean, standard deviation, variance
and probability of the wind speeds on monthly and yearly
basis, which can be utilized for the optimal selection of the
wind machine model. Furthermore, the results can be
effectively used in the wind plant design.
0.1
8
0.1
6
0.1
4
V. ACKNOWLEDGEMENT
0.1
2
The author extends his sincere thanks to Dr. Suresh H.
Jangamshetti, Bavasweshwar Engineering College Bagalkot,
Bagalkot, India for his valuable suggestions and providing
the wind data.
0.
1
0.0
8
0.0
6
0.0
4 2
3
4
5
6
7
8
9
1
1
1
0
1
2
Wind speed
(m/s)
Fig.6. Probability densities of wind speeds of August month
VI.
REFERENCES
[1] Thomas Bellarmine and Joe Urquhart,: “Wind Energy for the 1990s and
Beyond”, Energy Conversion and Management (Elsevier), vol.37, No. 12,
1996, pp.1741-1752.
[2] Suresh H. Jangamshetti, Ran V. G.,“Optimal Siting of wind
generators”,energy conversion, IEEE Trans. vol. 16, issue 1, march 2001.
TABLE. II. MEAN, STANDARD DEVIATION AND VARIANCE DATA
OF AUGUST MONTH.
Height (m)
Mean (m/s)
S.D.(m/s)
50
7.801
2.174
Variance
(m²/s²).
4.729
30
10
7.457
6.199
2.20
1.90
4.843
3.626
[3] Rudra Pratap,”Introduction to MATLAB”, vol. 1. New York: Wiley,
1950, p. 1-100.
[4] Johnson, Gary L. “Wind Energy System”, Prentice Hall Inc., Englewood
Cliffs, NJ 07632 1985.
VII. BIOGRAPHIES
Mr. Uttam S. Sapute was born in
Belgaum, Karnataka, India on 25 May
1978. He obtained B.E. (Electrical and
Electronics)
from
Karnataka
University Dharwad, India in 2000. He
has completed M-Tech from B.E.C.
Bagalkot, Karnataks, India. Presently
pursuing
Ph.D.
from
V.T.U.
Belagaum, Karnataka, India in power
system. His area of interest includes
Computer Techniques in Power
System,
Applications
of
DSP
Processors in Power system etc.
Mr. Shamkumar.Y.Iti was born
in Hubli, Karnataka, India on 20
January 1989. He obtained B.E
(Electrical
from
and
V.D.R.I.T,
Electronics)
Haliyal,
Karnataka, India in 2011. He
has completed M-Tech from
V.D.R.I.T, Haliyal, Karnataka,
India from V.T.U, Belgaum in
Industrial Electronics in 2014.
`