The project aimed at 1) Understanding the performance of wind power project of 830KW 2) Determining system reliability (Grid availability, Machine availability, System availability) and operating hours of the wind conversion system from the data obtained at site 3) Analyzing the effect of various parameters like velocity, blade length, temperature, pressure, air density on the power generation of a wind turbine 4) Forecasting or Predicting the performance of the wind turbine generators based on the above parameters This analysis can be used to existing sites which are nearby the above evaluated wind power project for Maximizing power generation It helps us to understand effect of various parameters viz. air density, air pressure, air temperature, blade length, velocity on the power generation According to the results, there is a high effect of air characteristics on the mechanical power. The environment’s parameter has a massive effect on the generated power, which will lead the researchers to concentrate on it with highest priority Complete one year data was used for the analysis of the wind power project Results were executed using Matlab

1. PERFORMANCE EVALUATION
OF 830kWWINDTURBINE &
ANALYSIS OFVARIOUS
PARAMETER EFFECT
Presented by
RAFIK AHMED 2GI11ME055
RAJESH TALAWAR 2GI11ME058
ROHAN RAIBAGKAR 2GI11ME064
MOHIT BENADIKAR 2GI08ME048
Under the guidance of
Prof. S.H.KULKARNI
Batch No. 05

2. INTRODUCTION
 The world primary energy demand projections
according to International Energy Agency (IEA) Report,
world primary energy demand increases by 1.6 percent
per year on an average between 2006 and 2030,
registering a growth of 45 percent.The demand for oil
may rise to 106mb/d in 2030.
 Renewable resources are growing faster, overtaking oil
and gas and have become the largest source of energy. It
is assumed that India and China may account for nearly
half of incremental energy demand by 2030.

3. LITERATURE SURVEY
 The utilization of wind energy for power generation
purposes is occupying a great share in the electricity
market worldwide and becoming increasingly attractive
 With the increasing shortage in fossil fuels, and
pollution problems renewable energy has become an
important energy source.Among the other renewable
energy sources wind energy has proven to be one of
the most economical one.

4. S No. Districts Total wind
power in
MW
Capacity
allocated in
MW
No. of wind
farm developer
Capacity
commissioned
in MW
No. of
independent
power producer
1 Bagalkot 643.8 293.2 9 0 0
2 Bangaluru (Rural) 205.4 180.13 5 0 0
3 Belagavi 1414 1118.95 58 153.2 17
4 Bellary 700.9 573.45 34 141.1 37
5 Bidar 80.85 79.5 6 0 0
6 Bijapur 795 521.6 18 0 0
7 Chamrajnaar 67.58 92.5 8 0 0
8 Chikmagalur 702.4 666.4 27 0 0
9 Chitradurga 1300 1289.01 84 510.2 265
10 Mangaluru 221.1 107.75 7 0 0
11 Davanagere 544.4 613.4 36 298 66
12 Dharwad 450.7 321.85 17 0 0
13 Gadag 1102 981.985 57 468 236
14 Gulburga 400.2 200.6 10 0 0
15 Hasan 650.5 577.42 19 98.75 50
16 Haveri 685.3 199.26 8 0 0
17 Kolar 300.6 297.46 8 0 0
18 Koppal 474.7 93.35 11 14.17 9
19 Madikere 101 96.95 6 0.55 1
20 Mandya 70.6 65.8 4 0 0
21 Mysore 300.7 254.3 13 0 0
22 Raichur 408.4 236.5 12 7.5 5
23 Shimoga 700.4 593.98 23 27 16
24 Tumkur 500.5 428.5 20 60.8 24
25 Udupi 47.38 0 0 0 0
26 Karwar 367.8 29.9 3 0 0
Total 13236 9913.745 503 1779 726

5. OBJECTIVES OFTHE PROJECT
 To understand the performance of the wind power project.
 To determine system reliability (Grid, Machine & System
Availability) of the wind energy conversion system
 To analyze the effect of various parameters like velocity, blade
length, temperature, pressure, air density on the power
generation of a wind turbine.
 To forecast or predict the performance of the wind turbine
generators based on the above parameters.

6. Mathematical Model
We know that kinetic energy of a mass in motions is:
E =
1
2
× 𝑚 × 𝑣2
Also,
m = 𝜌 × 𝑉
As
V = 𝐴 × 𝑙
∴ m = 𝜌 × 𝐴 × 𝑙
Hence Energy becomes, E =
1
2
× 𝜌 × 𝐴 × 𝑙 × 𝑣2
As,
Pw =
𝐸
𝑡
∴ Pw =
1
2
×𝜌×𝐴×𝑙×𝑣2
𝑡
as, v =
𝑙
𝑡
∴ 𝑊𝑖𝑛𝑑 𝑃𝑜𝑤𝑒𝑟, Pw =
1
2
× 𝜌 × 𝐴 × 𝑣3
∴ 𝑀𝑒𝑐ℎ𝑎𝑛𝑖𝑐𝑎𝑙 𝑃𝑜𝑤𝑒𝑟, P =
1
2
× 𝜌 × 𝐴 × 𝑣3
× 𝐶 𝑝

7. Betz Limit
Out of 59.3% of wind energy extracted only 41% is actually
converted into electricity.

8. Technical specification of wind turbine
Rated Power 830kW
Cut-in power 4 m/s
Rated wind speed 12 m/s
Cut-out power 28 m/s
Blade Length 26.5 m
Swept Area 2206.18 m2
No. of blades 3

9. Power generation for the 830kW wind turbine
Month Wind
Speed
(m/s)
T
(K)
P
(Pa)
Air Density
(Kg/m3)
Swept Area
(m2)
Power
(with Betz Limit)
(KW)
Power
(KW)
Jan 5 295 101300 1.20 2206.18 97.32 64.33
Feb 4 296 101000 1.19 2206.18 49.51 25.18
Mar 4 299 101000 1.18 2206.18 49.02 24.92
Apr 5 301 100800 1.17 2206.18 94.91 62.74
May 6 300 100700 1.17 2206.18 164.39 118.41
June 11 299 100600 1.17 2206.18 1015.34 619.53
July 15 296 100600 1.18 2206.18 2600.67 925.66
Aug 11 296 100700 1.19 2206.18 1026.65 626.43
Sept 8 297 100800 1.18 2206.18 393.98 280.46
Oct 5 297 101000 1.18 2206.18 96.38 63.71
Nov 5 295 101200 1.20 2206.18 97.22 64.27
Dec 5 294 101200 1.20 2206.18 97.55 64.49

10.  The power generation report containing energy generation, generation
hours, grid OK hours was collected fromWind World India Pvt Ltd.,
Saundatti, Belagavi. Machine shutdown / break down and various were also
considered.
Capacity Factor, CF =
Energy Generated
Energy Available
Machine availability (percent) =
Operating Hours
Grid OK hours
Grid availability (percent) =
Grid OK hours
Total available hours
System Availability (percent) = Machine availability × Grid availability

11. Annual energy generation, machine availability and grid availability with capacity
factor of the wind power project at wind site (CLPSN-01) - 2014
Month
Energy
Generated
(kWh)
Capacity
Factor
Generation
Hours
Operating
Hours
Grid OK
Hours
Machine
Availability
Grid
Availability
System
Availability
Jan 96351 16.18 668 730 743.15 98.23 99.89 98.12
Feb 52766 9.82 562 670.95 671.42 99.93 99.92 99.85
Mar 67381 11.32 624 742.26 742.26 100 99.77 99.77
Apr 53452 9.28 591 693.62 701.62 98.86 97.47 96.36
May 82236 13.82 673 737.92 737.92 100 99.18 99.18
June 178708 31.02 691 702.42 703.40 99.86 97.7 97.56
July 218711 36.74 729 729.52 738.23 98.82 99.23 98.06
Aug 121617 20.43 675 719.97 733.09 98.21 98.56 96.80
Sept 119286 20.71 692 717.15 717.29 99.98 99.63 99.61
Oct 57621 9.68 664 723.78 744.02 97.28 100 97.28
Nov 75359 13.08 688 718.43 718.43 100 99.78 99.78
Dec 68954 11.58 677 731.43 731.43 100 98.31 98.31
Total 1192442 7934 8617.45 8682.27
Avg 16.97 99.26 99.12 98.39

12. Annual energy generation, machine availability and grid availability with
capacity factor of the wind power project at wind site (CLPSN-05) - 2014
Month
Energy
Generated
(kWh)
Capacity
Factor
Generation
Hours
Operating
Hours
Grid OK
Hours
Machine
Availability
Grid
Availability
System
Availability
Jan 120347 20.21 682 734.88 740.13 99.29 99.48 98.77
Feb 77499 14.41 555 647.5 670.29 96.6 99.75 96.36
Mar 91579 15.38 635 470.75 471.69 99.8 99.77 99.57
Apr 73833 12.81 613 709.87 709.87 100 98.59 98.59
May 103572 17.4 670 731.38 734.83 99.53 98.78 98.32
June 242859 42.16 688 694.93 695.35 99.94 96.58 96.52
July 308970 51.91 737 738.37 742.83 99.4 99.8 99.20
Aug 190422 31.99 703 738.77 738.84 99.99 99.31 99.30
Sept 160276 27.82 689 711.33 717.07 99.2 99.59 98.79
Oct 73745 12.38 677 727.33 738.93 98.43 99.33 97.77
Nov 93897 16.3 687 712.7 712.70 100 98.99 98.99
Dec 72828 12.23 567 615.67 729.38 84.41 98.34 83.01
Total 1609827 7903 8233.48 8401.92
Avg 22.92 98.05 99.03 97.09

13. v=4:1:30;
cp=[0, 0.39, 0.425, 0.425, 0.425, 0.42, 0.4, 0.385, 0.355,.32 , 0.275, 0.223,
0.182, 0.15, 0.125, 0.105, 0.089, .076, 0.0656, 0.057, 0.05, 0.044, 0.038, 0.033,
0.028, 0.024, 0.02];
vq=v.^3;
k=cp.*vq;
p1=753.98*0.593*k;
p2=1178.09*0.593*k;
p3=1696.46*0.593*k;
%p1=753.98*0.593.*cp*v.^3;
%p2=1178.09*0.593.*cp*64;
%p3=1696.46*0.593.*cp*64;
plot(v,p1,v,p2,v,p3);
xlabel('Wind speed(m/s)');
ylabel('power(watt)');
title('Blade length effect of mechanical power');
MATLAB Program

14. Power coefficient as a function with wind speed
Power Coefficient,
Cp=
Power output of turbine
Power available in the wind

15. The effect of blade length on mechanical power
From this figure it can be seen that as the blade length is increased from 20m to
30m, the mechanical power increased from approximately 178.844KW to
402.4KW at wind speed of 10m/s

16. Mechanical power versus the blade length
(wind speed=10m/sec)

17. The effect of air density on mechanical power
From figure its clear that as air density increases from 1kg/m3 to 1.3kg/m3 the
available power also increases from 261.652KW to 340.147KW at wind speed of
10m/s

18. The effect of air pressure on mechanical power
As pressure increases from 0.8bar to 1.1bar, power increases from 243.336KW
to 334.914KW at wind speed 10m/s.

19. The effect of temperature on mechanical power
As the temperature increases from 250C to 450C, power decreases from
303.516KW to 285.199KW at wind speed of 10m/s.

20. Pollutant Avoided due to Installation of WECS in Belagavi Cluster
(Saundatti Site)
Emissions avoided
compared to (coal power
plant)
Quantum of emission
avoided per day
Total emission avoided during 20
years life of WECS (considering
seven month working of WECS in a
year)
CO2
(19 ton/day/MW)
1368 ton 5.74 million ton
SO2
(136 kg/day/MW)
9.79 ton 0.0411 million ton
Fly ash
(7 ton/day/MW)
504 ton 2.11 million ton
Particulate matter
(60 kg/day/MW)
4.32 ton 0.0181 million ton

21. Outcome & Conclusion
0
50000
100000
150000
200000
250000
70
75
80
85
90
95
100
Jan Feb Mar Apr May June July Aug Sept Oct Nov Dec
MachineAvailability%
GridAvailabity%
Month
Energy(kWh)
Energy Generated
Machine
Grid
CLPSN-01

22. 0
50000
100000
150000
200000
250000
300000
350000
70
75
80
85
90
95
100
Jan Feb Mar Apr May June July Aug Sept Oct Nov Dec
MachineAvailability%
GridAvailability%
Month
Energy(kWh)
Energy Generated
Machine Availability
Grid Availability
CLPSN-05

23. 0.00
100.00
200.00
300.00
400.00
500.00
600.00
700.00
800.00
900.00
1000.00
0
2
4
6
8
10
12
14
16
Jan Feb Mar Apr May June July Aug Sept Oct Nov Dec
WindSpeed(m/s)
Month
ActualPower(kW)
Wind Speed
Actual Power
Effect of Wind Speed in Power Output

24. 0.00
100.00
200.00
300.00
400.00
500.00
600.00
700.00
800.00
900.00
1000.00
1.15
1.16
1.17
1.18
1.19
1.20
1.21
Jan Feb Mar Apr May June July Aug Sept Oct Nov Dec
AirDensity(kg/m3)
Month
ActualPower(kW)
Air Density
Actual Power
Effect of Air Density in Power Output

25.  This analysis was undertaken to understand the
performance of the wind power project. Complete
one year data was used for the analysis of the wind
power project.
 This analysis mainly emphasizes on the system energy
generation, system reliability of the wind energy
conversion system.
 Energy generation of the system was also affected due
to the availability of the operation.These are mainly
machine availability, grid availability and the system
availability.
 The capacity factor for CLPSN-01 Machine was found
to be 16.97 percent.

26.  The capacity factor for CLPSN-05 Machine was
found to be 22.92 percent.
 According to the results, there is a high effect of
air characteristics on the mechanical power.
 The environment’s parameter has a massive
effect on the generated power, which will lead the
researchers to concentrate on it with highest
priority.

27. REFERENCES
 Salih Mohammed Salih, Mohammed QasimTaha, Mohammed K Alawsaj,
“International Journal of ENERGY AND ENVIRONMENT” volume 3, Issue
6, 2012 pp.895-904.
 V P Khambalkar, D S Karale, S R Gadge,“Performance evaluation of a 2 MW
wind power project” volume 17, no. 4
 C. Marimuthu,V. Kirubakaran,“A critical review of factors affecting wind
turbine and solar cell system power production” E-ISSN2249–8974
 kidwind science snack,“Understanding Coefficient of Power (Cp) and Betz
Limit”
 International journal of renewable energy research, IJRER.Vol. 5 2015.
 India Renewable Energy Status Report, Green Summit, May 2014.