![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](https://image.slidesharecdn.com/finalphase-performanceevaluationofwindturbine-171027145608/85/Performance-Evaluation-of-830kW-Wind-Turbine-and-an-Analysis-of-Various-Parameters-Affecting-its-Performance-13-320.jpg)
Performance Evaluation of 830kW Wind Turbine and an Analysis of Various Parameters Affecting its Performance
- 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.