HW4TK

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Oregon State University, Corvallis *

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530

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Electrical Engineering

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Dec 6, 2023

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pdf

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Uploaded by CoachHerring1955

Table of Contents Homework Wind 4 .............................................................................................................................. 1 Run initialization ................................................................................................................................. 2 Part 1 ................................................................................................................................................ 2 Part 2 ................................................................................................................................................ 3 Part 3 ................................................................................................................................................ 3 Part 4 ................................................................................................................................................ 3 Homework Wind 4 Tzu-Hao Kuo % Your assignment is: % % 1) Scatter plot the wind turbine power vs the wind speed to create the % wind turbine power curve. Is it as expected? % % 2) Plot u, blade pitch and Cp vs time. What relationship do you see % between blade pitch and the coefficient of power? % % 3) Integrate Pgen to get the energy over the simulation time. Then % calculate the capacity factor. There are at least two ways to do this: % 1) Within the simulation, simply use an integrator block to integrate % power, and log that signal. To calculate average power, just divide that % energy by the simulation time, which could be done within the simulation, % or in post-processing; 2) When the simulation is complete, access the % saved power signal and use the "trapz" command to integrate: % trapz(time,data). Again, divide by the total time to get the average % power. % % 4) Try three values of generator rating: 100 kW, 80 kW, and 120 kW. For % each case, list the capacity factor, and the total generator energy % produced. Which case has the highest capacity factor, and which case has % the highest energy produced? (For this task, create a loop that redefines % Pgen_rated each time through the loop and runs the simulation.) (Note: % when you calculate the capacity factor, you may very well get values % quite a bit different than the typical 1/3 we discussed in class. % Remember that the typical value of 1/3 is for a wind turbine installed in % a typical location over a year. We are running a simulation over only 24 % hours. So if that happens to be an especially windy 24 hour period, we % will certainly calculate a 24 hour capacity factor that is very different % than 1/3. If we ran the simulation over an entire year, with realistic % wind data, we should expect to get something around 1/3.) % % "Publish" this file when have completed items #1 through #4. I started #1 % for you. % % Tip: In the Model Settings dialog, and Data Import/Export pane, if % "Single simulation output" is checked, then logsout is saved within that % structure, in "out.logsout". If "Single simulation output" is not % checked, then "logsout" is saved directly. 1

Run initialization microgrid_y22s_step4_init open_system( 'microgrid_y22s_step4' ) % Includes image of block diagram in publish sim( 'microgrid_y22s_step4' ) % Run simulation Part 1 figure plot( ... logsout.getElement( 'u0' ).Values.Data, ... logsout.getElement( 'Pgen' ).Values.Data, ... 'o' ) xlabel( 'wind speed (m/s)' ) ylabel( 'Power (W)' ) legend( 'generator power vs speed' ) % Power increases when wind speed increased. % Power remains at a maximum value when power reach the value. % It is same as expect. 2

Part 2 figure subplot(311) hold on % plot wind speed plot( ... logsout.getElement( 'u0' ).Values.Data, ... 'o' ) % plot a constant line: the wind speed at the boundary of region 2 and 3 line([0,9e5],[9.7,9.7]); hold off xlabel( 'time (s)' ) ylabel( 'm/s' ) legend( 'Wind speed' , 'Region 2 and 3 boundary' ) subplot(312) % plot blade pitch plot( ... logsout.getElement( 'bladepitch' ).Values.Data, ... 'o' ) xlabel( 'time (s)' ) ylabel( 'degrees' ) legend( 'Blade angle' ) subplot(313) % plot Cp plot( ... logsout.getElement( 'Cp' ).Values.Data, ... 'o' ) xlabel( 'time (s)' ) ylabel( 'Cp' ) legend( 'Coefficient of power vs time' ) % The power coefficient is the highest in region 2. % In region 3, the blade pitch increased when the wind speed increased. % In order to maintain the power in the constant value. Part 3 %Integrate Pgen to get the energy over the simulation time P_generated_int=trapz(tout,logsout.getElement( 'Pgen' ).Values.Data) % calculate the capacity factor P_generated_avg=P_generated_int/simu.endTime capacity_factor=P_generated_avg/wt.Pgen_rated Part 4 for g_rating =[80000 100000 120000] % CP_new = P_generated_avg/g_rating % when the value of generator rating change, this is new capacity factor E_generated = g_rating*tout; E_generated_int =trapz(tout,E_generated) end 3

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% When generator rating is 80KW, the capacity factor is the highest, it is % 0.8784 % when generator rating is 120KW, the energy produced is the highest,it is % 4.4790e+14 CP_new = 0.8784 E_generated_int = 2.9860e+14 CP_new = 0.7027 E_generated_int = 3.7325e+14 CP_new = 0.5856 E_generated_int = 4.4790e+14 Published with MATLAB® R2022a 4

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