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Kindly help me correct this code to function properly. I am just learning MATLAB. i cannot get the output in abc frame. This is the code:
%----------- Define input and state parameters-----------------------------
clc
v_dc = 350; % DC input voltage in V
m = 0.841; % modulation index
C = 4000e-6; % DC buss capacitance in uf
L_1 = 2.5e-3; % Inverter side inductance in mH
L_2 = 2.5e-3; % Load side inductance in mH
L = 0; % load inductance
C_f = 10e-6; % filter capacitance in uf
R_f = 0.7; % damping resistance in ohms
R_L = 20; % load resistance in ohms
f_s = 10e3; % switching frequency
f = 60; % System frequency
R_s = 0.01; % Capacitance of the DC circuit
I_d = 8.594; % steady state current
w = 2*pi*f; % System angular Frequency
% Define initial steady state values
v_c = 349.4; i_d = 8.594; i_q = -0.213; v_df = 285; v_qf = -120; i_Ld = 8.594; i_Lq = 0.85;
%------------------S V P W M Generator-------------------------------------
% Define reference vector Uref
U_mag = m*v_dc/2; % Magnitude of Uref
% Define switching vectors
U1 = [v_dc/2;0]; % Vector Q1
U2 = [v_dc/4;sqrt(3)*v_dc/4]; % Vector Q2
U3 = [-v_dc/4;sqrt(3)*v_dc/4]; % Vector Q3
U4 = [-v_dc/2;0]; % Vector Q4
U5 = [-v_dc/4;-sqrt(3)*v_dc/4]; % Vector Q5
U6 = [v_dc/4;-sqrt(3)*v_dc/4]; % Vector Q6
% Define sector angles
theta1 = pi/6;
theta2 = pi/2;
theta3 = 5*pi/6;
theta4 = 7*pi/6;
theta5 = 3*pi/2;
theta6 = 11*pi/6;
% Define duty cycles for each switch using a for loop
for t=0:1/f_s:1/f % Time variable from 0 to one cycle of system frequency with steps of switching frequency
U_phase = w*t; % Phase of Uref (t is time variable)
U_alpha = U_mag*cos(U_phase); % Alpha component of Uref
U_beta = U_mag*sin(U_phase); % Beta component of Uref
if (0 <= U_phase) && (U_phase < theta1) % Sector 1
T1 = (sqrt(3)*U_beta + U_alpha)/(2*v_dc);
T2 = (-sqrt(3)*U_beta + U_alpha)/(2*v_dc);
T0 = 1 - T1 - T2;
d_a(round(t)+1) = T1 + T0/2;
d_b(round(t)+1) = T2 + T0/2;
d_c(round(t)+1) = T0/2;
elseif (theta1 <= U_phase) && (U_phase < theta2) % Sector 2
T3 = (sqrt(3)*U_beta - U_alpha)/(2*v_dc);
T2 = (sqrt(3)*U_beta + U_alpha)/(2*v_dc);
T0 = 1 - T3 - T2;
d_a(round(t)+1) = T0/2;
d_b(round(t)+1) = T2 + T0/2;
d_c(round(t)+1) = T3 + T0/2;
elseif (theta2 <= U_phase) && (U_phase < theta3) % Sector 3
T3 = (sqrt(3)*U_beta - U_alpha)/(2*v_dc);
T4 = (-sqrt(3)*U_beta - U_alpha)/(2*v_dc);
T0 = 1 - T3 - T4;
d_a(round(t)+1) = T0/2;
d_b(round(t)+1) = T0/2;
d_c(round(t)+1) = T3 + T0/2;
elseif (theta3 <= U_phase) && (U_phase < theta4) % Sector 4
T5 = (-sqrt(3)*U_beta + U_alpha)/(2*v_dc);
T4 = (-sqrt(3)*U_beta - U_alpha)/(2*v_dc);
T0 = 1 - T5 - T4;
d_a(round(t)+1) = T5 + T0/2;
d_b(round(t)+1) = T0/2;
d_c(round(t)+1) = T4 + T0/2;
elseif (theta4 <= U_phase) && (U_phase < theta5) % Sector 5
T5 = (-sqrt(3)*U_beta + U_alpha)/(2*v_dc);
T6 = (sqrt(3)*U_beta + U_alpha)/(2*v_dc);
T0 = 1 - T5 - T6;
d_a(round(t)+1) = T5 + T0/2;
d_b(round(t)+1) = T6 + T0/2;
d_c(round(t)+1) = T0/2;
elseif (theta5 <= U_phase) && (U_phase < theta6) % Sector 6
T1 = (sqrt(3)*U_beta + U_alpha)/(2*v_dc);
T6 = (sqrt(3)*U_beta - U_alpha)/(2*v_dc);
T0 = 1 - T1 - T6;
d_a(round(t)+1) = T1 + T0/2;
d_b(round(t)+1) = T0/2;
d_c(round(t)+1) = T6 + T0/2;
end
end
%-------------------------Define system matrices---------------------------
% Create Three-phase SVPWM VSI Inverter
% System matrix Nx-by-Nx matrix
A = [-1/(C*R_s),-sqrt(3)*m/(2*C),0,0,0,0,0;
sqrt(3)*m/(3*L_1),-R_f/(3*L_1),w,-1/(2*L_1),-sqrt(3)/(6*L_1),-R_f/(3*L_1),0;
0,-w,-R_f/(3*L_1),-sqrt(3)/(6*L_1),-1/(2*L_1),0,R_f/(3*L_1);
0,1/(2*C_f),-sqrt(3)/(6*C_f),0,w,-1/(2*C_f),sqrt(3)/(6*C_f);
0,sqrt(3)/(6*C_f),1/(2*C_f),-w,0,-sqrt(3)/(6*C_f),-1/(2*C_f);
0,R_f/(3*(L_2+L)),0,1/(2*(L_2+L)),sqrt(3)/(6*(L_2+L)),((-3*R_L-R_f)/(3*(L_2+L))),w;
0, 0, R_f/(3*(L_2+L)), -sqrt(3)/(6*(L_2+L)), 1/(2*(L_2+L)), -w, ((-3*R_L-R_f)/(3*(L_2+L)))];
% Define input matrix
B = [1/(C*R_s),-sqrt(3)*i_d/(2*C);d_a*v_dc,(sqrt(3)*v_c)/L_1;d_b*v_dc,0;d_c*v_dc,0;0,0;0,0;0,0]; % Nx-by-Nu input matrix
% Define output matrix
C = [0 1 0 0 0 0 0; % Ny-by-Nx matrix
0 0 1 0 0 0 0;
0 0 0 1 0 0 0;
0 0 0 0 1 0 0;
0 0 0 0 0 1 0;
0 0 0 0 0 0 1];
% Feedthrough matrix
D = zeros(6, 2); % Ny-by-Nu matrix
% create state-space model object
sys = ss(A,B,C,D);
% Define initial conditions and input
x0 = [v_c; i_d; i_q; v_df; v_qf; i_Ld; i_Lq]; % Initial state vector
t = 0:1e-6:0.5; % Time vector for simulation
u = repmat([v_dc;m],1,length(t)); % repeat u for each time step
% Simulate the system
[y, ~, x] = lsim(sys, u, t, x0);
% Extract the states
v_c_sim = x(:, 1);
i_d_sim = x(:, 2);
i_q_sim = x(:, 3);
v_df_sim = x(:, 4);
v_qf_sim = x(:, 5);
i_Ld_sim = x(:, 6);
i_Lq_sim = x(:, 7);
% Extract the outputs
v_abc_sim = y(:, 1:3);
i_abc_sim = y(:, 4:6);
v_dq_sim = y(:, 4:5);
i_dq_sim = y(:, 2:3);
% Plot the variables
figure;
subplot(4, 2, 1);
plot(t, v_c_sim);
xlabel('Time');
ylabel('v_c');
title('Capacitor Voltage');
subplot(4, 2, 2);
plot(t, i_d_sim);
xlabel('Time');
ylabel('i_d');
title('d-Axis Current');
subplot(4, 2, 3);
plot(t, i_q_sim);
xlabel('Time');
ylabel('i_q');
title('q-Axis Current');
subplot(4, 2, 4);
plot(t, v_df_sim);
xlabel('Time');
ylabel('v_df');
title('d-Component Filter Voltage');
subplot(4, 2, 5);
plot(t, v_qf_sim);
xlabel('Time');
ylabel('v_qf');
title('q-Component Filter Voltage');
subplot(4, 2, 6);
plot(t, i_Ld_sim);
xlabel('Time');
ylabel('i_Ld');
title('d-Axis Load Current');
subplot(4, 2, 7);
plot(t, i_Lq_sim);
xlabel('Time');
ylabel('i_Lq');
title('q-Axis Load Current');
% Perform coordinate transformation from dq frame to abc frame for currents
i_a_sim = cos(w*t)*i_d_sim - sin(w*t)*i_q_sim;
i_b_sim = cos(w*t - 2*pi/3)*i_d_sim - sin(w*t - 2*pi/3)*i_q_sim;
i_c_sim = cos(w*t + 2*pi/3)*i_d_sim - sin(w*t + 2*pi/3)*i_q_sim;
% Perform coordinate transformation from dq frame to abc frame for voltages
v_a_sim = cos(w*t)*v_df_sim - sin(w*t)*v_qf_sim;
v_b_sim = cos(w*t - 2*pi/3)*v_df_sim - sin(w*t - 2*pi/3)*v_qf_sim;
v_c_sim = cos(w*t + 2*pi/3)*v_df_sim - sin(w*t + 2*pi/3)*v_qf_sim;
Many thanks
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I am trying to do image to image regression neural network training. In place of digitTrain4DArrayData, I have used my own dataset of 500 input and 500 output grayscale images of size 100*100 pixels for training.
The code is as follows:
imds1 = datastore(fullfile(matlabdrive,"T10itTI"),"IncludeSubFolders",true,...
"FileExtensions",".png","type","image");
imds2 = datastore(fullfile(matlabdrive,"TFiSTI"),"IncludeSubFolders",true,...
"FileExtensions",".png","type","image");
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imageInputLayer([100 100 1])
batchNormalizationLayer
reluLayer('Name','relu_1')
convolution2dLayer(3,1,'Stride',1)
batchNormalizationLayer
reluLayer('Name','relu_2')
convolution2dLayer(3,1,'Stride',1)
batchNormalizationLayer
reluLayer('Name','relu_3')
convolution2dLayer(3,1,'Stride',1)
batchNormalizationLayer
reluLayer('Name','relu_4')
convolution2dLayer(1,1,'Stride',1)
batchNormalizationLayer
reluLayer('Name','relu_5')
convolution2dLayer(3,1,'Stride',2)
batchNormalizationLayer
reluLayer('Name','relu_6')
convolution2dLayer(3,1,'Stride',1)
batchNormalizationLayer
reluLayer('Name','relu_7')
convolution2dLayer(3,1,'Stride',1)
batchNormalizationLayer
reluLayer('Name','relu_8')
convolution2dLayer(3,1,'Stride',1)
batchNormalizationLayer
reluLayer('Name','relu_9')
convolution2dLayer(3,1,'Stride',1)
batchNormalizationLayer
reluLayer('Name','relu_10')
convolution2dLayer(3,1,'Stride',1)
batchNormalizationLayer
reluLayer('Name','relu_11')
convolution2dLayer(3,1,'Stride',1)
batchNormalizationLayer
reluLayer('Name','relu_12')
convolution2dLayer(1,1,'Stride',1)
batchNormalizationLayer
reluLayer('Name','relu_13')
transposedConv2dLayer(1,1,'Stride',2)
batchNormalizationLayer
reluLayer('Name','relu_14')
convolution2dLayer(1,1,'Stride',1)
batchNormalizationLayer
reluLayer('Name','relu_15')
convolution2dLayer(1,1,'Stride',1)
batchNormalizationLayer
reluLayer('Name','relu_16')
convolution2dLayer(3,1,'Stride',1)
batchNormalizationLayer
reluLayer('Name','relu_17')
convolution2dLayer(1,1,'Stride',1)
batchNormalizationLayer
reluLayer('Name','relu_18')
transposedConv2dLayer(3,1,'Stride',2)
fullyConnectedLayer(1)
regressionLayer
]
lgraph = layerGraph(layers);
options = trainingOptions("adam", ...
InitialLearnRate=8e-3, ...
SquaredGradientDecayFactor=0.99, ...
MaxEpochs=20, ...
MiniBatchSize=64, ...
Plots="training-progress")
net=trainNetwork(dsnew, lgraph, options);
On running the code, I get following error:
Error using trainNetwork Invalid training data. The output size ([1 1 1]) of the last layer does not match the response size ([1 1 1972224]).
When I use fullyConnectedLayer(1972224) in place of fullyConnectedLayer(1), I get following error:
Error using trainNetwork Layer 'fc': Invalid initializer. Requested 1972224x17161 (252.2GB) array exceeds maximum array size preference (5.0GB). This might cause MATLAB to become unresponsive.
Error in mt1tt1 (line 95) net=trainNetwork(dsnew, lgraph, options);
Caused by: Error using nnet.internal.cnn.assembler.InitializeMixin/initializeLearnableParameters Layer 'fc': Invalid initializer. Requested 1972224x17161 (252.2GB) array exceeds maximum array size preference (5.0GB). This might cause MATLAB to become unresponsive.
Can you suggest correction in code?
Thankyou
Hi Fellow Users,
I am Muthuserpi From A begginer of Optics studio Please anyone help me how to add two diffeaction reflective grating and How to tilt and decenter in matlab simulation
Thanks And Regards
S.Muthuserpi
Hello Community,
For E vehicle application to control the speed and torque of PMSM motor I am implementing FOC algorithm. We know that to arrive at Speed set point we have speed control loop which is outer loop, Further PI CONTROLLER of speed loop will give reference for Iq current which regulates the current using current loop. My question is 1.How to arrive at Torque set point?
2. Is speed control using FOC is enough or we should implement Torque control algorithm also?
3. should we have speed mode and Torque mode selection in case of constant speed requirement during cruise mode and high torque requirement during uphill situation respectively
4. To arrive at Torque set point what is the relation or transfer function to tune PI controller for Torque loop?
Please give your valuable inputs and answers, thanks in advance
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