I’ve defined a half-wavelength antenna, and I use the current function in Matlab Antenna Toolbox to get the value of the current over the entire antenna, which is discrete point by point. I found that the current function in Matlab Antenna Toolbox can divide the antenna into several points with different densities according to the physical meaning of the antenna, but I don’t know the inner method of this division. Then I change the position of the feedpoint in this dipole. Each time I change the position of the feedpoint, the number of points divided by the current function changes. But I want to store the current value of each feedpoint in a matrix, so I want the number of points can be manually determined. However, I did not find such an operation in Matlab. I could only change the mesh size of maxEdgeLength, but I could not determine the number of points. Is there a similar operation in Matlab?
ant = dipole(‘Length’, L, ‘Width’, 10e-4*L); % Define length and width
maxEdgeLength = L / 60;
mesh(ant, ‘MaxEdgeLength’, maxEdgeLength);
[currentData, points] = current(ant, f);I’ve defined a half-wavelength antenna, and I use the current function in Matlab Antenna Toolbox to get the value of the current over the entire antenna, which is discrete point by point. I found that the current function in Matlab Antenna Toolbox can divide the antenna into several points with different densities according to the physical meaning of the antenna, but I don’t know the inner method of this division. Then I change the position of the feedpoint in this dipole. Each time I change the position of the feedpoint, the number of points divided by the current function changes. But I want to store the current value of each feedpoint in a matrix, so I want the number of points can be manually determined. However, I did not find such an operation in Matlab. I could only change the mesh size of maxEdgeLength, but I could not determine the number of points. Is there a similar operation in Matlab?
ant = dipole(‘Length’, L, ‘Width’, 10e-4*L); % Define length and width
maxEdgeLength = L / 60;
mesh(ant, ‘MaxEdgeLength’, maxEdgeLength);
[currentData, points] = current(ant, f); I’ve defined a half-wavelength antenna, and I use the current function in Matlab Antenna Toolbox to get the value of the current over the entire antenna, which is discrete point by point. I found that the current function in Matlab Antenna Toolbox can divide the antenna into several points with different densities according to the physical meaning of the antenna, but I don’t know the inner method of this division. Then I change the position of the feedpoint in this dipole. Each time I change the position of the feedpoint, the number of points divided by the current function changes. But I want to store the current value of each feedpoint in a matrix, so I want the number of points can be manually determined. However, I did not find such an operation in Matlab. I could only change the mesh size of maxEdgeLength, but I could not determine the number of points. Is there a similar operation in Matlab?
ant = dipole(‘Length’, L, ‘Width’, 10e-4*L); % Define length and width
maxEdgeLength = L / 60;
mesh(ant, ‘MaxEdgeLength’, maxEdgeLength);
[currentData, points] = current(ant, f); antenna toolbox, current MATLAB Answers — New Questions

​

I’m running Matlab 2014b and I need to install Matlab and borrow one of our floating licenses onto a PC which is not allowed to connect to the internet or intranet.

As far as I can determine, in order to use the license borrowing feature, the PC initially has to be connected to the network. This is not an option for me.

Is there any way to transfer a borrowed license onto a PC without network access – like using a memory stick or similar?I’m running Matlab 2014b and I need to install Matlab and borrow one of our floating licenses onto a PC which is not allowed to connect to the internet or intranet.

As far as I can determine, in order to use the license borrowing feature, the PC initially has to be connected to the network. This is not an option for me.

Is there any way to transfer a borrowed license onto a PC without network access – like using a memory stick or similar? I’m running Matlab 2014b and I need to install Matlab and borrow one of our floating licenses onto a PC which is not allowed to connect to the internet or intranet.

As far as I can determine, in order to use the license borrowing feature, the PC initially has to be connected to the network. This is not an option for me.

Is there any way to transfer a borrowed license onto a PC without network access – like using a memory stick or similar? license borrowing, no network MATLAB Answers — New Questions

​

Hello All,
I have an input tiff file image that has 342 slices, and I would like to extract each slice individually and save as a separate file.
% x = imread("inputTiffFile.tif", 100);
% imwrite(x, "slice_100.tif");

% x = imread("inputTiffFile.tif", 163);
% imwrite(x, "slice_163.tif");
However, I have one problem: The voxel spacing in the input image is [1, 1, 1], but the voxel spacing after using ‘imwrite’ is changed to [0.352778, 0.352778, 1]. I require the voxel spacing to be same as input.

Any inputs on how to avoid this change in voxel size would be very helpful.
Thanks,Hello All,
I have an input tiff file image that has 342 slices, and I would like to extract each slice individually and save as a separate file.
% x = imread("inputTiffFile.tif", 100);
% imwrite(x, "slice_100.tif");

% x = imread("inputTiffFile.tif", 163);
% imwrite(x, "slice_163.tif");
However, I have one problem: The voxel spacing in the input image is [1, 1, 1], but the voxel spacing after using ‘imwrite’ is changed to [0.352778, 0.352778, 1]. I require the voxel spacing to be same as input.

Any inputs on how to avoid this change in voxel size would be very helpful.
Thanks, Hello All,
I have an input tiff file image that has 342 slices, and I would like to extract each slice individually and save as a separate file.
% x = imread("inputTiffFile.tif", 100);
% imwrite(x, "slice_100.tif");

% x = imread("inputTiffFile.tif", 163);
% imwrite(x, "slice_163.tif");
However, I have one problem: The voxel spacing in the input image is [1, 1, 1], but the voxel spacing after using ‘imwrite’ is changed to [0.352778, 0.352778, 1]. I require the voxel spacing to be same as input.

Any inputs on how to avoid this change in voxel size would be very helpful.
Thanks, voxel_spacing, imwrite MATLAB Answers — New Questions

​

please help me, I would like to calculate dominant and centroid frequency and plot them tohether in one plot. Please CMIIW if i have misundertanding the basic concept. As I undertand, dominant freq is higest freq that appear in our signal. I used FFT and get the max power. And about dominant freq I am not quite sure. What makes me don’t understand is, if I take example from weighted mean as basic of centroid freq, based on example in https://en.wikipedia.org/wiki/Weighted_arithmetic_mean, after calculate weighted mean we have weighted mean of the frequencies present in the signal, meaning that is not a single value. I use spectralCentroid to calculate centroid frequency, but then i confuse, if i have single falue for dominant freq and several value from spectralCentroid, how i should plot them together?
i also attach my file, basically the code will doing looping as my file represent one day data, so my goal is plot dominant and centroid freq in one plot for 1 month. I assume my dominant freq has 30 data point, but not sure about spectralCentroid.
this is my attempt to do it.
sig=load(‘signal2023-335.txt’);

%BAND PASS FILTER long range –> can be considered as UNFILTER condition
% Define the parameters
lowcut = 1/400; % Low cutoff frequency in Hz
high1 = 40; % High cutoff frequency in Hz
high2 = 1.25; % Midrange cutoff frequency in Hz
fs = 100; % Sampling frequency in Hz

% Design the Butterworth bandpass filter (UNFILTER)
[bU, aU] = butter(2,[lowcut high1] / (fs / 2), ‘bandpass’);
sigUnfilt= filtfilt(bU,aU,sig);

% calculate FFT UNFILT
n=length(sigUnfilt);
srate=100; %sampling rate
nyq=srate/2;
fftsig=fft(sigUnfilt-mean(sigUnfilt))/n; %dataX is fft result from filtered signal
hz=linspace(0,nyq,floor(n/2)+1);
pow=2*abs(fftsig(1:length(hz)));
Iv = 1:numel(hz);

%get the maximum freq/dominant freq
[~,idx] = max(pow) ;
domfreq = hz(idx)

% Calculate centroid frequency
centroid = spectralCentroid(sigUnfilt,100)please help me, I would like to calculate dominant and centroid frequency and plot them tohether in one plot. Please CMIIW if i have misundertanding the basic concept. As I undertand, dominant freq is higest freq that appear in our signal. I used FFT and get the max power. And about dominant freq I am not quite sure. What makes me don’t understand is, if I take example from weighted mean as basic of centroid freq, based on example in https://en.wikipedia.org/wiki/Weighted_arithmetic_mean, after calculate weighted mean we have weighted mean of the frequencies present in the signal, meaning that is not a single value. I use spectralCentroid to calculate centroid frequency, but then i confuse, if i have single falue for dominant freq and several value from spectralCentroid, how i should plot them together?
i also attach my file, basically the code will doing looping as my file represent one day data, so my goal is plot dominant and centroid freq in one plot for 1 month. I assume my dominant freq has 30 data point, but not sure about spectralCentroid.
this is my attempt to do it.
sig=load(‘signal2023-335.txt’);

%BAND PASS FILTER long range –> can be considered as UNFILTER condition
% Define the parameters
lowcut = 1/400; % Low cutoff frequency in Hz
high1 = 40; % High cutoff frequency in Hz
high2 = 1.25; % Midrange cutoff frequency in Hz
fs = 100; % Sampling frequency in Hz

% Design the Butterworth bandpass filter (UNFILTER)
[bU, aU] = butter(2,[lowcut high1] / (fs / 2), ‘bandpass’);
sigUnfilt= filtfilt(bU,aU,sig);

% calculate FFT UNFILT
n=length(sigUnfilt);
srate=100; %sampling rate
nyq=srate/2;
fftsig=fft(sigUnfilt-mean(sigUnfilt))/n; %dataX is fft result from filtered signal
hz=linspace(0,nyq,floor(n/2)+1);
pow=2*abs(fftsig(1:length(hz)));
Iv = 1:numel(hz);

%get the maximum freq/dominant freq
[~,idx] = max(pow) ;
domfreq = hz(idx)

% Calculate centroid frequency
centroid = spectralCentroid(sigUnfilt,100) please help me, I would like to calculate dominant and centroid frequency and plot them tohether in one plot. Please CMIIW if i have misundertanding the basic concept. As I undertand, dominant freq is higest freq that appear in our signal. I used FFT and get the max power. And about dominant freq I am not quite sure. What makes me don’t understand is, if I take example from weighted mean as basic of centroid freq, based on example in https://en.wikipedia.org/wiki/Weighted_arithmetic_mean, after calculate weighted mean we have weighted mean of the frequencies present in the signal, meaning that is not a single value. I use spectralCentroid to calculate centroid frequency, but then i confuse, if i have single falue for dominant freq and several value from spectralCentroid, how i should plot them together?
i also attach my file, basically the code will doing looping as my file represent one day data, so my goal is plot dominant and centroid freq in one plot for 1 month. I assume my dominant freq has 30 data point, but not sure about spectralCentroid.
this is my attempt to do it.
sig=load(‘signal2023-335.txt’);

%BAND PASS FILTER long range –> can be considered as UNFILTER condition
% Define the parameters
lowcut = 1/400; % Low cutoff frequency in Hz
high1 = 40; % High cutoff frequency in Hz
high2 = 1.25; % Midrange cutoff frequency in Hz
fs = 100; % Sampling frequency in Hz

% Design the Butterworth bandpass filter (UNFILTER)
[bU, aU] = butter(2,[lowcut high1] / (fs / 2), ‘bandpass’);
sigUnfilt= filtfilt(bU,aU,sig);

% calculate FFT UNFILT
n=length(sigUnfilt);
srate=100; %sampling rate
nyq=srate/2;
fftsig=fft(sigUnfilt-mean(sigUnfilt))/n; %dataX is fft result from filtered signal
hz=linspace(0,nyq,floor(n/2)+1);
pow=2*abs(fftsig(1:length(hz)));
Iv = 1:numel(hz);

%get the maximum freq/dominant freq
[~,idx] = max(pow) ;
domfreq = hz(idx)

% Calculate centroid frequency
centroid = spectralCentroid(sigUnfilt,100) centroid frequency, fft, centroid, weighted mean MATLAB Answers — New Questions

​

I have a mesured voltage and current. I want to find the impedance magnitude, real and imaginary part then plot yquist and cole-cole plot, but i don’t know to proceed, can someone help please.I have a mesured voltage and current. I want to find the impedance magnitude, real and imaginary part then plot yquist and cole-cole plot, but i don’t know to proceed, can someone help please. I have a mesured voltage and current. I want to find the impedance magnitude, real and imaginary part then plot yquist and cole-cole plot, but i don’t know to proceed, can someone help please. impedance, varying frequency, frequency sweep, nyquist, cole cole plot MATLAB Answers — New Questions

​

Hi,there . The code that i want to write is :(i have a file of images and each images has a gps coordination in my csv excel file , well i need a code that gets a coordinates from my excel and tags it to it’s own picture… and at the end when i get the properties of my pictures i can see the coordinates in details) … i wrote to many codes but I wasn’t successful …plz help me to solve it ..thank you . And i took an example of my code .

Picfile=(“address”);coordsfile=(“address.csv”);
‏for i = 1:height(coords)
‏ filename = coords.Filename{i};
‏ lat = coords.Latitude(i);
‏ lon = coords.Longitude(i);

‏ command = sprintf( lat, lon, filename);
‏ system([‘exiftool ‘ command]);
‏endHi,there . The code that i want to write is :(i have a file of images and each images has a gps coordination in my csv excel file , well i need a code that gets a coordinates from my excel and tags it to it’s own picture… and at the end when i get the properties of my pictures i can see the coordinates in details) … i wrote to many codes but I wasn’t successful …plz help me to solve it ..thank you . And i took an example of my code .

Picfile=(“address”);coordsfile=(“address.csv”);
‏for i = 1:height(coords)
‏ filename = coords.Filename{i};
‏ lat = coords.Latitude(i);
‏ lon = coords.Longitude(i);

‏ command = sprintf( lat, lon, filename);
‏ system([‘exiftool ‘ command]);
‏end Hi,there . The code that i want to write is :(i have a file of images and each images has a gps coordination in my csv excel file , well i need a code that gets a coordinates from my excel and tags it to it’s own picture… and at the end when i get the properties of my pictures i can see the coordinates in details) … i wrote to many codes but I wasn’t successful …plz help me to solve it ..thank you . And i took an example of my code .

Picfile=(“address”);coordsfile=(“address.csv”);
‏for i = 1:height(coords)
‏ filename = coords.Filename{i};
‏ lat = coords.Latitude(i);
‏ lon = coords.Longitude(i);

‏ command = sprintf( lat, lon, filename);
‏ system([‘exiftool ‘ command]);
‏end tagging MATLAB Answers — New Questions

​

I am trying to combine GPS data and video. I have a GPS device which gives me gps/nmea format gps data. I am reading each frame from the video and storing it as a image and want to add GPSInfo (metadata) to it. If I use the IMWRITE function, it doesn’t let me add the GPSInfo data since its not a tag which can be edited or changed.

How can I then geotag images using MATLAB?I am trying to combine GPS data and video. I have a GPS device which gives me gps/nmea format gps data. I am reading each frame from the video and storing it as a image and want to add GPSInfo (metadata) to it. If I use the IMWRITE function, it doesn’t let me add the GPSInfo data since its not a tag which can be edited or changed.

How can I then geotag images using MATLAB? I am trying to combine GPS data and video. I have a GPS device which gives me gps/nmea format gps data. I am reading each frame from the video and storing it as a image and want to add GPSInfo (metadata) to it. If I use the IMWRITE function, it doesn’t let me add the GPSInfo data since its not a tag which can be edited or changed.

How can I then geotag images using MATLAB? geotagging images, gps, video processing, gps tagging MATLAB Answers — New Questions

​

Hello,
How can I use Createpde to solve the a fourth degree partial differential equation?
∇4(u)=q/D?Hello,
How can I use Createpde to solve the a fourth degree partial differential equation?
∇4(u)=q/D? Hello,
How can I use Createpde to solve the a fourth degree partial differential equation?
∇4(u)=q/D? createpde MATLAB Answers — New Questions

​

I use MATLAB-YALMIP to write the optimal trend program based on the IEEEE14 system, but I can’t converge. Please help to see if the program is wrong?
code:
% Optimal Power Flow Example
% Based on the IEEE 14-bus standard case (i.e., case14 in Matpower)
% Implemented in MATLAB with YALMIP
clear; clc;

% Load power system data
mpc = loadcase(‘case14’); % You can also load other data files, such as case9, case30, etc.

% Define variables and parameters
nb = size(mpc.bus, 1); % Number of buses (nodes)
nl = size(mpc.branch, 1); % Number of branches (lines)
ng = size(mpc.gen, 1); % Number of generators
nr = nb – ng; % Number of load buses

% Extract node data
bus_id = mpc.bus(:, 1); % Bus IDs
bus_type = mpc.bus(:, 2); % Bus types (1-PQ, 2-PV, 3-Slack)
Pd = mpc.bus(:, 3); % Active power demand (MW)
Qd = mpc.bus(:, 4); % Reactive power demand (MVAr)
Vmin = mpc.bus(:, 13); % Minimum bus voltage (p.u.)
Vmax = mpc.bus(:, 12); % Maximum bus voltage (p.u.)

% Extract generator data
gen_id = mpc.gen(:, 1); % Bus IDs where generators are connected
Pgmin = mpc.gen(:, 10); % Minimum generator active power output (MW)
Pgmax = mpc.gen(:, 9); % Maximum generator active power output (MW)
Qgmin = mpc.gen(:, 5); % Minimum generator reactive power output (MVAr)
Qgmax = mpc.gen(:, 4); % Maximum generator reactive power output (MVAr)
a2 = mpc.gencost(:, 5); % Generator cost curve coefficient for quadratic term
a1 = mpc.gencost(:, 6); % Generator cost curve coefficient for linear term
a0 = mpc.gencost(:, 7); % Generator cost curve constant term

% Extract branch data
fbus = mpc.branch(:, 1); % "From" bus (starting node) of each branch
tbus = mpc.branch(:, 2); % "To" bus (ending node) of each branch
Plmax = ones(nl, 1) * 4000; % Maximum branch active power flow limit (MW)
% Plmax = mpc.branch(:, 6) .* mpc.branch(:, 3) * 1e-3; % Maximum branch active power flow limit (MW)

% Calculate the bus admittance matrix
[Ybus, ~, ~] = makeYbus(mpc); % Use a Matpower function to calculate the bus admittance matrix
Gbus = real(Ybus); % Real part of bus admittance matrix (conductance)
Bbus = imag(Ybus); % Imaginary part of bus admittance matrix (susceptance)

% Define YALMIP variables
Pg = sdpvar(ng, 1, ‘full’); % Generator active power output variables
Qg = sdpvar(ng, 1, ‘full’); % Generator reactive power output variables
V = sdpvar(nb, 1, ‘full’); % Bus voltage magnitude variables
theta = sdpvar(nb, 1, ‘full’); % Bus voltage angle variables
Pl = sdpvar(nl, 1, ‘full’); % Branch active power flow variables

% Define the objective function
objective = sum(a2 .* Pg.^2 + a1 .* Pg + a0); % Minimize the total operating cost

% Define constraints
constraints = [];
% Node power balance constraints
for i = 1:nb
% Active power balance equation for node i
if ismember(i, gen_id)
record = find(gen_id == i);
constraints = [constraints, Pg(record) – Pd(i) – V(i) * sum(V * (Gbus(i, 🙂 * cos(theta(i) – theta) + Bbus(i, 🙂 * sin(theta(i) – theta))) == 0];
constraints = [constraints, Qg(record) – Qd(i) + V(i) * sum(V * (Gbus(i, 🙂 * sin(theta(i) – theta) – Bbus(i, 🙂 * cos(theta(i) – theta))) == 0];
else
constraints = [constraints, -Pd(i) – V(i) * sum(V * (Gbus(i, 🙂 * cos(theta(i) – theta) + Bbus(i, 🙂 * sin(theta(i) – theta))) == 0];
constraints = [constraints, -Qd(i) + V(i) * sum(V * (Gbus(i, 🙂 * sin(theta(i) – theta) – Bbus(i, 🙂 * cos(theta(i) – theta))) == 0];
end
end

% Generator output limit constraints
for i = 1:ng
% Minimum active power output constraint for generator i
constraints = [constraints, Pgmin(i) <= Pg(i)];
% Maximum active power output constraint for generator i
constraints = [constraints, Pg(i) <= Pgmax(i)];
% Minimum reactive power output constraint for generator i
constraints = [constraints, Qgmin(i) <= Qg(i)];
% Maximum reactive power output constraint for generator i
constraints = [constraints, Qg(i) <= Qgmax(i)];
end
% Bus voltage limit constraints
for i = 1:nb
% Minimum bus voltage magnitude constraint for bus i
constraints = [constraints, Vmin(i) <= V(i)];
% Maximum bus voltage magnitude constraint for bus i
constraints = [constraints, V(i) <= Vmax(i)];
end
% Branch power flow limit constraints
for i = 1:nl
% Calculate active power flow on branch i (assuming the "from" bus is the positive direction)
Pl(i) = V(fbus(i))^2 * Gbus(fbus(i), tbus(i)) – V(fbus(i)) * V(tbus(i)) * (Gbus(fbus(i), tbus(i)) * cos(theta(fbus(i) – theta(tbus(i))) + Bbus(fbus(i), tbus(i)) * sin(theta(fbus(i) – theta(tbus(i)))));
% Absolute value constraint for active power flow on branch i
constraints = [constraints, abs(Pl(i)) <= Plmax(i)];
end

% Solve the optimal power flow problem
result = solvesdp(constraints, objective); % Use YALMIP to solve the optimal power flow problem

% Output and analyze the results
if result.problem == 0 % If the solution is successful
disp(‘Optimal Power Flow Solved Successfully!’); % Display success message
disp(‘Total System Operating Cost:’); % Display total system operating cost
value(objective) % Display the objective function value
disp(‘Generator Active Power Output:’); % Display generator active power output
value(Pg) % Display the generator active power output variable values
disp(‘Generator Reactive Power Output:’); % Display generator reactive power output
value(Qg) % Display the generator reactive power output variable values
disp(‘Bus Voltage Magnitude:’); % Display bus voltage magnitude
value(V) % Display the bus voltage magnitude variable values
disp(‘Bus Voltage Angle (in degrees):’); % Display bus voltage angle (in degrees)
rad2deg(value(theta)) % Display bus voltage angle variable values and convert to degrees
disp(‘Branch Active Power Flow:’); % Display branch active power flow
value(Pl)
endI use MATLAB-YALMIP to write the optimal trend program based on the IEEEE14 system, but I can’t converge. Please help to see if the program is wrong?
code:
% Optimal Power Flow Example
% Based on the IEEE 14-bus standard case (i.e., case14 in Matpower)
% Implemented in MATLAB with YALMIP
clear; clc;

% Load power system data
mpc = loadcase(‘case14’); % You can also load other data files, such as case9, case30, etc.

% Define variables and parameters
nb = size(mpc.bus, 1); % Number of buses (nodes)
nl = size(mpc.branch, 1); % Number of branches (lines)
ng = size(mpc.gen, 1); % Number of generators
nr = nb – ng; % Number of load buses

% Extract node data
bus_id = mpc.bus(:, 1); % Bus IDs
bus_type = mpc.bus(:, 2); % Bus types (1-PQ, 2-PV, 3-Slack)
Pd = mpc.bus(:, 3); % Active power demand (MW)
Qd = mpc.bus(:, 4); % Reactive power demand (MVAr)
Vmin = mpc.bus(:, 13); % Minimum bus voltage (p.u.)
Vmax = mpc.bus(:, 12); % Maximum bus voltage (p.u.)

% Extract generator data
gen_id = mpc.gen(:, 1); % Bus IDs where generators are connected
Pgmin = mpc.gen(:, 10); % Minimum generator active power output (MW)
Pgmax = mpc.gen(:, 9); % Maximum generator active power output (MW)
Qgmin = mpc.gen(:, 5); % Minimum generator reactive power output (MVAr)
Qgmax = mpc.gen(:, 4); % Maximum generator reactive power output (MVAr)
a2 = mpc.gencost(:, 5); % Generator cost curve coefficient for quadratic term
a1 = mpc.gencost(:, 6); % Generator cost curve coefficient for linear term
a0 = mpc.gencost(:, 7); % Generator cost curve constant term

% Extract branch data
fbus = mpc.branch(:, 1); % "From" bus (starting node) of each branch
tbus = mpc.branch(:, 2); % "To" bus (ending node) of each branch
Plmax = ones(nl, 1) * 4000; % Maximum branch active power flow limit (MW)
% Plmax = mpc.branch(:, 6) .* mpc.branch(:, 3) * 1e-3; % Maximum branch active power flow limit (MW)

% Calculate the bus admittance matrix
[Ybus, ~, ~] = makeYbus(mpc); % Use a Matpower function to calculate the bus admittance matrix
Gbus = real(Ybus); % Real part of bus admittance matrix (conductance)
Bbus = imag(Ybus); % Imaginary part of bus admittance matrix (susceptance)

% Define YALMIP variables
Pg = sdpvar(ng, 1, ‘full’); % Generator active power output variables
Qg = sdpvar(ng, 1, ‘full’); % Generator reactive power output variables
V = sdpvar(nb, 1, ‘full’); % Bus voltage magnitude variables
theta = sdpvar(nb, 1, ‘full’); % Bus voltage angle variables
Pl = sdpvar(nl, 1, ‘full’); % Branch active power flow variables

% Define the objective function
objective = sum(a2 .* Pg.^2 + a1 .* Pg + a0); % Minimize the total operating cost

% Define constraints
constraints = [];
% Node power balance constraints
for i = 1:nb
% Active power balance equation for node i
if ismember(i, gen_id)
record = find(gen_id == i);
constraints = [constraints, Pg(record) – Pd(i) – V(i) * sum(V * (Gbus(i, 🙂 * cos(theta(i) – theta) + Bbus(i, 🙂 * sin(theta(i) – theta))) == 0];
constraints = [constraints, Qg(record) – Qd(i) + V(i) * sum(V * (Gbus(i, 🙂 * sin(theta(i) – theta) – Bbus(i, 🙂 * cos(theta(i) – theta))) == 0];
else
constraints = [constraints, -Pd(i) – V(i) * sum(V * (Gbus(i, 🙂 * cos(theta(i) – theta) + Bbus(i, 🙂 * sin(theta(i) – theta))) == 0];
constraints = [constraints, -Qd(i) + V(i) * sum(V * (Gbus(i, 🙂 * sin(theta(i) – theta) – Bbus(i, 🙂 * cos(theta(i) – theta))) == 0];
end
end

% Generator output limit constraints
for i = 1:ng
% Minimum active power output constraint for generator i
constraints = [constraints, Pgmin(i) <= Pg(i)];
% Maximum active power output constraint for generator i
constraints = [constraints, Pg(i) <= Pgmax(i)];
% Minimum reactive power output constraint for generator i
constraints = [constraints, Qgmin(i) <= Qg(i)];
% Maximum reactive power output constraint for generator i
constraints = [constraints, Qg(i) <= Qgmax(i)];
end
% Bus voltage limit constraints
for i = 1:nb
% Minimum bus voltage magnitude constraint for bus i
constraints = [constraints, Vmin(i) <= V(i)];
% Maximum bus voltage magnitude constraint for bus i
constraints = [constraints, V(i) <= Vmax(i)];
end
% Branch power flow limit constraints
for i = 1:nl
% Calculate active power flow on branch i (assuming the "from" bus is the positive direction)
Pl(i) = V(fbus(i))^2 * Gbus(fbus(i), tbus(i)) – V(fbus(i)) * V(tbus(i)) * (Gbus(fbus(i), tbus(i)) * cos(theta(fbus(i) – theta(tbus(i))) + Bbus(fbus(i), tbus(i)) * sin(theta(fbus(i) – theta(tbus(i)))));
% Absolute value constraint for active power flow on branch i
constraints = [constraints, abs(Pl(i)) <= Plmax(i)];
end

% Solve the optimal power flow problem
result = solvesdp(constraints, objective); % Use YALMIP to solve the optimal power flow problem

% Output and analyze the results
if result.problem == 0 % If the solution is successful
disp(‘Optimal Power Flow Solved Successfully!’); % Display success message
disp(‘Total System Operating Cost:’); % Display total system operating cost
value(objective) % Display the objective function value
disp(‘Generator Active Power Output:’); % Display generator active power output
value(Pg) % Display the generator active power output variable values
disp(‘Generator Reactive Power Output:’); % Display generator reactive power output
value(Qg) % Display the generator reactive power output variable values
disp(‘Bus Voltage Magnitude:’); % Display bus voltage magnitude
value(V) % Display the bus voltage magnitude variable values
disp(‘Bus Voltage Angle (in degrees):’); % Display bus voltage angle (in degrees)
rad2deg(value(theta)) % Display bus voltage angle variable values and convert to degrees
disp(‘Branch Active Power Flow:’); % Display branch active power flow
value(Pl)
end I use MATLAB-YALMIP to write the optimal trend program based on the IEEEE14 system, but I can’t converge. Please help to see if the program is wrong?
code:
% Optimal Power Flow Example
% Based on the IEEE 14-bus standard case (i.e., case14 in Matpower)
% Implemented in MATLAB with YALMIP
clear; clc;

% Load power system data
mpc = loadcase(‘case14’); % You can also load other data files, such as case9, case30, etc.

% Define variables and parameters
nb = size(mpc.bus, 1); % Number of buses (nodes)
nl = size(mpc.branch, 1); % Number of branches (lines)
ng = size(mpc.gen, 1); % Number of generators
nr = nb – ng; % Number of load buses

% Extract node data
bus_id = mpc.bus(:, 1); % Bus IDs
bus_type = mpc.bus(:, 2); % Bus types (1-PQ, 2-PV, 3-Slack)
Pd = mpc.bus(:, 3); % Active power demand (MW)
Qd = mpc.bus(:, 4); % Reactive power demand (MVAr)
Vmin = mpc.bus(:, 13); % Minimum bus voltage (p.u.)
Vmax = mpc.bus(:, 12); % Maximum bus voltage (p.u.)

% Extract generator data
gen_id = mpc.gen(:, 1); % Bus IDs where generators are connected
Pgmin = mpc.gen(:, 10); % Minimum generator active power output (MW)
Pgmax = mpc.gen(:, 9); % Maximum generator active power output (MW)
Qgmin = mpc.gen(:, 5); % Minimum generator reactive power output (MVAr)
Qgmax = mpc.gen(:, 4); % Maximum generator reactive power output (MVAr)
a2 = mpc.gencost(:, 5); % Generator cost curve coefficient for quadratic term
a1 = mpc.gencost(:, 6); % Generator cost curve coefficient for linear term
a0 = mpc.gencost(:, 7); % Generator cost curve constant term

% Extract branch data
fbus = mpc.branch(:, 1); % "From" bus (starting node) of each branch
tbus = mpc.branch(:, 2); % "To" bus (ending node) of each branch
Plmax = ones(nl, 1) * 4000; % Maximum branch active power flow limit (MW)
% Plmax = mpc.branch(:, 6) .* mpc.branch(:, 3) * 1e-3; % Maximum branch active power flow limit (MW)

% Calculate the bus admittance matrix
[Ybus, ~, ~] = makeYbus(mpc); % Use a Matpower function to calculate the bus admittance matrix
Gbus = real(Ybus); % Real part of bus admittance matrix (conductance)
Bbus = imag(Ybus); % Imaginary part of bus admittance matrix (susceptance)

% Define YALMIP variables
Pg = sdpvar(ng, 1, ‘full’); % Generator active power output variables
Qg = sdpvar(ng, 1, ‘full’); % Generator reactive power output variables
V = sdpvar(nb, 1, ‘full’); % Bus voltage magnitude variables
theta = sdpvar(nb, 1, ‘full’); % Bus voltage angle variables
Pl = sdpvar(nl, 1, ‘full’); % Branch active power flow variables

% Define the objective function
objective = sum(a2 .* Pg.^2 + a1 .* Pg + a0); % Minimize the total operating cost

% Define constraints
constraints = [];
% Node power balance constraints
for i = 1:nb
% Active power balance equation for node i
if ismember(i, gen_id)
record = find(gen_id == i);
constraints = [constraints, Pg(record) – Pd(i) – V(i) * sum(V * (Gbus(i, 🙂 * cos(theta(i) – theta) + Bbus(i, 🙂 * sin(theta(i) – theta))) == 0];
constraints = [constraints, Qg(record) – Qd(i) + V(i) * sum(V * (Gbus(i, 🙂 * sin(theta(i) – theta) – Bbus(i, 🙂 * cos(theta(i) – theta))) == 0];
else
constraints = [constraints, -Pd(i) – V(i) * sum(V * (Gbus(i, 🙂 * cos(theta(i) – theta) + Bbus(i, 🙂 * sin(theta(i) – theta))) == 0];
constraints = [constraints, -Qd(i) + V(i) * sum(V * (Gbus(i, 🙂 * sin(theta(i) – theta) – Bbus(i, 🙂 * cos(theta(i) – theta))) == 0];
end
end

% Generator output limit constraints
for i = 1:ng
% Minimum active power output constraint for generator i
constraints = [constraints, Pgmin(i) <= Pg(i)];
% Maximum active power output constraint for generator i
constraints = [constraints, Pg(i) <= Pgmax(i)];
% Minimum reactive power output constraint for generator i
constraints = [constraints, Qgmin(i) <= Qg(i)];
% Maximum reactive power output constraint for generator i
constraints = [constraints, Qg(i) <= Qgmax(i)];
end
% Bus voltage limit constraints
for i = 1:nb
% Minimum bus voltage magnitude constraint for bus i
constraints = [constraints, Vmin(i) <= V(i)];
% Maximum bus voltage magnitude constraint for bus i
constraints = [constraints, V(i) <= Vmax(i)];
end
% Branch power flow limit constraints
for i = 1:nl
% Calculate active power flow on branch i (assuming the "from" bus is the positive direction)
Pl(i) = V(fbus(i))^2 * Gbus(fbus(i), tbus(i)) – V(fbus(i)) * V(tbus(i)) * (Gbus(fbus(i), tbus(i)) * cos(theta(fbus(i) – theta(tbus(i))) + Bbus(fbus(i), tbus(i)) * sin(theta(fbus(i) – theta(tbus(i)))));
% Absolute value constraint for active power flow on branch i
constraints = [constraints, abs(Pl(i)) <= Plmax(i)];
end

% Solve the optimal power flow problem
result = solvesdp(constraints, objective); % Use YALMIP to solve the optimal power flow problem

% Output and analyze the results
if result.problem == 0 % If the solution is successful
disp(‘Optimal Power Flow Solved Successfully!’); % Display success message
disp(‘Total System Operating Cost:’); % Display total system operating cost
value(objective) % Display the objective function value
disp(‘Generator Active Power Output:’); % Display generator active power output
value(Pg) % Display the generator active power output variable values
disp(‘Generator Reactive Power Output:’); % Display generator reactive power output
value(Qg) % Display the generator reactive power output variable values
disp(‘Bus Voltage Magnitude:’); % Display bus voltage magnitude
value(V) % Display the bus voltage magnitude variable values
disp(‘Bus Voltage Angle (in degrees):’); % Display bus voltage angle (in degrees)
rad2deg(value(theta)) % Display bus voltage angle variable values and convert to degrees
disp(‘Branch Active Power Flow:’); % Display branch active power flow
value(Pl)
end yalmip, opf, matlab, optimal power flow MATLAB Answers — New Questions

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The 3 DOF Vehicle Body Block supports steering angle and wheel speed as inputs, but the 6 DOF Vehicle Body Block only seems to support suspension and external forces and moments as inputs.
Is there a way to use the 6 DOF block with steering angle and wheel speed control inputs, and perhaps an additional environment input? I know the Constant Radius Reference Application uses the 6DOF block under the hood, but this is a huge step up in complexity from the 2-dimensional 3DOF block. Looking to make the jump from 2-D to 3-D, but preferably still using typical control inputs.The 3 DOF Vehicle Body Block supports steering angle and wheel speed as inputs, but the 6 DOF Vehicle Body Block only seems to support suspension and external forces and moments as inputs.
Is there a way to use the 6 DOF block with steering angle and wheel speed control inputs, and perhaps an additional environment input? I know the Constant Radius Reference Application uses the 6DOF block under the hood, but this is a huge step up in complexity from the 2-dimensional 3DOF block. Looking to make the jump from 2-D to 3-D, but preferably still using typical control inputs. The 3 DOF Vehicle Body Block supports steering angle and wheel speed as inputs, but the 6 DOF Vehicle Body Block only seems to support suspension and external forces and moments as inputs.
Is there a way to use the 6 DOF block with steering angle and wheel speed control inputs, and perhaps an additional environment input? I know the Constant Radius Reference Application uses the 6DOF block under the hood, but this is a huge step up in complexity from the 2-dimensional 3DOF block. Looking to make the jump from 2-D to 3-D, but preferably still using typical control inputs. vehicle dynamics, 6dof, simulink MATLAB Answers — New Questions

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