Generate Parameters for Flux-Based PMSM Block
Using MathWorks tools, you can create lookup tables for an interior permanent magnet synchronous motor (PMSM) controller that characterizes the d-axis and q-axis current as a function of d-axis and q-axis flux.
To generate the flux parameters for the Flux-Based PMSM block, follow these
workflow steps. Example script CreatingIdqTable.m
calls
gridfit
to model the current surface using scattered or
semi-scattered flux data.
Workflow | Description |
---|---|
Load and preprocess this nonlinear motor flux data from dynamometer testing or finite element analysis (FEA):
| |
Step 2: Generate Evenly Spaced Table Data From Scattered Data |
Use the |
Set workspace variables that you can use for the Flux-Based PM Controller block parameters. |
Step 1: Load and Preprocess Data
Load and preprocess this nonlinear motor flux data from dynamometer testing or finite element analysis (FEA):
d- and q- axis current
d- and q- axis flux
Electromagnetic motor torque
Open the example script
CreatingIdqTable.m
.Load and preprocess the data.
% Load the data from a |mat| file captured from a dynamometer or % another CAE tool. load FEAdata.mat;
Determine the minimum and maximum flux values.
flux_d_min = min(min(FEAdata.flux.d)) ; flux_d_max = max(max(FEAdata.flux.d)) ; flux_q_min = min(min(FEAdata.flux.q)) ; flux_q_max = max(max(FEAdata.flux.q)) ;
Plot the sweeping current points used to collect the data.
for i = 1:length(FEAdata.current.d) for j = 1:1:length(FEAdata.current.q) plot(FEAdata.current.d(i),FEAdata.current.q(j),'b*'); hold on end end
Plot the current limit sweeping points and circle.
for angle_theta = pi/2:(pi/2/200):(3*pi/2) plot(300*cos(angle_theta),300*sin(angle_theta),'r.'); hold on end xlabel('I_d [A]') ylabel('I_q [A]') title('Sweeping Points'); grid on; xlim([-300,0]); ylim([-300,300]); hold off
Step 2: Generate Evenly Spaced Table Data From Scattered Data
The flux tables and can have different step sizes for the currents. Evenly spacing the rows and columns helps improve interpolation accuracy. This script uses spline interpolation.
Set the spacing for the table rows and columns.
% Set the spacing for the table rows and columns flux_d_size = 50; flux_q_size = 50;
Generate linear spaced vectors for the breakpoints.
% Generate linear spaced vectors for the breakpoints ParamFluxDIndex = linspace(flux_d_min,flux_d_max,flux_d_size); ParamFluxQIndex = linspace(flux_q_min,flux_q_max,flux_q_size);
Create 2-D grid coordinates based on the d-axis and q-axis currents.
% Create 2-D grid coordinates based on the d-axis and q-axis currents [id_m,iq_m] = meshgrid(FEAdata.current.d,FEAdata.current.q);
Create the table for the d-axis current.
% Create the table for the d-axis current id_fit = gridfit(FEAdata.flux.d,FEAdata.flux.q,id_m,ParamFluxDIndex,ParamFluxQIndex); ParamIdLookupTable = id_fit'; figure; surf(ParamFluxDIndex,ParamFluxQIndex,ParamIdLookupTable'); xlabel('\lambda_d [v.s]');ylabel('\lambda_q [v.s]');zlabel('id [A]');title('id Table'); grid on; shading flat;
d-axis current, Id, as a function of q-axis flux, λq, and d-axis flux, λd.
Create the table for the q-axis current.
% Create the table for the q-axis current iq_fit = gridfit(FEAdata.flux.d,FEAdata.flux.q,iq_m,ParamFluxDIndex,ParamFluxQIndex); ParamIqLookupTable = iq_fit'; figure; surf(ParamFluxDIndex,ParamFluxQIndex,ParamIqLookupTable'); xlabel('\lambda_d [v.s]');ylabel('\lambda_q [v.s]');zlabel('iq [A]'); title('iq Table'); grid on; shading flat;
q-axis current, Iq, as a function of q-axis flux, λq, and d-axis flux, λd.
Step 3: Set Block Parameters
Set the block parameters to these values assigned in the example script.
Parameter | MATLAB® Commands |
---|---|
Vector of d-axis flux, flux_d |
flux_d=ParamFluxDIndex; |
Vector of q-axis flux, flux_q |
flux_q=ParamFluxQIndex; |
Corresponding d-axis current, id |
id=ParamIdLookupTable; |
Corresponding q-axis current, iq |
iq=ParamIqLookupTable; |
References
[1] Hu, Dakai, Yazan Alsmadi, and Longya Xu. “High fidelity nonlinear IPM modeling based on measured stator winding flux linkage.” IEEE® Transactions on Industry Applications, Vol. 51, No. 4, July/August 2015.
[2] Chen, Xiao, Jiabin Wang, Bhaskar Sen, Panagiotis Lasari, Tianfu Sun. “A High-Fidelity and Computationally Efficient Model for Interior Permanent-Magnet Machines Considering the Magnetic Saturation, Spatial Harmonics, and Iron Loss Effect.” IEEE Transactions on Industrial Electronics, Vol. 62, No. 7, July 2015.
[3] Ottosson, J., M. Alakula. “A compact field weakening controller implementation.” International Symposium on Power Electronics, Electrical Drives, Automation and Motion, July, 2006.
See Also
Flux-Based PM Controller | Flux-Based PMSM