Refactoring (#115)

.vscode/settings.json

@@ -1,6 +1,6 @@
 {
     "editor.rulers": [
-        80
+        79
     ],
     "editor.formatOnType": false,
     "editor.formatOnSave": true,

examples/flux_vector/plot_flux_vector_pmsm_2kw.py

@@ -46,7 +46,7 @@
 # %%
 # Create the simulation object and simulate it.
 
-sim = model.Simulation(mdl, ctrl, pwm=False)
+sim = model.Simulation(mdl, ctrl)
 sim.simulate(t_stop=1.6)
 
 # %%

examples/flux_vector/plot_flux_vector_pmsyrm_5kw.py

@@ -3,11 +3,11 @@
 =========================
 
 This example simulates sensorless stator-flux-vector control of a 5.5-kW 
-PM-SyRM (Baldor ECS101M0H7EF4) drive. The machine model is parametrized using an 
-algebraic saturation model, fitted to the flux linkage maps measured using the 
-constant-speed test. For comparison, the measured data is plotted together with 
-the model predictions. Notice that the control system used in this example does 
-not consider the saturation, only the system model does. 
+PM-SyRM (Baldor ECS101M0H7EF4) drive. The machine model is parametrized using 
+an algebraic saturation model, fitted to the flux linkage maps measured using 
+the constant-speed test. For comparison, the measured data is plotted together 
+with the model predictions. Notice that the control system used in this example 
+does not consider the saturation, only the system model does. 
 
 """
 
@@ -35,14 +35,15 @@
 # available later.
 
 
+# pylint: disable=too-many-locals
 def i_s(psi_s):
     """
-    Saturation model for a 5.5-kW PM synchronous reluctance machine.
+    Saturation model for a 5.5-kW PM-SyRM.
     
     This model takes into account the bridge saturation in addition to the 
     regular self- and cross-saturation effects of the d- and q-axis. The bridge 
     saturation model is based on a nonlinear reluctance element in parallel 
-    with the Norton-equivalent PM model.
+    with the Norton-equivalent PM model. 
 
     Parameters
     ----------
@@ -56,8 +57,9 @@ def i_s(psi_s):
 
     Notes
     -----
-    This model can also be used for other PM synchronous reluctance machines by 
-    changing the model parameters.  
+    For simplicity, the saturation model parameters are hard-coded in the 
+    function below. This model can also be used for other PM-SyRMs by changing 
+    the model parameters.  
 
     """
     # d-axis self-saturation
@@ -131,8 +133,8 @@ def i_s(psi_s):
 plt.show()
 
 # %%
-# Solve the PM flux linkage for the initial value of the stator flux, which is
-# needed in the machine model below.
+# Solve the PM flux linkage for the initial value of the stator flux linkage,
+# which is needed in the machine model below.
 
 res = minimize_scalar(
     lambda psi_d: np.abs(i_s(psi_d)), bounds=(0, base.psi), method="bounded")
@@ -143,12 +145,13 @@ def i_s(psi_s):
 
 machine = model.sm.SynchronousMachineSaturated(
     n_p=2, R_s=.63, current=i_s, psi_s0=psi_s0)
-# Magnetically linear PMSyRM model for comparison
+# Magnetically linear PM-SyRM model for comparison
 # machine = model.sm.SynchronousMachine(
-#     n_p=2, R_s=.63, L_d=18e-3, L_q=110e-3, psi_f=.47)
+#    n_p=2, R_s=.63, L_d=18e-3, L_q=110e-3, psi_f=.47)
 mechanics = model.Mechanics(J=.015)
 converter = model.Inverter(u_dc=540)
 mdl = model.sm.Drive(machine, mechanics, converter)
+# mdl.pwm = model.CarrierComparison()  # Enable the PWM model
 
 # %%
 # Configure the control system.
@@ -178,7 +181,7 @@ def i_s(psi_s):
 # %%
 # Create the simulation object and simulate it.
 
-sim = model.Simulation(mdl, ctrl, pwm=False)
+sim = model.Simulation(mdl, ctrl)
 sim.simulate(t_stop=4)
 
 # %%

examples/flux_vector/plot_flux_vector_syrm_7kw.py

@@ -80,7 +80,7 @@ def i_s(psi_s):
 # %%
 # Create the simulation object and simulate it.
 
-sim = model.Simulation(mdl, ctrl, pwm=False)
+sim = model.Simulation(mdl, ctrl)
 sim.simulate(t_stop=4)
 
 # %%

examples/obs_vhz/plot_obs_vhz_ctrl_im_2kw.py

@@ -60,11 +60,9 @@
 # mdl.mech.tau_L_w = lambda w_M: np.sign(w_M)*k*w_M**2
 
 # %%
-# Create the simulation object and simulate it. You can also enable the PWM
-# model (which makes simulation slower). One-sampling-period computational
-# delay is modeled.
+# Create the simulation object and simulate it.
 
-sim = model.Simulation(mdl, ctrl, pwm=False, delay=1)
+sim = model.Simulation(mdl, ctrl)
 sim.simulate(t_stop=4)
 
 # %%

examples/obs_vhz/plot_obs_vhz_ctrl_pmsm_2kw.py

@@ -49,11 +49,9 @@
 mdl.mechanics.tau_L_t = Sequence(times, values)
 
 # %%
-# Create the simulation object and simulate it. You can also enable the PWM
-# model (which makes simulation slower). One-sampling-period computational
-# delay is modeled.
+# Create the simulation object and simulate it.
 
-sim = model.Simulation(mdl, ctrl, pwm=False, delay=1)
+sim = model.Simulation(mdl, ctrl)
 sim.simulate(t_stop=8)
 
 # %%

examples/obs_vhz/plot_obs_vhz_ctrl_pmsm_2kw_two_mass.py

@@ -5,8 +5,8 @@
 This example simulates observer-based V/Hz control of a 2.2-kW PMSM drive. The
 mechanical subsystem is modeled as a two-mass system. The resonance frequency
 of the mechanics is around 85 Hz. The mechanical parameters correspond to 
-[#Saa2015]_, except that the torsional damping is set to a smaller value in this 
-example.
+[#Saa2015]_, except that the torsional damping is set to a smaller value in 
+this example.
 
 """
 
@@ -57,7 +57,7 @@
 # %%
 # Create the simulation object and simulate it.
 
-sim = model.Simulation(mdl, ctrl, pwm=False)
+sim = model.Simulation(mdl, ctrl)
 sim.simulate(t_stop=1.2)
 # sphinx_gallery_thumbnail_number = 3
 plot(sim, base)  # Plot results in per-unit values

examples/obs_vhz/plot_obs_vhz_ctrl_pmsyrm_thor.py

@@ -118,11 +118,9 @@ def i_s(psi_s):
 # mdl.mechanics.tau_L_t = Sequence(times, values)
 
 # %%
-# Create the simulation object and simulate it. You can also enable the PWM
-# model (which makes simulation slower). One-sampling-period computational
-# delay is modeled.
+# Create the simulation object and simulate it.
 
-sim = model.Simulation(mdl, ctrl, pwm=False, delay=1)
+sim = model.Simulation(mdl, ctrl)
 sim.simulate(t_stop=8)
 
 # %%

examples/obs_vhz/plot_obs_vhz_ctrl_syrm_7kw.py

@@ -27,8 +27,8 @@
 # but the same model structure can also be used for other synchronous motors.
 # For PM motors, the magnetomotive force (MMF) of the PMs can be modeled using
 # constant current source `i_f` on the d-axis [#Awa2018]_, [#Jah1986]_.
-# Correspondingly, this approach assumes that the MMFs of the d-axis current and
-# of the PMs are in series. This model cannot capture the desaturation
+# Correspondingly, this approach assumes that the MMFs of the d-axis current
+# and of the PMs are in series. This model cannot capture the desaturation
 # phenomenon of thin iron ribs, see [#Arm2009]_ for details. For such motors,
 # look-up tables can be used.
 
@@ -104,11 +104,9 @@ def i_s(psi_s):
 mdl.mechanics.tau_L_t = Sequence(times, values)
 
 # %%
-# Create the simulation object and simulate it. You can also enable the PWM
-# model (which makes simulation slower). One-sampling-period computational
-# delay is modeled.
+# Create the simulation object and simulate it.
 
-sim = model.Simulation(mdl, ctrl, pwm=False, delay=1)
+sim = model.Simulation(mdl, ctrl)
 sim.simulate(t_stop=8)
 
 # %%

examples/signal_inj/plot_signal_inj_pmsm_2kw.py

@@ -54,7 +54,7 @@
 # %%
 # Create the simulation object and simulate it.
 
-sim = model.Simulation(mdl, ctrl, pwm=False)
+sim = model.Simulation(mdl, ctrl)
 sim.simulate(t_stop=4)
 
 # %%

examples/signal_inj/plot_signal_inj_syrm_7kw.py

@@ -46,7 +46,7 @@
 
 # Speed reference
 times = np.array([0, .25, .25, .375, .5, .625, .75, .75, 1])*4
-values = np.array([0, 0, 1, 1, 0, -1, -1, 0, 0])*base.w*.1
+values = np.array([0, 0, 1, 1, 0, -1, -1, 0, 0])*.1*base.w
 ctrl.w_m_ref = Sequence(times, values)
 # External load torque
 times = np.array([0, .125, .125, .875, .875, 1])*4
@@ -56,7 +56,7 @@
 # %%
 # Create the simulation object and simulate it.
 
-sim = model.Simulation(mdl, ctrl, pwm=False)
+sim = model.Simulation(mdl, ctrl)
 sim.simulate(t_stop=4)
 
 # %%

examples/vector/plot_vector_ctrl_im_2kw.py

@@ -31,7 +31,7 @@
 
 def L_s(psi):
     """
-    Stator inductance saturation model for a 2.2-kW machine.
+    Stator inductance saturation model for a 2.2-kW induction machine.
 
     Parameters
     ----------
@@ -59,13 +59,15 @@ def L_s(psi):
     n_p=2, R_s=3.7, R_r=2.5, L_ell=.023, L_s=L_s)
 # Unsaturated machine model, using its inverse-Γ parameters (uncomment to try)
 # machine = model.im.InductionMachineInvGamma(
-#    R_s=3.7, R_R=2.1, L_sgm=.021, L_M=.224, n_p=2)
+#    n_p=2, R_s=3.7, R_R=2.1, L_sgm=.021, L_M=.224)
 # Alternatively, configure the machine model using its Γ parameters
 # machine = model.im.InductionMachine(
-#     R_s=3.7, R_r=2.5, L_ell=.023, L_s=.245, n_p=2)
+#     n_p=2, R_s=3.7, R_r=2.5, L_ell=.023, L_s=.245)
 mechanics = model.Mechanics(J=.015)
 converter = model.Inverter(u_dc=540)
 mdl = model.im.Drive(machine, mechanics, converter)
+# mdl.pwm = model.CarrierComparison()  # Try to enable the PWM model
+# mdl.delay = model.Delay(2)  # Try longer computational delay
 
 # %%
 # Configure the control system.
@@ -94,22 +96,11 @@ def L_s(psi):
 # ctrl.w_m_ref = lambda t: (t > .2)*(2*base.w)
 # mdl.mechanics.tau_L_t = lambda t: 0
 
-# Speed reversals under the rated load (uncomment to try, change t_stop=8 below)
-# import numpy as np
-# from motulator.helpers import Sequence
-# times = np.array([0, .125, .25, .375, .5, .625, .75, .875, 1])*8
-# values = np.array([0, 0, 1, 1, 0, -1, -1, 0, 0])*base.w
-# ctrl.w_m_ref = Sequence(times, values)
-# # External load torque
-# times = np.array([0, .125, .125, .875, .875, 1])*8
-# values = np.array([0, 0, 1, 1, 0, 0])*base.tau_nom
-# mdl.mechanics.tau_L_t = Sequence(times, values)
-
 # %%
 # Create the simulation object and simulate it. You can also enable the PWM
 # model (which makes simulation slower).
 
-sim = model.Simulation(mdl, ctrl, pwm=False)
+sim = model.Simulation(mdl, ctrl)
 sim.simulate(t_stop=1.5)
 
 # %%

examples/vector/plot_vector_ctrl_pmsm_2kw.py

@@ -1,17 +1,16 @@
 """
-2.2-kW PMSM
-===========
+2.2-kW PMSM, diode bridge
+=========================
 
-This example simulates sensorless vector control of a 2.2-kW PMSM drive.
+This example simulates sensorless vector control of a 2.2-kW PMSM drive. 
 
 """
 
 # %%
 # Imports.
 
-import numpy as np
 from motulator import model, control
-from motulator import BaseValues, Sequence, plot, plot_extra
+from motulator import BaseValues, plot
 
 # %%
 # Compute base values based on the nominal values (just for figures).
@@ -27,6 +26,7 @@
 mechanics = model.Mechanics(J=.015)
 converter = model.Inverter(u_dc=540)
 mdl = model.sm.Drive(machine, mechanics, converter)
+# mdl.pwm = model.CarrierComparison()  # Enable the PWM model
 
 # %%
 # Configure the control system.
@@ -40,30 +40,15 @@
 # Set the speed reference and the external load torque.
 
 # Speed reference
-times = np.array([0, .125, .25, .375, .5, .625, .75, .875, 1])*4
-values = np.array([0, 0, 1, 1, 0, -1, -1, 0, 0])*base.w
-ctrl.w_m_ref = Sequence(times, values)
-# External load torque
-times = np.array([0, .125, .125, .875, .875, 1])*4
-values = np.array([0, 0, 1, 1, 0, 0])*base.tau_nom
-mdl.mechanics.tau_L_t = Sequence(times, values)
+ctrl.w_m_ref = lambda t: (t > .2)*2*base.w
 
-# mdl.mechanics.tau_L_t = lambda t: (t > .8)*base.tau_nom*.7
-# ctrl.w_m_ref = lambda t: (t > .2)*(2*base.w)
+# External load torque
+mdl.mechanics.tau_L_t = lambda t: (t > .8)*.7*base.tau_nom
 
 # %%
 # Create the simulation object and simulate it.
 
-# Simulate the system without modeling PWM
-sim = model.Simulation(mdl, ctrl, pwm=False)
-sim.simulate(t_stop=4)
-plot(sim, base)  # Plot results in per-unit values.
-
-# Repeat the same simulation with PWM model enabled (takes a bit longer)
-mdl.clear()  # First clear the stored data from the previous simulation run
-ctrl.clear()
-sim = model.Simulation(mdl, ctrl, pwm=True)
-sim.simulate(t_stop=4)
+# Simulate the system and plot results in per-unit values
+sim = model.Simulation(mdl, ctrl)
+sim.simulate(t_stop=1.4)
 plot(sim, base)
-# Plot a zoomed view
-plot_extra(sim, t_span=(1.1, 1.125), base=base)

examples/vector/plot_vector_ctrl_pmsm_2kw_diode.py

@@ -0,0 +1,58 @@
+"""
+2.2-kW PMSM, diode bridge
+=========================
+
+This example simulates sensorless vector control of a 2.2-kW PMSM drive, 
+equipped with a diode bridge rectifier. 
+
+"""
+
+# %%
+# Imports.
+
+from motulator import model, control
+from motulator import BaseValues, plot, plot_extra
+
+# %%
+# Compute base values based on the nominal values (just for figures).
+
+base = BaseValues(
+    U_nom=370, I_nom=4.3, f_nom=75, tau_nom=14, P_nom=2.2e3, n_p=3)
+
+# %%
+# Configure the system model.
+
+machine = model.sm.SynchronousMachine(
+    n_p=3, R_s=3.6, L_d=.036, L_q=.051, psi_f=.545)
+mechanics = model.Mechanics(J=.015)
+converter = model.FrequencyConverter(L=2e-3, C=235e-6, U_g=400, f_g=50)
+mdl = model.sm.DriveWithDiodeBridge(machine, mechanics, converter)
+mdl.pwm = model.CarrierComparison()  # Enable the PWM model
+
+# %%
+# Configure the control system.
+
+par = control.sm.ModelPars(
+    n_p=3, R_s=3.6, L_d=.036, L_q=.051, psi_f=.545, J=.015)
+ref = control.sm.CurrentReferencePars(par, w_m_nom=base.w, i_s_max=1.5*base.i)
+ctrl = control.sm.VectorCtrl(par, ref, T_s=250e-6, sensorless=True)
+
+# %%
+# Set the speed reference and the external load torque.
+
+# Speed reference
+ctrl.w_m_ref = lambda t: (t > .2)*base.w
+
+# External load torque
+mdl.mechanics.tau_L_t = lambda t: (t > .6)*base.tau_nom
+
+# %%
+# Create the simulation object and simulate it.
+
+# Simulate the system
+sim = model.Simulation(mdl, ctrl)
+sim.simulate(t_stop=1)
+
+# Plot results in per-unit values
+plot(sim, base)
+plot_extra(sim, base, t_span=(.8, .825))