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Flux-vector control for induction machines #137
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""" | ||
2.2-kW AM | ||
=========== | ||
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This example simulates sensorless flux-vector control of a 2.2-kW induction machine drive. | ||
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""" | ||
# %% | ||
import numpy as np | ||
from motulator.common.utils import Sequence | ||
from motulator.drive import model | ||
import motulator.drive.control.im as control | ||
from motulator.drive.utils import ( | ||
BaseValues, NominalValues, plot, InductionMachineInvGammaPars, InductionMachinePars) | ||
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# %% | ||
# Compute base values based on the nominal values (just for figures). | ||
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nom = NominalValues(U=400, I=5, f=50, P=2.2e3, tau=14.6) | ||
base = BaseValues.from_nominal(nom, n_p=2) | ||
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# %% | ||
# Configure the system model. | ||
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# Unsaturated machine model, using its inverse-Γ parameters | ||
par = InductionMachineInvGammaPars( | ||
n_p=2, R_s=3.7, R_R=2.1, L_sgm=.021, L_M=.224) | ||
mdl_par = InductionMachinePars.from_inv_gamma_model_pars(par) | ||
machine = model.InductionMachine(mdl_par) | ||
mechanics = model.StiffMechanicalSystem(J=.015) | ||
converter = model.Inverter(u_dc=540) | ||
mdl = model.Drive(converter, machine, mechanics) | ||
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# %% | ||
# Configure the control system. | ||
# Set nominal values and limits for reference generation | ||
cfg = control.FluxVectorControlCfg(base.u, base.w, base.tau) | ||
ctrl = control.FluxVectorControl(par, cfg, J=.015, T_s=250e-6, sensorless=True) | ||
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# %% | ||
# Set the speed reference and the external load torque. | ||
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# Speed reference (electrical rad/s) | ||
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.ref.w_m = 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])*nom.tau | ||
mdl.mechanics.tau_L = Sequence(times, values) | ||
# %% | ||
# Create the simulation object and simulate it. | ||
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sim = model.Simulation(mdl, ctrl) | ||
sim.simulate(t_stop=4) | ||
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# %% | ||
# Plot results in per-unit values. | ||
plot(sim, base) |
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"""Flux-vector control of synchronous machine drives.""" | ||
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from dataclasses import dataclass, InitVar | ||
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import numpy as np | ||
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from motulator.drive.control import DriveControlSystem, SpeedController | ||
from motulator.drive.control.im._common import Observer, ObserverCfg | ||
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class FluxVectorControlCfg: | ||
""" | ||
Controller configuration. | ||
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Parameters | ||
---------- | ||
nom_u_s : float | ||
Nominal voltage | ||
nom_w_s : float | ||
Nominal speed | ||
tau_max : float | ||
Maximum torque reference | ||
k_u : float, optional | ||
Voltage utilization factor. The default is 0.95. | ||
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""" | ||
def __init__(self, nom_u_s, nom_w_s, nom_tau_M, k_u = 0.95) -> None: | ||
self.nom_u_s = nom_u_s | ||
self.nom_w_s = nom_w_s | ||
self.tau_max = nom_tau_M * 1.5 | ||
self.nom_psi_s = nom_u_s/nom_w_s | ||
self.k_u = k_u | ||
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# %% | ||
class FluxVectorControl(DriveControlSystem): | ||
""" | ||
Flux-vector control of asynchronous machine drives. | ||
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Parameters | ||
---------- | ||
par : InductionMachineInvGammaPars | ||
Machine model parameters. | ||
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alpha_psi : float, optional | ||
Bandwidth of the flux controller (rad/s). The default is 2*pi*100. | ||
alpha_tau : float, optional | ||
Bandwidth of the torque controller (rad/s). The default is 2*pi*200. | ||
alpha_o : float, optional | ||
Observer bandwidth (rad/s). The default is 2*pi*40. | ||
J : float, optional | ||
Moment of inertia (kgm²). Needed only for the speed controller. | ||
T_s : float | ||
Sampling period (s). The default is 250e-6. | ||
sensorless : bool, optional | ||
If True, sensorless control is used. The default is True. | ||
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References | ||
---------- | ||
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""" | ||
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def __init__( | ||
self, | ||
par, | ||
cfg: FluxVectorControlCfg, | ||
alpha_psi=2*np.pi*100, | ||
alpha_tau=2*np.pi*200, | ||
alpha_o=2*np.pi*40, | ||
J=None, | ||
T_s=250e-6, | ||
sensorless=True): | ||
super().__init__(par, T_s, sensorless) | ||
self.cfg = cfg | ||
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if J is not None: | ||
self.speed_ctrl = SpeedController(J, 2*np.pi*4) | ||
else: | ||
self.speed_ctrl = None | ||
self.observer = Observer( | ||
ObserverCfg(par, T_s, sensorless, alpha_o)) | ||
# Bandwidths | ||
self.alpha_psi = alpha_psi | ||
self.alpha_tau = alpha_tau | ||
self.k = 0 | ||
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def get_flux_reference(self, fbk): | ||
"""Simple field-weakening with flux-magnitude | ||
reduced inversely proportional to the speed at speeds beyond the nominal.""" | ||
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max_u_s = self.cfg.k_u*fbk.u_dc/np.sqrt(3) | ||
max_psi_s = max_u_s/np.abs(fbk.w_s) if fbk.w_s != 0 else np.inf | ||
return np.min([max_psi_s, self.cfg.nom_psi_s]) | ||
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def output(self, fbk): | ||
"""Calculate references.""" | ||
par = self.par | ||
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# Get the references from the outer loop | ||
ref = super().output(fbk) | ||
ref = super().get_torque_reference(fbk, ref) | ||
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# Compute flux and torque references | ||
ref.psi_s = self.get_flux_reference(fbk) | ||
ref.tau_M = np.clip(ref.tau_M, -self.cfg.tau_max, self.cfg.tau_max) | ||
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# Torque estimates | ||
tau_M = 1.5*par.n_p*np.imag(fbk.i_s*np.conj(fbk.psi_s)) | ||
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# Torque-production factor, c_tau = 0 corresponds to the MTPV condition | ||
c_tau = 1.5*par.n_p*np.real(fbk.psi_R*np.conj(fbk.psi_s)) | ||
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# References for the flux and torque controllers | ||
v_psi = self.alpha_psi*(ref.psi_s - np.abs(fbk.psi_s)) | ||
v_tau = self.alpha_tau*(ref.tau_M - tau_M) | ||
if c_tau > 0: | ||
v = ( | ||
1.5*par.n_p*np.abs(fbk.psi_s)*fbk.psi_R*v_psi + | ||
1j*fbk.psi_s*par.L_sgm*v_tau)/c_tau | ||
else: | ||
v = v_psi | ||
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# Stator voltage reference | ||
ref.u_s = par.R_s*fbk.i_s + 1j*(fbk.w_m + fbk.w_r)*fbk.psi_s + v | ||
u_ss = ref.u_s*np.exp(1j*fbk.theta_s) | ||
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more.
This is coming from the observer. Could be computed here according to eq. (4c), i.e. \omegarb L_sgm \hattauM / (\hatpsis^T\hatpsiR * par.n_p * 1.5). Did not observe any real difference between the two implementations. |
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ref.d_abc = self.pwm(ref.T_s, u_ss, fbk.u_dc, fbk.w_s) | ||
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return ref | ||
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ref.tau_M = np.clip(ref.tau_M, -self.cfg.tau_max, self.cfg.tau_max)
Something better should be implemented for field-weakening operation.