5.6-kW PM-SyRM, train flux map, FEM data, no spatial harmonics

This example trains a GradNet flux-linkage map for a four-pole 5.6-kW PM synchronous reluctance machine (Baldor ECS101M0H7EF4) from a FEM dataset without spatial harmonics.

from pathlib import Path

import motulator.drive.gradnet as gn
from motulator.drive import utils

from motulator_gradnet import (
    PNormGradient,
    get_training_data,
    print_flux_map_errors_meas,
    train_gradnet,
)

Compute base values based on the nominal values.

nom = utils.NominalValues(U=460, I=8.8, f=60, P=5.6e3, tau=29.7)
base = utils.BaseValues.from_nominal(nom, n_p=2)

Set up the paths and parameters.

p = Path(__file__).resolve().parent if "__file__" in globals() else Path.cwd()

dataset_path = p / "datasets/baldor_fem.npz"
trained_path = p / "trained_models/baldor_fem_flux_map_pnorm_d12_sub20.npz"
subsample = 20

activation = PNormGradient

Train the model. The FEM data is on a uniform grid, so the loss is dominated by the saturated region. The loss of the data points whose current magnitude is below 12 A is weighted by 10 to improve the accuracy at low currents.

if not trained_path.exists():
    train_gradnet(
        dataset_path=dataset_path,
        base=base,
        save_model_path=trained_path,
        is_flux_map=True,
        embed_dim=12,
        epochs=2000,
        subsample=subsample,
        activation=activation,
        i_low=12,
        weight_low=10,
    )

Create the GradNet model and its callable.

model = gn.load_gradnet(trained_path)
flux_map_fcn = gn.FluxMap(model)

Load the validation data, i.e., the complement of the training data.

_, val_data = get_training_data(str(dataset_path), base=base, subsample=subsample)

Print statistical error metrics.

print_flux_map_errors_meas(flux_map_fcn, val_data, base=base)