.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "drive_examples/gradnet/plot_6kw_pmsyrm_flux_map_fem_harm.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code. .. rst-class:: sphx-glr-example-title .. _sphx_glr_drive_examples_gradnet_plot_6kw_pmsyrm_flux_map_fem_harm.py: 5.6-kW PM-SyRM, train flux map, FEM data with spatial harmonics =============================================================== This example trains a GradNet flux-linkage map for a four-pole 5.6-kW PM synchronous reluctance machine (ABB Baldor ECS101M0H7EF4) from a FEM dataset with spatial harmonics. .. GENERATED FROM PYTHON SOURCE LINES 9-17 .. code-block:: Python from pathlib import Path import numpy as np import motulator.drive.gradnet as gn from motulator.drive import utils .. GENERATED FROM PYTHON SOURCE LINES 18-19 Set nominal and base values. .. GENERATED FROM PYTHON SOURCE LINES 19-23 .. code-block:: Python 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) .. GENERATED FROM PYTHON SOURCE LINES 24-25 Set up the paths and parameters. .. GENERATED FROM PYTHON SOURCE LINES 25-28 .. code-block:: Python p = Path(__file__).resolve().parent if "__file__" in globals() else Path.cwd() .. GENERATED FROM PYTHON SOURCE LINES 29-30 Flux map with spatial harmonics, trained on FEM data. .. GENERATED FROM PYTHON SOURCE LINES 30-37 .. code-block:: Python dataset_path = p / "datasets/baldor_fem.npz" trained_path = p / "trained_models/baldor_fem_flux_map_harm_pnorm_d48_sub10.pth" subsample = 10 k = 6 activation = gn.PNormGradient .. GENERATED FROM PYTHON SOURCE LINES 38-39 Train the model. .. GENERATED FROM PYTHON SOURCE LINES 39-52 .. code-block:: Python if not trained_path.exists(): gn.train_gradnet( dataset_path=dataset_path, base=base, save_model_path=trained_path, is_flux_map=True, k=k, embed_dim=48, epochs=1000, subsample=subsample, activation=activation, ) .. GENERATED FROM PYTHON SOURCE LINES 53-54 Load the dataset for visualization comparison. .. GENERATED FROM PYTHON SOURCE LINES 54-68 .. code-block:: Python # Get the training and validation data (complement) from the helper function # Note: get_training_data returns (psi, i, ...), but we need (i, psi, ...) (trn_psi, trn_i, trn_theta, trn_tau), (val_psi, val_i, val_theta, val_tau) = ( gn.get_training_data( str(dataset_path), base=base, subsample=subsample, other_keys=["theta_m", "tau_m"], ) ) trn_data = (trn_i, trn_psi, trn_theta, trn_tau) val_data = (val_i, val_psi, val_theta, val_tau) .. GENERATED FROM PYTHON SOURCE LINES 69-70 Load the GradNet model and create its callable. .. GENERATED FROM PYTHON SOURCE LINES 70-74 .. code-block:: Python model = gn.load_gradnet(trained_path, activation=activation) harm_map = gn.FluxMapWithHarmonics(model, k=k) .. GENERATED FROM PYTHON SOURCE LINES 75-76 Evaluate the model at single point. .. GENERATED FROM PYTHON SOURCE LINES 76-85 .. code-block:: Python i_s_dq_mtpa = -9 + 9j # Approximately the rated MTPA current theta_m = np.deg2rad(30) psi_s_dq, tau_m = harm_map(i_s_dq_mtpa, np.exp(1j * theta_m)) print(f"Current: {i_s_dq_mtpa:.2f} A") print(f"Angle: {np.rad2deg(theta_m):.2f} deg") print(f"Flux linkage: {psi_s_dq:.2f} Vs") print(f"Torque per pole pair: {tau_m:.2f} Nm") .. rst-class:: sphx-glr-script-out .. code-block:: none Current: -9.00+9.00j A Angle: 30.00 deg Flux linkage: 0.29+0.82j Vs Torque per pole pair: 13.69 Nm .. GENERATED FROM PYTHON SOURCE LINES 86-87 Plot torque surface. .. GENERATED FROM PYTHON SOURCE LINES 87-110 .. code-block:: Python # Define ranges for visualization i_q_range = np.linspace(0, 2 * base.i, 50) theta_m_range = np.linspace(0, 2 * np.pi / k, 50) # Plot torque as a function of i_q and theta_m at fixed i_d gn.plot_surface_vs_current_and_angle( current_range=i_q_range, fixed_value=i_s_dq_mtpa.real, theta_m_range=theta_m_range, map_fcn=harm_map, input="i_q", output="tau_m", val_data=val_data, trn_data=trn_data, opts=gn.PlotOptions( base=base, lims={"x": (0, 2), "y": (0, 60), "z": (0, 1.5)}, ticks={"x": [0, 1, 2], "y": [0, 30, 60], "z": [0, 0.5, 1.0, 1.5]}, loci_levels_source="val", ), ) .. image-sg:: /drive_examples/gradnet/images/sphx_glr_plot_6kw_pmsyrm_flux_map_fem_harm_001.png :alt: plot 6kw pmsyrm flux map fem harm :srcset: /drive_examples/gradnet/images/sphx_glr_plot_6kw_pmsyrm_flux_map_fem_harm_001.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 111-112 Plot torque vs angle. .. GENERATED FROM PYTHON SOURCE LINES 112-127 .. code-block:: Python theta_m_range = np.linspace(0, 2 * np.pi / k, 120) gn.plot_output_vs_angle( fixed_value=i_s_dq_mtpa, theta_m_range=theta_m_range, map_fcn=harm_map, val_data=val_data, trn_data=trn_data, opts=gn.PlotOptions( base=base, lims={"x": (0, 60), "y": (0, 1)}, ticks={"x": [0, 15, 30, 45, 60], "y": [0, 0.2, 0.4, 0.6, 0.8, 1.0]}, ), ) .. image-sg:: /drive_examples/gradnet/images/sphx_glr_plot_6kw_pmsyrm_flux_map_fem_harm_002.png :alt: plot 6kw pmsyrm flux map fem harm :srcset: /drive_examples/gradnet/images/sphx_glr_plot_6kw_pmsyrm_flux_map_fem_harm_002.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 128-129 Print statistical error metrics on validation data. .. GENERATED FROM PYTHON SOURCE LINES 129-137 .. code-block:: Python val_dict = { "i_s_dq": val_i, "psi_s_dq": val_psi, "theta_m": val_theta, "tau_m": val_tau, } gn.print_flux_map_errors_fem(map_fcn=harm_map, raw_data=val_dict, base=base) .. rst-class:: sphx-glr-script-out .. code-block:: none Error metrics: Flux linkage: rmse=0.018 p.u., max=0.071 p.u., std=0.009 p.u. Torque: rmse=0.016 p.u., max=0.115 p.u., std=0.011 p.u. .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 0.380 seconds) .. _sphx_glr_download_drive_examples_gradnet_plot_6kw_pmsyrm_flux_map_fem_harm.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_6kw_pmsyrm_flux_map_fem_harm.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_6kw_pmsyrm_flux_map_fem_harm.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_6kw_pmsyrm_flux_map_fem_harm.zip ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_