Update documentation

docs/_downloads/01a31468f0628d249938535bd3e8e129/plot_flux_vector_pmsyrm_5kw.py

@@ -2,12 +2,12 @@
 5.5-kW PM-SyRM, saturated
 =========================
 
-This example simulates sensorless stator-flux-vector control of a 5.5-kW 
-PM-SyRM (Baldor ECS101M0H7EF4) drive. The machine model is parametrized using 
-the algebraic saturation model from [#Lel2024]_, 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 
+This example simulates sensorless stator-flux-vector control of a 5.5-kW
+PM-SyRM (Baldor ECS101M0H7EF4) drive. The machine model is parametrized using
+the algebraic saturation model from [#Lel2024]_, 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.
 
 """
@@ -41,11 +41,11 @@
 def i_s(psi_s):
     """
     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. 
+
+    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.
 
     Parameters
     ----------
@@ -59,9 +59,9 @@ def i_s(psi_s):
 
     Notes
     -----
-    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.  
+    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

docs/_downloads/04c5141a67584a6524f8c198ae23ef35/plot_flux_vector_syrm_7kw.ipynb

@@ -4,7 +4,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# 6.7-kW SyRM, saturated, disturbance estimation\n\nThis example simulates sensorless stator-flux-vector control of a saturated\n6.7-kW synchronous reluctance motor drive. The saturation is not taken into\naccount in the control method (only in the system model). Even if the machine \nhas no magnets, the PM-flux disturbance estimation is enabled [#Tuo2018]_. In \nthis case, this PM-flux estimate lumps the effects of inductance errors. \nNaturally, the PM-flux estimation can be used in PM machine drives as well. \n"
+        "\n# 6.7-kW SyRM, saturated, disturbance estimation\n\nThis example simulates sensorless stator-flux-vector control of a saturated\n6.7-kW synchronous reluctance motor drive. The saturation is not taken into\naccount in the control method (only in the system model). Even if the machine\nhas no magnets, the PM-flux disturbance estimation is enabled [#Tuo2018]_. In\nthis case, this PM-flux estimate lumps the effects of inductance errors.\nNaturally, the PM-flux estimation can be used in PM machine drives as well.\n"
       ]
     },
     {

docs/_downloads/063cf0c7a6cd6584d8b416190228666c/plot_gfl_lcl_10kva.ipynb

@@ -4,7 +4,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# 10-kVA converter, LCL filter\n    \nThis example simulates a grid-following-controlled converter connected to a\nstrong grid through an LCL filter. The control system includes a phase-locked\nloop (PLL) to synchronize with the grid, a current reference generator, and a\nPI-type current controller. The dynamics of the LCL filter are not taken into\naccount in the control system.\n"
+        "\n# 10-kVA converter, LCL filter\n\nThis example simulates a grid-following-controlled converter connected to a\nstrong grid through an LCL filter. The control system includes a phase-locked\nloop (PLL) to synchronize with the grid, a current reference generator, and a\nPI-type current controller. The dynamics of the LCL filter are not taken into\naccount in the control system.\n"
       ]
     },
     {

docs/_downloads/0e8207fbfefb0b923d7f22208bd3cd83/plot_obs_vhz_ctrl_pmsm_2kw_two_mass.py

@@ -4,8 +4,8 @@
 
 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 
+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.
 
 """

docs/_downloads/11388a34fc999c2467fbd17556f5ee11/plot_gfm_rfpsc_13kva.ipynb

@@ -4,7 +4,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# 12.5-kVA converter, RFPSC\n    \nThis example simulates reference-feedforward power-synchronization control \n(RFPSC) of a converter connected to a weak grid. \n"
+        "\n# 12.5-kVA converter, RFPSC\n\nThis example simulates reference-feedforward power-synchronization control\n(RFPSC) of a converter connected to a weak grid.\n"
       ]
     },
     {

docs/_downloads/16706dd8bc863891161047a58b1fa773/plot_gfl_dc_bus_10kva.py

@@ -1,10 +1,10 @@
 """
 10-kVA converter, DC-bus voltage
 ================================
-    
+
 This example simulates a grid-following-controlled converter connected to a
-strong grid and regulating the DC-bus voltage. The control system includes a 
-DC-bus voltage controller, a phase-locked loop (PLL) to synchronize with the 
+strong grid and regulating the DC-bus voltage. The control system includes a
+DC-bus voltage controller, a phase-locked loop (PLL) to synchronize with the
 grid, a current reference generator, and a PI-type current controller.
 
 """

docs/_downloads/1901236edeea2d2b57c3aeb0455fdb6c/plot_vhz_ctrl_im_2kw.ipynb

@@ -4,7 +4,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# 2.2-kW induction motor, diode bridge\n\nA diode bridge, stiff three-phase grid, and a DC link is modeled. The default\nparameters in this example yield open-loop V/Hz control. \n"
+        "\n# 2.2-kW induction motor, diode bridge\n\nA diode bridge, stiff three-phase grid, and a DC link is modeled. The default\nparameters in this example yield open-loop V/Hz control.\n"
       ]
     },
     {

docs/_downloads/1a774b5e6ea2682176bf1201b07c962e/plot_obs_vhz_ctrl_syrm_7kw.ipynb

@@ -4,7 +4,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# 6.7-kW SyRM, saturated\n\nThis example simulates observer-based V/Hz control of a saturated 6.7-kW\nsynchronous reluctance motor drive. The saturation is not taken into account in \nthe control method (only in the system model).\n"
+        "\n# 6.7-kW SyRM, saturated\n\nThis example simulates observer-based V/Hz control of a saturated 6.7-kW\nsynchronous reluctance motor drive. The saturation is not taken into account in\nthe control method (only in the system model).\n"
       ]
     },
     {
@@ -51,7 +51,7 @@
       },
       "outputs": [],
       "source": [
-        "def i_s(psi_s):\n    \"\"\"\n    Magnetic model for a 6.7-kW synchronous reluctance motor.\n\n    Parameters\n    ----------\n    psi_s : complex\n        Stator flux linkage (Vs).\n\n    Returns\n    -------\n    complex\n        Stator current (A).\n\n    Notes\n    -----\n    For nonzero `i_f`, the initial value of the stator flux linkage `psi_s0` \n    needs to be solved, e.g., as follows::\n\n    from scipy.optimize import minimize_scalar\n    res = minimize_scalar(\n        lambda psi_d: np.abs(\n                    (a_d0 + a_dd*np.abs(psi_d)**S)*psi_d - i_f))\n    psi_s0 = complex(res.x)\n\n    \"\"\"\n    # Parameters\n    a_d0, a_dd, S = 17.4, 373., 5  # d-axis self-saturation\n    a_q0, a_qq, T = 52.1, 658., 1  # q-axis self-saturation\n    a_dq, U, V = 1120., 1, 0  # Cross-saturation\n    i_f = 0  # MMF of PMs\n    # Inverse inductance functions\n    G_d = a_d0 + a_dd*np.abs(psi_s.real)**S + (\n        a_dq/(V + 2)*np.abs(psi_s.real)**U*np.abs(psi_s.imag)**(V + 2))\n    G_q = a_q0 + a_qq*np.abs(psi_s.imag)**T + (\n        a_dq/(U + 2)*np.abs(psi_s.real)**(U + 2)*np.abs(psi_s.imag)**V)\n    # Stator current\n    return G_d*psi_s.real - i_f + 1j*G_q*psi_s.imag"
+        "def i_s(psi_s):\n    \"\"\"\n    Magnetic model for a 6.7-kW synchronous reluctance motor.\n\n    Parameters\n    ----------\n    psi_s : complex\n        Stator flux linkage (Vs).\n\n    Returns\n    -------\n    complex\n        Stator current (A).\n\n    Notes\n    -----\n    For nonzero `i_f`, the initial value of the stator flux linkage `psi_s0`\n    needs to be solved, e.g., as follows::\n\n    from scipy.optimize import minimize_scalar\n    res = minimize_scalar(\n        lambda psi_d: np.abs(\n                    (a_d0 + a_dd*np.abs(psi_d)**S)*psi_d - i_f))\n    psi_s0 = complex(res.x)\n\n    \"\"\"\n    # Parameters\n    a_d0, a_dd, S = 17.4, 373., 5  # d-axis self-saturation\n    a_q0, a_qq, T = 52.1, 658., 1  # q-axis self-saturation\n    a_dq, U, V = 1120., 1, 0  # Cross-saturation\n    i_f = 0  # MMF of PMs\n    # Inverse inductance functions\n    G_d = a_d0 + a_dd*np.abs(psi_s.real)**S + (\n        a_dq/(V + 2)*np.abs(psi_s.real)**U*np.abs(psi_s.imag)**(V + 2))\n    G_q = a_q0 + a_qq*np.abs(psi_s.imag)**T + (\n        a_dq/(U + 2)*np.abs(psi_s.real)**(U + 2)*np.abs(psi_s.imag)**V)\n    # Stator current\n    return G_d*psi_s.real - i_f + 1j*G_q*psi_s.imag"
       ]
     },
     {

docs/_downloads/27bdd99b6f85e00ef0728d1aecd88506/plot_obs_vhz_ctrl_pmsyrm_thor.py

@@ -10,8 +10,8 @@
 
 The SyR-e project has been licensed under the Apache License, Version 2.0. We
 acknowledge the developers of the SyR-e project. The flux maps from other
-sources can be used in a similar manner. It is worth noticing that the 
-saturation is not taken into account in the control method, only in the system 
+sources can be used in a similar manner. It is worth noticing that the
+saturation is not taken into account in the control method, only in the system
 model. Naturally, the control performance could be improved by taking the
 saturation into account in the control algorithm.
 

docs/_downloads/30af500ff87488650139aa5684d4e812/plot_flux_vector_pmsyrm_5kw.ipynb

@@ -4,7 +4,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# 5.5-kW PM-SyRM, saturated\n\nThis example simulates sensorless stator-flux-vector control of a 5.5-kW \nPM-SyRM (Baldor ECS101M0H7EF4) drive. The machine model is parametrized using \nthe algebraic saturation model from [#Lel2024]_, fitted to the flux linkage \nmaps measured using the constant-speed test. For comparison, the measured data \nis plotted together with the model predictions. Notice that the control system \nused in this example does not consider the saturation, only the system model \ndoes.\n"
+        "\n# 5.5-kW PM-SyRM, saturated\n\nThis example simulates sensorless stator-flux-vector control of a 5.5-kW\nPM-SyRM (Baldor ECS101M0H7EF4) drive. The machine model is parametrized using\nthe algebraic saturation model from [#Lel2024]_, fitted to the flux linkage\nmaps measured using the constant-speed test. For comparison, the measured data\nis plotted together with the model predictions. Notice that the control system\nused in this example does not consider the saturation, only the system model\ndoes.\n"
       ]
     },
     {
@@ -51,7 +51,7 @@
       },
       "outputs": [],
       "source": [
-        "# pylint: disable=too-many-locals\ndef i_s(psi_s):\n    \"\"\"\n    Saturation model for a 5.5-kW PM-SyRM.\n    \n    This model takes into account the bridge saturation in addition to the \n    regular self- and cross-saturation effects of the d- and q-axis. The bridge \n    saturation model is based on a nonlinear reluctance element in parallel \n    with the Norton-equivalent PM model. \n\n    Parameters\n    ----------\n    psi_s : complex\n        Stator flux linkage (Vs).\n\n    Returns\n    -------\n    complex\n        Stator current (A).\n\n    Notes\n    -----\n    For simplicity, the saturation model parameters are hard-coded in the \n    function below. This model can also be used for other PM-SyRMs by changing \n    the model parameters.  \n\n    \"\"\"\n    # d-axis self-saturation\n    a_d0, a_dd, S = 3.96, 28.46, 4\n    # q-axis self-saturation\n    a_q0, a_qq, T = 5.89, 2.672, 6\n    # Cross-saturation\n    a_dq, U, V = 41.52, 1, 1\n    # PM model and bridge saturation\n    a_b, a_bp, k_q, psi_n, W = 81.75, 1, .1, .804, 2\n\n    # Inverse inductance functions for the d- and q-axis\n    G_d = a_d0 + a_dd*np.abs(psi_s.real)**S + (\n        a_dq/(V + 2)*np.abs(psi_s.real)**U*np.abs(psi_s.imag)**(V + 2))\n    G_q = a_q0 + a_qq*np.abs(psi_s.imag)**T + (\n        a_dq/(U + 2)*np.abs(psi_s.real)**(U + 2)*np.abs(psi_s.imag)**V)\n\n    # Bridge flux\n    psi_b = psi_s.real - psi_n\n    # State of the bridge saturation depends also on the q-axis flux\n    psi_b_sat = np.sqrt(psi_b**2 + k_q*psi_s.imag**2)\n    # Inverse inductance function for the bridge saturation\n    G_b = a_b*psi_b_sat**W/(1 + a_bp*psi_b_sat**W)\n\n    # Stator current\n    return G_d*psi_s.real + G_b*psi_b + 1j*(G_q + k_q*G_b)*psi_s.imag"
+        "# pylint: disable=too-many-locals\ndef i_s(psi_s):\n    \"\"\"\n    Saturation model for a 5.5-kW PM-SyRM.\n\n    This model takes into account the bridge saturation in addition to the\n    regular self- and cross-saturation effects of the d- and q-axis. The bridge\n    saturation model is based on a nonlinear reluctance element in parallel\n    with the Norton-equivalent PM model.\n\n    Parameters\n    ----------\n    psi_s : complex\n        Stator flux linkage (Vs).\n\n    Returns\n    -------\n    complex\n        Stator current (A).\n\n    Notes\n    -----\n    For simplicity, the saturation model parameters are hard-coded in the\n    function below. This model can also be used for other PM-SyRMs by changing\n    the model parameters.\n\n    \"\"\"\n    # d-axis self-saturation\n    a_d0, a_dd, S = 3.96, 28.46, 4\n    # q-axis self-saturation\n    a_q0, a_qq, T = 5.89, 2.672, 6\n    # Cross-saturation\n    a_dq, U, V = 41.52, 1, 1\n    # PM model and bridge saturation\n    a_b, a_bp, k_q, psi_n, W = 81.75, 1, .1, .804, 2\n\n    # Inverse inductance functions for the d- and q-axis\n    G_d = a_d0 + a_dd*np.abs(psi_s.real)**S + (\n        a_dq/(V + 2)*np.abs(psi_s.real)**U*np.abs(psi_s.imag)**(V + 2))\n    G_q = a_q0 + a_qq*np.abs(psi_s.imag)**T + (\n        a_dq/(U + 2)*np.abs(psi_s.real)**(U + 2)*np.abs(psi_s.imag)**V)\n\n    # Bridge flux\n    psi_b = psi_s.real - psi_n\n    # State of the bridge saturation depends also on the q-axis flux\n    psi_b_sat = np.sqrt(psi_b**2 + k_q*psi_s.imag**2)\n    # Inverse inductance function for the bridge saturation\n    G_b = a_b*psi_b_sat**W/(1 + a_bp*psi_b_sat**W)\n\n    # Stator current\n    return G_d*psi_s.real + G_b*psi_b + 1j*(G_q + k_q*G_b)*psi_s.imag"
       ]
     },
     {

docs/_downloads/353df5bc5a013b3c4d1aa12f9df12b1a/plot_gfm_rfpsc_13kva.py

@@ -1,9 +1,9 @@
 """
 12.5-kVA converter, RFPSC
 =========================
-    
-This example simulates reference-feedforward power-synchronization control 
-(RFPSC) of a converter connected to a weak grid. 
+
+This example simulates reference-feedforward power-synchronization control
+(RFPSC) of a converter connected to a weak grid.
 
 """
 

docs/_downloads/4a62c5ef3a1f5977ce95c858e910b2dd/plot_vector_ctrl_pmsm_2kw_diode.py

@@ -2,8 +2,8 @@
 2.2-kW PMSM, diode bridge
 =========================
 
-This example simulates sensorless current-vector control of a 2.2-kW PMSM 
-drive, equipped with a diode bridge rectifier. 
+This example simulates sensorless current-vector control of a 2.2-kW PMSM
+drive, equipped with a diode bridge rectifier.
 
 """
 # %%

docs/_downloads/4d070dbeed56e687e8fd0f06f72a1494/plot_gfm_obs_13kva.py

@@ -1,10 +1,10 @@
 """
 12.5-kVA converter, disturbance observer
 ========================================
-    
+
 This example simulates a converter using disturbance-observer-based control in
-grid-forming mode. The converter output voltage and the active power are 
-directly controlled, and grid synchronization is provided by the disturbance 
+grid-forming mode. The converter output voltage and the active power are
+directly controlled, and grid synchronization is provided by the disturbance
 observer. A transparent current controller is included for current limitation.
 
 """

docs/_downloads/4dcbc515fc33934d5f69b19fa8d11c75/plot_obs_vhz_ctrl_pmsm_2kw_two_mass.ipynb

@@ -4,7 +4,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# 2.2-kW PMSM, 2-mass mechanics\n\nThis example simulates observer-based V/Hz control of a 2.2-kW PMSM drive. The\nmechanical subsystem is modeled as a two-mass system. The resonance frequency\nof the mechanics is around 85 Hz. The mechanical parameters correspond to \n[#Saa2015]_, except that the torsional damping is set to a smaller value in \nthis example.\n"
+        "\n# 2.2-kW PMSM, 2-mass mechanics\n\nThis example simulates observer-based V/Hz control of a 2.2-kW PMSM drive. The\nmechanical subsystem is modeled as a two-mass system. The resonance frequency\nof the mechanics is around 85 Hz. The mechanical parameters correspond to\n[#Saa2015]_, except that the torsional damping is set to a smaller value in\nthis example.\n"
       ]
     },
     {

docs/_downloads/62bef1d87fe9b90cb5ff5b8020cddff0/plot_vector_ctrl_syrm_7kw.py

@@ -2,7 +2,7 @@
 6.7-kW SyRM
 ===========
 
-This example simulates sensorless current-vector control of a 6.7-kW SyRM 
+This example simulates sensorless current-vector control of a 6.7-kW SyRM
 drive.
 
 """

docs/_downloads/762c2b856ecc40f03ec81b867a35152d/plot_gfm_obs_13kva.ipynb

@@ -4,7 +4,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# 12.5-kVA converter, disturbance observer\n    \nThis example simulates a converter using disturbance-observer-based control in\ngrid-forming mode. The converter output voltage and the active power are \ndirectly controlled, and grid synchronization is provided by the disturbance \nobserver. A transparent current controller is included for current limitation.\n"
+        "\n# 12.5-kVA converter, disturbance observer\n\nThis example simulates a converter using disturbance-observer-based control in\ngrid-forming mode. The converter output voltage and the active power are\ndirectly controlled, and grid synchronization is provided by the disturbance\nobserver. A transparent current controller is included for current limitation.\n"
       ]
     },
     {

docs/_downloads/824ec437b909dd213e049bde99b9d868/plot_vhz_ctrl_6step_im_2kw.py

@@ -2,10 +2,10 @@
 2.2-kW induction motor, 6-step mode
 ===================================
 
-This example simulates V/Hz control of a 2.2-kW induction motor drive. The 
-six-step overmodulation is enabled, which increases the fundamental voltage as 
-well as the harmonics. Since the PWM is not synchronized with the stator 
-frequency, the harmonic content also depends on the ratio between the stator 
+This example simulates V/Hz control of a 2.2-kW induction motor drive. The
+six-step overmodulation is enabled, which increases the fundamental voltage as
+well as the harmonics. Since the PWM is not synchronized with the stator
+frequency, the harmonic content also depends on the ratio between the stator
 frequency and the sampling frequency.
 
 """

docs/_downloads/838cd6750a1112dc674c6007b2ca3fba/plot_gfl_dc_bus_10kva.ipynb

@@ -4,7 +4,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# 10-kVA converter, DC-bus voltage\n    \nThis example simulates a grid-following-controlled converter connected to a\nstrong grid and regulating the DC-bus voltage. The control system includes a \nDC-bus voltage controller, a phase-locked loop (PLL) to synchronize with the \ngrid, a current reference generator, and a PI-type current controller.\n"
+        "\n# 10-kVA converter, DC-bus voltage\n\nThis example simulates a grid-following-controlled converter connected to a\nstrong grid and regulating the DC-bus voltage. The control system includes a\nDC-bus voltage controller, a phase-locked loop (PLL) to synchronize with the\ngrid, a current reference generator, and a PI-type current controller.\n"
       ]
     },
     {

docs/_downloads/86f5cab72ae826f03997079d1a212b48/plot_vhz_ctrl_im_2kw.py

@@ -3,7 +3,7 @@
 ====================================
 
 A diode bridge, stiff three-phase grid, and a DC link is modeled. The default
-parameters in this example yield open-loop V/Hz control. 
+parameters in this example yield open-loop V/Hz control.
 
 """
 # %%

docs/_downloads/8809e2989a334febfa4f25b94b42ea4e/plot_vector_ctrl_im_2kw.py

@@ -2,9 +2,9 @@
 2.2-kW induction motor, saturated
 =================================
 
-This example simulates sensorless current-vector control of a 2.2-kW induction 
-motor drive. The magnetic saturation of the machine is also included in the 
-system model, while the control system assumes constant parameters. 
+This example simulates sensorless current-vector control of a 2.2-kW induction
+motor drive. The magnetic saturation of the machine is also included in the
+system model, while the control system assumes constant parameters.
 
 """
 # %%