Update documentation

Author: actions-user

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/18fa65c6cad00c3b4897e42fe572224a/grid_examples_jupyter.zip

@@ -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/1901236edeea2d2b57c3aeb0455fdb6c/plot_vhz_ctrl_im_2kw.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"
       ]
     },
     {