Modifications in the documentation, PLL refactoring

.vscode/extensions.json

@@ -0,0 +1,8 @@
+{
+    "recommendations": [
+        "ms-python.python"
+        "ms-python.pylint",
+        "eeyore.yapf",
+        "streetsidesoftware.code-spell-checker",
+    ]
+}

docs/source/control/control_system.rst

@@ -1,15 +1,15 @@
-Control Systems
-===============
+Common
+======
 
 Main Control Loop
 -----------------
 
-By default, the discrete-time controllers in *motulator* run the following scheme in a main control loop:
+By default, discrete-time control systems in *motulator* run the following scheme in their main control loop:
 
-   1. Get the feedback signals. This step may contain first getting the measurements and then optionally computing the observer outputs.
-   2. Compute the reference signals (controller outputs) based on the feedback signals.
-   3. Update the control system states for the next sampling instant.
-   4. Save the feedback signals and the reference signals.
+   1. Get the feedback signals for the controllers. This step may contain first getting the measurements and then optionally computing the observer outputs. These measured and estimated signals are collected to the SimpleNamespace object named `fbk`. 
+   2. Get the reference signals and compute the controller outputs based on the feedback signals `fbk`. These reference signals are collected to the SimpleNamespace object named `ref`. 
+   3. Update the states of the control system for the next sampling instant.
+   4. Save the feedback signals `fbk` and the reference signals `ref` so they can be accessed after the simulation.
    5. Return the sampling period `T_s` and the duty ratios `d_abc` for the carrier comparison.
 
-The main control loop is implemented in the base class for control systems in :class:`motulator.common.control.ControlSystem`.
+A template of this main control loop is available in the base class for control systems in :class:`motulator.common.control.ControlSystem`. Using this template is not necessary, but it may simplify the implementation of new control systems.

docs/source/control/grid/synchronization.rst

@@ -1,65 +1,91 @@
-Synchronization Methods
-=======================
+Disturbance-Observer-Based PLL
+==============================
 
-Phase-Locked Loop
+A phase-locked loop (PLL) is commonly used in grid-following converters to synchronize the converter output with the grid [#Kau1997]_. Here, we represent the PLL using the disturbance observer structure [#Fra1997]_, which may be simpler to extend than the classical PLL. Furthermore, this structure allows to see links to synchronization methods used in grid-forming converters, see [#Nur2024]_. 
+
+Disturbance Model
 -----------------
 
-The :doc:`/grid_examples/grid_following/index` examples use a Phase-Locked Loop (PLL) to synchronize with the grid. The block diagram of the PLL is shown in the figure below.
+Consider the positive-sequence voltage at the point of common coupling (PCC) in general coordinates, rotating at the angular speed :math:`\omega_\mathrm{c}`. The dynamics of the PCC voltage :math:`\boldsymbol{u}_\mathrm{g}` can be modeled using a disturbance model as
+
+.. math::
+    \frac{\mathrm{d}\boldsymbol{u}_\mathrm{g}}{\mathrm{d}t} = \mathrm{j}(\omega_\mathrm{g} - \omega_\mathrm{c})\boldsymbol{u}_\mathrm{g} 
+    :label: disturbance
+
+where :math:`\omega_\mathrm{g}` is the grid angular frequency. If needed, this disturbance model could be extended, e.g., with a negative-sequence component, allowing to design the PLL for unbalanced grids. 
+
+PLL in General Coordinates
+--------------------------
+
+Based on :eq:`disturbance`, the disturbance observer containing the regular PLL functionality can be formulated as  
+
+.. math::
+    \frac{\mathrm{d}\hat{\boldsymbol{u}}_\mathrm{g}}{\mathrm{d}t} = \mathrm{j}(\hat{\omega}_\mathrm{g} - \omega_\mathrm{c})\hat{\boldsymbol{u}}_\mathrm{g} + \alpha_\mathrm{g} (\boldsymbol{u}_\mathrm{g} - \hat{\boldsymbol{u}}_\mathrm{g} ) 
+    :label: pll
 
-.. figure:: ../figs/pll.svg
-   :width: 100%
-   :align: center
-   :alt: Block diagram of the phase-locked loop for grid synchronization
-   :target: .
+where :math:`\hat{\boldsymbol{u}}_\mathrm{g} = \hat{u}_\mathrm{g} \mathrm{e}^{\mathrm{j}\hat{\vartheta}_\mathrm{g}}` is the estimated PCC voltage, :math:`\hat{\omega}_\mathrm{g}` is the grid angular frequency estimate (either constant corresponding to the nominal value or dynamic from grid-frequency tracking), and :math:`\alpha_\mathrm{g}` is the bandwidth. If needed, the disturbance observer can be extended with the frequency tracking as
 
-   Block diagram of the phase-locked loop.
+.. math::
+    \frac{\mathrm{d}\hat{\omega}_\mathrm{g}}{\mathrm{d}t} = k_\omega\mathrm{Im}\left\{ \frac{\boldsymbol{u}_\mathrm{g} - \hat{\boldsymbol{u}}_\mathrm{g}}{\hat{\boldsymbol{u}}_\mathrm{g}} \right\} 
+    :label: frequency_tracking
 
-The PLL drives the signal :math:`\hat{u}_\mathrm{gq}` to zero, leading to :math:`\hat{\vartheta}_\mathrm{g}=\vartheta_\mathrm{g}` and :math:`\hat{u}_\mathrm{gd}=u_\mathrm{gd}` in ideal conditions. The grid voltage-vector :math:`\boldsymbol{u}_\mathrm{g}^\mathrm{s}=u_\mathrm{g} \mathrm{e}^{\mathrm{j} \vartheta_\mathrm{g}}` is measured. The angle :math:`\vartheta_\mathrm{g}` can be noisy and it is not directly used in the control. Instead, the PLL tracks :math:`\vartheta_{g}` and filters its noise and harmonics above the PLL bandwidth.
+where :math:`k_\omega` is the frequency-tracking gain. Notice that :eq:`pll` and :eq:`frequency_tracking` are both driven by the estimation error :math:`\boldsymbol{u}_\mathrm{g} - \hat{\boldsymbol{u}}_\mathrm{g}` of the PCC voltage.
 
-The gain selection:
+PLL in Estimated PCC Voltage Coordinates
+----------------------------------------
 
-.. math:: 
-    k_\mathrm{p} = \frac{2 \zeta \omega_\mathrm{0,PLL}}{u_\mathrm{gN}} \qquad
-    k_\mathrm{i} = \frac{\omega_\mathrm{0,PLL}^2}{u_\mathrm{gN}}
+The disturbance observer :eq:`pll` can be equivalently expressed in estimated PCC voltage coordinates, where :math:`\hat{\boldsymbol{u}}_\mathrm{g} = \hat{u}_\mathrm{g}` and :math:`\omega_\mathrm{c} = \mathrm{d} \hat{\vartheta}_\mathrm{g}/ \mathrm{d} t`, resulting in
 
-where :math:`\zeta` is the damping factor, :math:`\omega_\mathrm{0,PLL}` is the natural frequency of the PLL, and :math:`u_\mathrm{gN}` is the nominal grid voltage amplitude.
+.. math::
+    \frac{\mathrm{d}\hat{u}_\mathrm{g}}{\mathrm{d}t} &= \alpha_\mathrm{g} \left(\mathrm{Re}\{\boldsymbol{u}_\mathrm{g}\} - \hat{u}_\mathrm{g} \right) \\
+    \frac{\mathrm{d} \hat{\vartheta}_\mathrm{g}}{\mathrm{d} t} &= \hat{\omega}_\mathrm{g} + \frac{\alpha_\mathrm{g}}{\hat{u}_\mathrm{g}}\mathrm{Im}\{ \boldsymbol{u}_\mathrm{g} \} = \omega_\mathrm{c}
+    :label: pll_polar
 
-More details on the control methods used can be found in [#Kau1997]_.
+It can be seen that the first equation in :eq:`pll_polar` is low-pass filtering of the measured PCC voltage magnitude and the second equation is the conventional angle-tracking PLL. In these coordinates, the frequency tracking :eq:`frequency_tracking` reduces to
 
-This controller is implemented in the class :class:`motulator.grid.control.PLL`.
+.. math::
+    \frac{\mathrm{d} \hat{\omega}_\mathrm{g}}{\mathrm{d} t} = \frac{k_\omega}{\hat{u}_\mathrm{g} } \mathrm{Im}\{ \boldsymbol{u}_\mathrm{g} \}
+    :label: frequency_tracking_polar
 
-Power Synchronization
----------------------
+It can be noticed that the disturbance observer with the frequency tracking equals the conventional frequency-adaptive PLL [#Kau1997]_, with the additional feature of low-pass filtering the PCC voltage magnitude. The low-pass filtered PCC voltage can be used as a feedforward term in current control [#Har2009]_. 
 
-In :doc:`/grid_examples/grid_forming/plot_gfm_rfpsc_13kva` a power synchronization loop (PSL) is used as a means of synchronizing with the grid. The dynamics of a synchronous machine are emulated, as the converter output active power is tied to the angle of the converter output voltage. This allows for synchronization of a converter with the grid without the use of a PLL. More details on the control method used can be found in [#Har2020]_.
+Linearized Closed-Loop System
+-----------------------------
 
-The PSL is implemented as
+The estimation-error dynamics are analyzed by means of linearization. Using the PCC voltage as an example, the small-signal deviation about the operating point is denoted by :math:`\Delta \boldsymbol{u}_\mathrm{g} = \boldsymbol{u}_\mathrm{g} - \boldsymbol{u}_\mathrm{g0}`, where :math:`\boldsymbol{u}_\mathrm{g0}` is the operating-point quantity. From :eq:`disturbance`--:eq:`frequency_tracking`, the linearized model for the estimation-error dynamics is obtained as
 
 .. math::
-    \frac{\mathrm{d}\vartheta_\mathrm{c}}{\mathrm{d}t} = \omega_\mathrm{g} + k_\mathrm{p} (p_\mathrm{g,ref} - p_\mathrm{g})
-    :label: psl
+    \frac{\mathrm{d}\Delta \tilde{\boldsymbol{u}}_\mathrm{g}}{\mathrm{d}t} &= -\alpha_\mathrm{g}\Delta \tilde{\boldsymbol{u}}_\mathrm{g} + \mathrm{j}\boldsymbol{u}_\mathrm{g0} (\Delta \omega_\mathrm{g} - \Delta \hat{\omega}_\mathrm{g}) \\
+    \frac{\mathrm{d}\Delta \hat{\omega}_\mathrm{g}}{\mathrm{d}t} &= k_\omega\mathrm{Im}\left\{ \frac{\Delta \tilde{\boldsymbol{u}}_\mathrm{g}}{\boldsymbol{u}_\mathrm{g0}} \right\} 
+    :label: linearized_model
+
+where :math:`\Delta \tilde{\boldsymbol{u}}_\mathrm{g} = \Delta\boldsymbol{u}_\mathrm{g} - \Delta \hat{\boldsymbol{u}}_\mathrm{g}` is the estimation error. 
 
-where :math:`\vartheta_\mathrm{c}` is the converter output voltage angle, :math:`\omega_\mathrm{g}` the nominal grid angular frequency and :math:`k_\mathrm{p}` the active power control gain. Furthermore, :math:`p_\mathrm{g,ref}` and :math:`p_\mathrm{g}` are the reference and realized value for the converter active power output, respectively. The active power output is calculated from the measured converter current and the realized converter output voltage obtained from the PWM.
+First, assume that the grid frequency :math:`\omega_\mathrm{g}` is constant and the frequency tracking is disabled. From :eq:`linearized_model`, the closed-loop transfer function from the PCC voltage to its estimate becomes
 
-Disturbance Observer
---------------------
+.. math::
+    \frac{\Delta\hat{\boldsymbol{u}}_\mathrm{g}(s)}{\Delta\boldsymbol{u}_\mathrm{g}(s)} = \frac{\alpha_\mathrm{g}}{s + \alpha_\mathrm{g}} 
+    :label: closed_loop_pll
+
+It can be realized that both the angle and magnitude of the PCC voltage estimate converge with the bandwidth :math:`\alpha_\mathrm{g}`. 
 
-In :doc:`/grid_examples/grid_forming/plot_gfm_obs_13kva`, a disturbance observer-based control method is used, where the disturbance observer provides grid synchronization [#Nur2024]_. The observer is implemented in coordinates rotating at angular frequency :math:`\omega_\mathrm{c}` as
+Next, the frequency-tracking dynamics are also considered. From :eq:`linearized_model`, the closed-loop transfer function from the grid angular frequency to its estimate becomes 
 
 .. math::
-    \frac{\mathrm{d}\hat{\boldsymbol{v}}_\mathrm{c}'}{\mathrm{d}t} &= (\boldsymbol{k}_\mathrm{o}
-    + \mathrm{j}\hat{\omega}_\mathrm{g})\boldsymbol{e}_\mathrm{c} + \mathrm{j}(\hat{\omega}_\mathrm{g}
-    - \omega_\mathrm{c})\hat{\boldsymbol{v}}_\mathrm{c}'\\
-    \hat{\boldsymbol{v}}_\mathrm{c} &= \hat{\boldsymbol{v}}_\mathrm{c}'
-    - \boldsymbol{k}_\mathrm{o}\hat{L}\boldsymbol{i}_\mathrm{c}
-    :label: gfm_obs_eqs
+    \frac{\Delta\hat{\omega}_\mathrm{g}(s)}{\Delta\omega_\mathrm{g}(s)} 
+    = \frac{k_\omega}{s^2 + \alpha_\mathrm{g}s + k_\omega}
+    :label: closed_loop_pll_frequency_tracking
 
-where :math:`\hat{\boldsymbol{v}}_\mathrm{c}` is the quasi-static converter output voltage estimate, :math:`\boldsymbol{k}_\mathrm{o}` the observer gain, :math:`\hat{\omega}_\mathrm{g}` the grid angular frequency estimate, :math:`\boldsymbol{e}_\mathrm{c}` the feedback correction term, :math:`\hat{L}` the total inductance estimate, and :math:`\boldsymbol{i}_\mathrm{c}` the measured converter current.
+Choosing :math:`k_\omega = \alpha_\mathrm{pll}^2` and :math:`\alpha_\mathrm{g} = 2\alpha_\mathrm{pll}` yields the double pole at :math:`s = -\alpha_\mathrm{pll}`, where :math:`\alpha_\mathrm{pll}` is the frequency-tracking bandwidth.
+
+This PLL is implemented in the class :class:`motulator.grid.control.PLL`. The :doc:`/grid_examples/grid_following/index` examples use the PLL to synchronize with the grid. 
 
 .. rubric:: References
 
-.. [#Kau1997] Kaura and Blasko, "Operation of a phase locked loop system under distorted utility conditions," in IEEE Trans. Ind. Appl., vol. 33, no. 1, pp. 58-63, Jan.-Feb. 1997, https://doi.org/10.1109/28.567077
+.. [#Kau1997] Kaura and Blasko, "Operation of a phase locked loop system under distorted utility conditions," in IEEE Trans. Ind. Appl., 1997, https://doi.org/10.1109/28.567077
+
+.. [#Fra1997] Franklin, Powell, Workman, "Digital Control of Dynamic Systems," 3rd ed., Menlo Park, CA: Addison-Wesley, 1997
 
-.. [#Har2020] Harnefors, Rahman, Hinkkanen, Routimo, "Reference-Feedforward Power-Synchronization Control," IEEE Trans. Power Electron., Sep. 2020, https://doi.org/10.1109/TPEL.2020.2970991
+.. [#Nur2024] Nurminen, Mourouvin, Hinkkanen, Kukkola, "Multifunctional grid-forming converter control based on a disturbance observer, "IEEE Trans. Power Electron., 2024, https://doi.org/10.1109/TPEL.2024.3433503
 
-.. [#Nur2024] Nurminen, Mourouvin, Hinkkanen, Kukkola, "Multifunctional grid-forming converter control based on a disturbance observer, "IEEE Trans. Power Electron., 2024, https://doi.org/10.1109/TPEL.2024.3433503
+.. [#Har2009] Harnefors, Bongiorno, "Current controller design for passivity of the input admittance," in Proc. EPE, 2009

docs/source/index.rst

@@ -1,7 +1,7 @@
 *motulator:* Motor Drive and Grid Converter Simulator in Python
 ===============================================================
 
-This open-source software includes simulation models and controllers for electric machine drives and grid-connected converters. The machine models include an induction machine, a synchronous reluctance machine, and a permanent-magnet synchronous machine. Various models are provided for grid converter-related electrical subsystems such as an inductive-capacitive-inductive (LCL) filter connected to an inductive-resistive grid.
+This open-source software includes simulation models and controllers for electric machine drives and grid converter systems. The machine models include an induction machine, a synchronous reluctance machine, and a permanent-magnet synchronous machine. Various subsystem models are provided for modeling grid converter systems, such as an LCL filter connected to an inductive-resistive grid.
 
 The system models are simulated in the continuous-time domain while the control methods run in discrete time. The default solver is the explicit Runge-Kutta method of order 5(4) from `scipy.integrate.solve_ivp`_. A number of control methods are provided as examples. The example methods aim to be simple yet feasible. 
 
@@ -31,14 +31,13 @@ The system models are simulated in the continuous-time domain while the control
    :name: models
    :maxdepth: 1
 
-   model/system
-   model/converters
+   model/common
    model/drive/index
    model/grid/index
 
 .. toctree::
    :titlesonly:
-   :caption: Design Notes on Control Methods
+   :caption: Control Methods
    :name: controllers
    :maxdepth: 1
 
@@ -48,7 +47,7 @@ The system models are simulated in the continuous-time domain while the control
 
 .. toctree::
    :titlesonly:
-   :caption: Class references
+   :caption: Class Reference
    :name: api
    :maxdepth: 1
 

docs/source/installation.rst

@@ -40,10 +40,12 @@ Several powerful open-source IDEs are available for Python. The following instru
 4) Create a virtual environment in the workspace using the instructions provided here: https://code.visualstudio.com/docs/python/environments.
 5) Enable installation of optional dependencies from ``pyproject.toml`` (selecting at least ``dev`` is recommended). Alternatively, for installing the project with its core dependencies, you may run the command ``pip install .`` in the VS Code terminal after the virtual environment is created and activated (or to include optional dependencies, run ``pip install .[dev,doc]``).
 
-After completing the above steps, the virtual environment can be found in the ``.venv`` directory at the root of the repository. Now you should be able to run all the examples as well as to modify the existing code. When you start VS Code next time, it will automatically detect the virtual environment and use it.
+After completing the above steps, the virtual environment can be found in the ``.venv`` directory at the root of the repository. Now you should be able to run all the examples as well as to modify the existing code. When you start VS Code next time, it will automatically detect the virtual environment and use it. If you installed the ``dev`` dependencies, you can also use the interactive IPython console (click on the *Play* button dropdown menu in VS Code). 
 
-If you installed the ``dev`` dependencies, you can also use the interactive IPython console (click on the *Play* button dropdown menu in VS Code). If you aim to work with the documentation, install also the ``doc`` dependencies. For previewing the documentation in VS Code, you can install the Esbonio extension: https://docs.esbon.io/en/latest/lsp/editors/vscode.html.
+If you use Windows, you may need to change the default terminal from the PowerShell to the Command Prompt (press Ctrl+Shift+P for the command palette and search for *Terminal: Select Default Profile*). We hope that these instructions allow you to create a virtual environment and start working on the project. Similar steps can be followed for other IDEs.
 
-If you use Windows, you may need to change the default terminal from the PowerShell to the Command Prompt (press Ctrl+Shift+P for the command palette and search for *Terminal: Select Default Profile*). 
+Notes for Developers
+^^^^^^^^^^^^^^^^^^^^
+We recommend to install the ``dev`` dependencies. In this project, we use the YAPF Python code formatter (https://github.com/google/yapf), configured based on the PEP8 coding conventions (https://peps.python.org/pep-0008/). To enable YAPF in VS Code, you need to install the YAPF extension (https://marketplace.visualstudio.com/items?itemName=eeyore.yapf). 
 
-We hope that these instructions allow you to create a virtual environment and start working on the project. Similar steps can be followed for other IDEs.
+If you aim to work with the documentation, install also the ``doc`` dependencies. For previewing the documentation in VS Code, you can install the Esbonio extension (https://marketplace.visualstudio.com/items?itemName=swyddfa.esbonio). Alternatively, you can build the documentation locally using the command ``make html`` in the ``docs`` directory. The documentation will be built in the ``docs/build`` directory.