Revert "Dummy change for testing of JupyterHub Execution"

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SLEW_Case2_Electromechanical_Phenomena.ipynb

 %% Cell type:markdown id:cfe34583 tags:
 
 <style>
 /* Set the font for the entire notebook to Times New Roman */
 * {
     font-family: "Times New Roman", Times, serif;
 }
 </style>
 
 <div>
     <img src="figures/slew_logo.png" style="float: right;height: 15em;">
 </div>
 
 # SLEW Case 2 - Electromechanical Dynamics: Small-signal Stability
 
 ## Theoretical background
 ### The Linearized Swing Equation
 The swing equation is the fundamental equation that determines the rotor dynamics of a synchronous machine.
 $$M\frac{d^{2}\delta}{dt^{2}} = P_{m} - P_{e}(\delta) - D\frac{d\delta}{dt}$$
 
 For small signal stability analysis, the swing equation can be linearized in the vicinity of the operating point $\delta = \delta_{0}$:
 $$M\frac{d^{2}\Delta\delta}{dt^{2}} + D\frac{d\Delta\delta}{dt}  + K_{e^{'}}\Delta\delta =  0 $$
 where $K_{e^{'}}$ is the transient synchronizing power coefficient at $\delta = \delta_{0}$:
 $$K_{e^{'}} = \frac{\partial P_{e^{'}}}{\partial \delta^{'}}\mid_{\delta = \delta_{0}}$$
 
 The characteristic equation and the roots of the linearized swing equation are as follows:
 $$\lambda^{2} + \frac{D}{M}\lambda + \frac{K_{e^{'}}}{M} =  0 $$
 
 $$\lambda_{1,2} = -\frac{D}{2M} \pm \sqrt{\left(\frac{D}{2M}\right)^{2} - \frac{K_{e^{'}}}{M}} $$
 
 %% Cell type:markdown id:eb59e0e7 tags:
 
 ***
 ## Case description
 
 %% Cell type:markdown id:58c9e2a2 tags:
 
-This is a test change (05.06.2025)
 A $200$ MVA round-rotor generator is connected to an infinite bus system of nominal frequency of $50 Hz$ through a step-up transformer with reactance of $0.13 p.u.$ and a transmission line with reactance of  $0.17 p.u.$ . The generator’s transient reactance is $x'_{d} = 0.23 p.u.$ and the inertia constant is $H = 4 s$. The synchronous generator mechanical power is set to $0.6 p.u.$ and the steady-state emf $E = 1.1 p.u.$
 
 %% Cell type:markdown id:a6940104 tags:
 
 ***
 ## Case tasks
 
 %% Cell type:markdown id:f3323e4f tags:
 
 #### Task 1
  If at the given operating point the system eigen-values are as follows:
   $$\lambda_{1,2} = -0.06254 \pm 8.8318j $$
 
 &nbsp;&nbsp;&nbsp;&nbsp;a) Determine whether the system response following a small disturbance is overdamped, critically damped or underdamped.
 
 &nbsp;&nbsp;&nbsp;&nbsp;b) Verify your findings by calculating the damping coefficient *D* (not to be confused with the damping ratio ζ ) of the synchronous generator and then applying the damping value *"D"* in SLEW and generating the corresponding power and angle plots.
 
 #### Task 2
  If the system response following a small disturbance is critically damped.
 
 &nbsp;&nbsp;&nbsp;&nbsp;a) Determine the synchronous generator damping coefficient (not to be confused with the damping ratio ζ ).
 
 &nbsp;&nbsp;&nbsp;&nbsp;b) Apply the calculated damping value in SLEW and generate the corresponding power and angle plots.
 
 &nbsp;&nbsp;&nbsp;&nbsp;c) Increase the damping coefficient (not to be confused with the damping ratio ζ ) value further and observe the impact of such increase.
 
 %% Cell type:markdown id:1fc3c700 tags:
 
 **You can process the results from SLEW (as required in 1 and 2) using the prepared notebook cells below.**
 
 %% Cell type:markdown id:9da09483 tags:
 
 ***
 ## Case solutions
 
 %% Cell type:markdown id:d1a0ab6c tags:
 
 #### First Setup your Python for post-processing
 
 %% Cell type:markdown id:8432a231 tags:
 
 Import relevant Python packages by executing the following cell:
 
 %% Cell type:code id:5bb649a9 tags:
 
 ``` python
 from IPython.display import display
 from villas.web.result import Result
 from pprint import pprint
 import tempfile
 import os
 from villas.dataprocessing.readtools import *
 from villas.dataprocessing.timeseries import *
 import matplotlib.pyplot as plt
 import scipy.io as sio
 outputs = sio.loadmat('outputs/case2_output.mat', simplify_cells=True)
 #matplotlib widget
 %matplotlib widget
 ```
 
 %% Cell type:markdown id:290ba8ba tags:
 
 ### Task 1
 
 %% Cell type:markdown id:3564b4be tags:
 
  If at the given operating point the system eigen-values are as follows:
   $$\lambda_{1,2} = -0.06254 \pm 8.8318j $$
 
 %% Cell type:markdown id:29d31393 tags:
 
 #### a)
 Determine whether the system response following a small disturbance is overdamped, critically damped or underdamped.
 
 
 %% Cell type:code id:e1620ea2 tags:
 
 ``` python
 import scipy.io
 import ipywidgets as widgets
 from IPython.display import display, clear_output
 
 # Load the solution from the .mat file
 mat_data = scipy.io.loadmat('outputs/case2_output.mat')
 correct_answer = mat_data['correct_answer'][0]
 
 # Create a radio button widget for the damping question
 radio_btn = widgets.RadioButtons(
     options=['Overdamped', 'Critically Damped', 'Underdamped'],
     description='Select the correct answer:',
     disabled=False
 )
 
 # Create an output widget to display the result
 output = widgets.Output()
 
 # Function to check the student's answer, display the result, and disable further attempts
 def check_answer(b):
     with output:
         clear_output()  # Clear previous output
         selected = radio_btn.value
         if selected == correct_answer:
             print("Your answer is correct! The Eigenvalues are complex conjugate pair with an imaginary part \ineq 0, therefore the system is underdamped.")
         else:
             print("Incorrect. Try again!")
 
         # Disable the radio buttons and the submit button after the first attempt
         #radio_btn.disabled = True
         #submit_btn.disabled = True
 
 # Create a button to submit the answer
 submit_btn = widgets.Button(description="Submit Answer")
 
 # Link the submit button to the check_answer function
 submit_btn.on_click(check_answer)
 
 # Display the radio button, submit button, and output widget
 display(radio_btn, submit_btn, output)
 ```
 
 %% Cell type:markdown id:b08ba784 tags:
 
 #### b)
 Verify your findings by calculating the damping coefficient (not to be confused with the damping ratio ζ ) of the synchronous generator and then applying the damping value in SLEW and generating the corresponding power and angle plots.
 To check your results, enter your solution for *"D"* rounded to 4 digits after the decimal point:
 
 %% Cell type:code id:4d8c545c tags:
 
 ``` python
 D=input('D in Subtask 1.b:')
 print('Your result is', 'correct!' if round(float(D),4)==round(outputs['D_T1b'],4) else 'incorrect. You should double-check your calculations.')
 ```
 
 %% Cell type:markdown id:5592eeac tags:
 
 Download simulation results from SLEW
 
 %% Cell type:markdown id:192ea4fa tags:
 
 Enter SLEW under [https://slew.k8s.eonerc.rwth-aachen.de](https://slew.k8s.eonerc.rwth-aachen.de).
 Adjust the parameter *"D"* and run a corresponding simulation.
 Then, enter your code snippet for result download in the following cell:
 
 %% Cell type:code id:3a922965 tags:
 
 ``` python
 # Access result using token
 r = Result(1, 'xyz', endpoint='https://slew.k8s.eonerc.rwth-aachen.de')
 ```
 
 %% Cell type:markdown id:7d391ebd tags:
 
 #### Plot the results
 
 %% Cell type:code id:609cbf87 tags:
 
 ``` python
 results_file_name='SP_SynGenTrStab_SMIB_Fault_SlewCase2_JsonSyngenParams_SP.csv'
 
 # Get file by name
 f = r.get_file_by_name(results_file_name)
 
 # Create temp dir
 tmpdir = tempfile.mkdtemp()
 results_file_path = os.path.join(tmpdir,results_file_name)
 
 # Load files as bytes
 f = f.download(dest=results_file_path)
 
 # Get time series object from csv
 ts_res1 = read_timeseries_csv(results_file_path, print_status=True)
 
 # Plot the currents
 plt.figure(figsize=(8,12))
 name_list = ['Rotor Angle','Output Power']
 plt.subplot(2,1,1)
 plt.ylabel("Angle (degree)")
 plt.xlabel("time (s)")
 plt.plot(ts_res1['delta_r_gen'].time, ts_res1['delta_r_gen'].values, label='Rotor Angle task 1.b', color='C0')
 plt.xlim([1, 30])
 plt.legend(loc='upper right')
 
 plt.subplot(2,1,2)
 plt.ylabel("Power (MW)")
 plt.xlabel("time (s)")
 plt.plot(ts_res1['P_elec'].time, ts_res1['P_elec'].values/1e6, label='Output Power task 1.b', color='C1')
 plt.xlim([1, 30])
 plt.legend(loc='upper right')
 plt.show()
 ```
 
 %% Cell type:markdown id:8ef9b8b2 tags:
 
 ### Task 2
 
 %% Cell type:markdown id:00d2e239 tags:
 
 If the system response following a small disturbance is critically damped.
 
 %% Cell type:markdown id:b1a6d35b tags:
 
 #### a)
 Determine the synchronous generator damping coefficient (not to be confused with the damping ratio ζ ).
 
 To check your results, enter your solution for *"D"* rounded to 4 digits behind the comma:
 
 %% Cell type:code id:e70ae849 tags:
 
 ``` python
 D=input('D in Subtask 2.a:')
 print('Your result is', 'correct!' if round(float(D),4)==round(outputs['D_T2a'],4) else 'incorrect. You should double-check your calculations.')
 ```
 
 %% Cell type:markdown id:6ab41b23 tags:
 
 #### b)
 Apply the calculated damping value in SLEW and generate the corresponding power and angle plots.
 
 Download simulation results from SLEW
 
 %% Cell type:markdown id:5f52f99d tags:
 
 Enter SLEW under [https://slew.k8s.eonerc.rwth-aachen.de](https://slew.k8s.eonerc.rwth-aachen.de).
 Adjust the parameter *"D"* and run a corresponding simulation.
 Then, enter your code snippet for result download in the following cell:
 
 %% Cell type:code id:921256f6 tags:
 
 ``` python
 # Access result using token
 r = Result(1, 'xyz', endpoint='https://slew.k8s.eonerc.rwth-aachen.de')
 ```
 
 %% Cell type:markdown id:4d20385b tags:
 
 #### Plot the results
 
 %% Cell type:code id:ba064129 tags:
 
 ``` python
 results_file_name='SP_SynGenTrStab_SMIB_Fault_SlewCase2_JsonSyngenParams_SP.csv'
 
 # Get file by name
 f = r.get_file_by_name(results_file_name)
 
 # Create temp dir
 tmpdir = tempfile.mkdtemp()
 results_file_path = os.path.join(tmpdir,results_file_name)
 
 # Load files as bytes
 f = f.download(dest=results_file_path)
 
 # Get time series object from csv
 ts_res2 = read_timeseries_csv(results_file_path, print_status=False)
 
 # Plot the currents
 plt.figure(figsize=(8,12))
 name_list = ['Rotor Angle','Output Power']
 plt.subplot(2,1,1)
 plt.ylabel("Angle (degree)")
 plt.xlabel("time (s)")
 plt.plot(ts_res2['delta_r_gen'].time, ts_res2['delta_r_gen'].values,  label='Rotor  Angle task 2.b', color='C0')
 plt.plot(ts_res1['delta_r_gen'].time, ts_res1['delta_r_gen'].values, label='Rotor  Angle task 1.b', color='black', linestyle=':')
 plt.xlim([1, 30])
 plt.legend(loc='upper right')
 
 plt.subplot(2,1,2)
 plt.ylabel("Power (MW)")
 plt.xlabel("time (s)")
 plt.plot(ts_res2['P_elec'].time, ts_res2['P_elec'].values/1e6,  label='Output Power task 2.b', color='C1')
 plt.plot(ts_res1['P_elec'].time, ts_res1['P_elec'].values/1e6, label='Output Power task 1.b', color='black', linestyle=':')
 plt.xlim([1, 30])
 plt.legend(loc='upper right')
 plt.show()
 ```
 
 %% Cell type:markdown id:18f20b09 tags:
 
 #### c)
 Increase the damping coefficient (not to be confused with the damping ratio ζ ) value (for example by a factor of 2) further and observe the impact of such increase.
 
 Download simulation results from SLEW
 
 %% Cell type:markdown id:4f967ccb tags:
 
 Enter SLEW under [https://slew.k8s.eonerc.rwth-aachen.de](https://slew.k8s.eonerc.rwth-aachen.de).
 Adjust the parameter *"D"* and run a corresponding simulation.
 Then, enter your code snippet for result download in the following cell:
 
 %% Cell type:code id:79b309eb tags:
 
 ``` python
 # Access result using token
 r = Result(1, 'xyz', endpoint='hhttps://slew.k8s.eonerc.rwth-aachen.de')
 ```
 
 %% Cell type:markdown id:a4dbb836 tags:
 
 #### Plot the results
 
 %% Cell type:code id:ba53ba36 tags:
 
 ``` python
 results_file_name='SP_SynGenTrStab_SMIB_Fault_SlewCase2_JsonSyngenParams_SP.csv'
 
 # Get file by name
 f = r.get_file_by_name(results_file_name)
 
 # Create temp dir
 tmpdir = tempfile.mkdtemp()
 results_file_path = os.path.join(tmpdir,results_file_name)
 
 # Load files as bytes
 f = f.download(dest=results_file_path)
 
 # Get time series object from csv
 ts_res3 = read_timeseries_csv(results_file_path, print_status=False)
 
 # Plot the currents
 plt.figure(figsize=(8,12))
 name_list = ['Rotor Angle','Output Power']
 plt.subplot(2,1,1)
 plt.ylabel("Angle (degree)")
 plt.xlabel("time (s)")
 plt.plot(ts_res3['delta_r_gen'].time, ts_res3['delta_r_gen'].values, label='Rotor  Angle task 2.c', color='C0')
 plt.plot(ts_res2['delta_r_gen'].time, ts_res2['delta_r_gen'].values,  label='Rotor  Angle task 2.b', color='black', linestyle=':')
 plt.xlim([1, 30])
 plt.legend(loc='upper right')
 
 plt.subplot(2,1,2)
 plt.ylabel("Power (MW)")
 plt.xlabel("time (s)")
 plt.plot(ts_res3['P_elec'].time, ts_res3['P_elec'].values/1e6, label='Output Power task 2.c', color='C1')
 plt.plot(ts_res2['P_elec'].time, ts_res2['P_elec'].values/1e6,  label='Output Power task 2.b', color='black', linestyle=':')
 plt.xlim([1, 30])
 plt.legend(loc='upper right')
 plt.show()
 ```
 
 %% Cell type:code id:d74fe01a tags:
 
 ``` python
 ```