"""Simulations of Synchronous Machines""""

Overview:

This repository contains a series of Jupyter Notebooks focused on the simulation and analysis of synchronous machines. Each notebook explores different aspects of synchronous machine modeling, including inductance calculations, magnetic fields, and comparative analyses of various synchronous machine models. Tasks Included

*Task 3: Calculation of Inductance and Rotating Magnetic Field Description: This notebook focuses on calculating the inductance of synchronous machines and analyzing the rotating magnetic field. File: Task 3: calculation of inductance and rotating magnetic field (1).ipynb

*Task 5: Simulation of a Synchronous Machine Description: This notebook presents a simulation of a synchronous machine, exploring its dynamic behavior under various conditions. File: Task 5 Simulation of a synchronous machine.ipynb

*Task 6: Simulation of a Synchronous Machine in Python (Colab) Description: This notebook demonstrates the simulation of a synchronous machine using Python in Google Colab, allowing for easy sharing and collaboration. File: Task 6: Simulation of a synchronous machine in Python (Colab) (2).ipynb

*Task 7: Simulation of the Classical Model of a Synchronous Machine Description: This notebook simulates the classical model of a synchronous machine, emphasizing its fundamental principles and operational characteristics. File: Task 7 Simulation of the classical model of a synchronous machine (1).ipynb

*ask_8: Comparative Analysis of the Second Order Mode of the Synchronous Generator Description: This notebook conducts a comparative analysis of the second-order mode of the synchronous generator, providing insights into its dynamic performance. File: ask_8_Comparative analysis_of_second_order_mode_of_synchronous_generator.ipynb

Requirements:

To run the notebooks, ensure that you have the following Python packages installed:

Required Libraries:

NumPy: Used for numerical computations and handling arrays efficiently.

import numpy as np

SciPy: Provides functionality for scientific and technical computing, including integration and optimization.

import scipy

Matplotlib: A plotting library for creating static, animated, and interactive visualizations in Python.

import matplotlib.pyplot as plt

Plotly: An interactive graphing library that allows for dynamic visualizations and dashboards.

import plotly.graph_objects as go

How to Run

Clone this repository to your local machine or open the notebooks in Google Colab. Install the required packages if you are running locally. Open each Jupyter Notebook and run the cells sequentially to execute the simulations and analyses.

License:

This project is licensed under the MIT License. See the LICENSE file for details.