/Optimal-Power-Flow-via-Neural-Networks

The projects aims to solve the optimal power flow (OPF) problem using feed-forward artificial neural networks (ANNs). The use of ANNs is to accelerate the time needed to solve the problem.

Primary LanguageJupyter Notebook

Optimal Power Flow via Neural Networks

Overview

This repository presents methodologies for solving Optimal Power Flow (OPF) problems using neural networks, split into two segments: Alternating Current (AC) and Direct Current (DC) OPF.

Structure

  • AC OPF: Contains notebooks for the AC OPF model.
    • acopf.ipynb: Notebook for AC OPF implementation and results.
  • DC OPF: Contains the code and documentation for the DC OPF model.
    • Code:
      • Neural Network Model.ipynb: Implements the neural network model.
      • Projection Scheme.ipynb: Implements the projection scheme utilized.
      • MATLAB scripts (generateData.m, generateSheet.m, etc.) for data handling and processing.
    • Documentation:
      • report.pdf: Detailed project report.
      • slides.pdf: Presentation slides summarizing the project.

Getting Started

Clone the repository and navigate to the respective notebooks or scripts for AC or DC OPF. Ensure you have the necessary libraries installed (such as TensorFlow for Python notebooks and MATLAB for scripts).

Contributing

Contributions to improve the models or extend the methodologies are welcome. Please fork the repository and submit pull requests with your enhancements.

License

This project is open-sourced under the MIT License.

Contact

Feel free to raise issues or submit pull requests if you have suggestions or need clarifications on the project.