DSA-learn.jl

This repository provides tools to generate datasets for dynamic security assessment using our proposed method and baseline benchmarks. Below is a guide to help you navigate the repository and generate your own dataset.

For more information, read our paper 'A Dataset Generation Toolbox for Dynamic Security Assessment: On the Role of the Security Boundary'. If you use this work, please cite it as follows:

@article{giraud2025datasets,
  author = {Bastien Giraud, Lola Charles, Agnes Marjorie Nakiganda, Johanna Vorwerk and Spyros Chatzivasileiadis},
  title = {A Dataset Generation Toolbox for Dynamic Security Assessment: On the Role of the Security Boundary},
  journal = {Submitted to IREP 2025 (under review)},
  year = {2025},
  url = {(http://arxiv.org/abs/2501.09513)},
}

Setting up the Environment

To get started with this repository, you simply need to clone the repository. All the dependencies are located in the Manifest.toml and project.toml files. All the dependencies will be installed on your machine when you run the main.jl file. For an additional overview of the packages used and their versions, you can go to functions/env.jl. But now, you can get started by:

1. Clone the Repository
   Clone the repository to your local machine:

   git clone https://github.com/bastiengiraud/DSA-learn.git

2. Install R This repository also uses R and calls it using Rcall in Julia. Therefore, make sure you have R installed on your device. Moreover, you will need to install the following two packages in R: Rcpp, and volesti. You can install them as follows:

• Open a terminal in R and simply type:
  • install.packages("Rcpp")
  • install.packages("volesti")

How to Use

To start using the toolbox, you simply need to specify your dataset parameters in the init.jl file, and then you can run the main.jl file. The init.jl file contains ALL parameters you can modify. The parameters in this file contain a short description behind it, please read this short explanation carefully; they explain what the parameters are and what you can choose from.

1. Configure Parameters:
   All parameters required for dataset generation are specified in init.jl. This includes configurations for both the proposed method and the benchmark methods.

2. Run the Main Script:
   The main script is main.jl. Execute this file to generate your dataset.

3. Generate Benchmark Datasets:
   To generate datasets using one of the baseline benchmarks, execute the corresponding code in the baseline folder.

Repository Structure

Here’s an overview of where to find everything in this repository:

• Julia Packages:
  The required Julia packages and their specific versions are listed in functions/env.jl.

• Toolbox Functions:
  All functions used throughout the toolbox are located in the functions folder.

• Case and Generator Data:
  Case data and generator parameters are stored in the cases folder.

• Baseline Benchmarks:
  The benchmarks used for comparison with the proposed method are found in the baseline folder.

• Dependencies and Environments:
  Use the Manifest.toml and Project.toml files to import all necessary packages and their dependencies.