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Constraint-Driven Deep Learning for Probabilistic SCOPF considering k-faults

Code related to the submission of

B.N. Giraud, A. Rajaei, J.L. Cremer. "Constraint-Driven Deep Learning for N-k Security Constrained Optimal Power Flow". Accepted to IEEE Power System Computing Conference 2024.

License

This work is licensed under a License: MIT

Code structure

This repository contains the following parts:

  • 'learn SCOPF create GLODF.ipynb' is the code used to generate the LODF matrices.
  • 'learn SCOPF correction reduced code.ipynb' is the code used to test the 39-bus system.
  • The requirements.txt file contains the required packages.
  • The folder 'Data' contains the training and testing data used.
  • The folder 'DelftBlue' contains the codes used to test the 118-bus system.
  • The folder 'LODFS' contains the LODFs used for the 39-bus system.
  • The folder 'Security Assessment' contains the files for the second case study.
  • The folder 'Support' contains the .py files which are imported in the main Jupyter Notebooks.
  • The folder 'Test Systems' contains the excels with the case study data.
  • The folder 'Trained Models' contains the weights of the trained models which can be used for inference.
  • The 'Figures.ipynb' files contains many different figures tried/used throughout the thesis.

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  • Jupyter Notebook 92.5%
  • Python 7.5%