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DSS²: Deep Statistical Solver for Distribution System State Estimation

This repository contains code for the paper:

B. Habib, E. Isufi, W. v. Breda, A. Jongepier and J. L. Cremer, "Deep Statistical Solver for Distribution System State Estimation," in IEEE Transactions on Power Systems, doi: 10.1109/TPWRS.2023.3290358.

Old Code

Code has been updated for better flow and use of PyTorch. Previous code used in the paper is available in the specified folder.

Data

This folder includes the synthetic data used for case studies.

Models and case studies

This repository contains:

  • data.py: Helper functions:

    • to get features for nodes and edges in specified grid
    • to calculate power flow values based on given voltages and grid features
    • to describe the loss function for DSS2 in gsp_wls_edge
    • to retrieve data for training and testing from pickle files

  • dss2_run.py: Main script to create a GNN model, train it with WLS and testing

  • networks.py: Script defining different GNN models based on PyTorch Geometric library and PowerFlowNet repository defining a GNN model for Power Flow: https://github.com/StavrosOrf/PoweFlowNet

  • loadsampling.py: Contains helper functions to perform sampling on the load profiles to generate randon load scenarios

  • toy_network.py: PandaPower script to create scenarios on different grids and gather a synthetic database

Necessary packages: Pytorch, Torch Geometric, Pandas, PandaPower, NumPy

License

This work is licensed under a License: MIT