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.
This repository includes the synthetic data used for case studies as well as the scripts developed to generate the data.
This repository contains:
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case_study.py: Main script to build a DSS2 model and try case studies on it
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fun_dss.py: Script containing the class definition of the DSS2 model and most of the helper functions
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problem_dss.py: Script defining the problem's loss function to train the model on and some problem's related parameters
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loadsampling.py: Contains helper functions to perform sampling on the load profiles to generate randon load scenarios
Some pre-trained models are available in the saved_models folder and can be load in the case_study.py file, using keras library
Data to train your own model is available in the datasets folder. It is not needed if using a pre-trained model
Data generation:
- data_gen.py: Script to set the scenarios and networks and to generate the datasets
- pp_to_dss_data.py: Contains the helper function to create a DSS2 instance from pandapower
- npy_to_tfrecords.py: Script to get a .tfrecords format for the DSS datasets, which is the data format used in TF2 during training
Necessary packages: Tensorflow 2.x, Pandas, PandaPower, NumPy
This work is licensed under a
