junbozhao / deep-statistical-solver-for-distribution-system-state-estimation

Implementation of Deep Statistical Solver for Distribution System State Estimation

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.

Data

This repository includes the synthetic data used for case studies as well as the scripts developed to generate the data.

Models and case studies

This repository contains:

• case_study.py: Main script to build a DSS2 model and try case studies on it

• fun_dss.py: Script containing the class definition of the DSS2 model and most of the helper functions

• problem_dss.py: Script defining the problem's loss function to train the model on and some problem's related parameters

• 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

License

This work is licensed under a