PPGN:Physics-Preserved-Graph-Networks

============================================================================== This software is to locate faults in distribution systems with limited observations and labels through PPGN. PPGN performs better than the baselines when labeled data are insufficient and the distribution of data vary randomly. Code accompanying the paper ["PPGN: Physics-Preserved Graph Networks for Fault Location with Limited Observation and Labels"]

Prerequisites

The proposed method is implemented through Jupyter Notebook. The required packages include:

• Jupyter Notebook
• Python 3
• Python packages: Numpy, torch, time, os, scipy, matplotlib

Getting started

1. You can train the proposed model with "training_123nodes.ipynb" and the test the well-trained model through "Testing_123nodes.ipynb" for the IEEE 123-node test case.
2. data: Check the "readme.md" file to find the training and testing datasets in various situations. Others are the mediate information used in the codes.
3. trained: this folder has the pre-trained models.