Replication Data for: Voltage-Based Faulty Section Identification in Rural Networks: Addressing IBR Integration and SWER Circuit Challenges

This repository contains the simulation files, converted data, final datasets, and Jupyter Notebooks necessary to fully replicate the findings of our research paper. Repository Structure The dataset and scripts are organized into five main stages representing the complete data pipeline: 1_Simulations_ATPDraw_ATP/: Contains the base simulation models (IEEE34NODE_DG.acp/.atp and IEEE34NODE_DG_SWER.acp). 2_Data_Generation_Code/: Contains the Jupyter Notebooks (three_phase_faults_IEEE34_DG.ipynb and single_phase_faults_IEEE34_DG_SWER.ipynb) used to dynamically inject fault parameters and automate the execution of the ATP engine in batch mode. 3_Converted_Data_MAT/: Contains the 2,800 `.mat` files generated from the raw .pl4 files. (Note: Due to file size limitations, this folder may be provided in multi-part .rar or .zip volumes). 4_Machine_Learning_Dataset/: Contains the signal processing script (1_feature_extraction_wavelet.ipynb) used for Clarke Transform and DWT, alongside the final tabulated and extracted dataset (dataset_all_buses_W1Level.csv). 5_Machine_Learning_Code/: Contains the scripts for model evaluation, including the baseline benchmark (2_Model_Benchmarking_and_Tuning.ipynb) and the final SMOTE-enhanced architecture (3_Proposed_kNN_Classifier.ipynb). Acknowledgments The authors acknowledge the Federal Institute of Piauí (IFPI) for the academic leave granted to Francisco Carlos Moreira Abreu, providing the necessary support for this work.