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Data-Driven Protection Software to classify fault locations by protective zone in distribution systems with high PV penetration

RESOURCE

Publicly Accessible Repository
https://github.com/pnnl/oedisi_transients
https://doi.org/10.11578/dc.20240718.2

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Abstract

The software contains (a) the source codes to generate Point-on-Wave (PoW) transient data for any feeder model in Alternative Transient Program (ATP) format. Codes provide options to change different steady state settings, including the loading condition and PV capacity and transient state setting like faults type, location and initiation time (b) data post-processing source code to converted data from native format to COMTRADE, csv, HDF5 (c) Docker container to train CNN to classify fault locations by protective zone. The container takes dataset and other training parameters (sampling rate, training epochs, batch size etc) as input to train CNN. The container writes back the trained CNN model, training and testing metrics and plots to the local workstation
Developers:
Ramesh, Meghana [1]
  1. Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
Release Date:
2024-07-18
Project Type:
Open Source, Publicly Available Repository
Software Type:
Scientific
Licenses:
BSD 2-clause "Simplified" License
Sponsoring Org.:
Code ID:
136461
Site Accession Number:
Battelle IPID 32849-E
Research Org.:
Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
Country of Origin:
United States

RESOURCE

Publicly Accessible Repository
https://github.com/pnnl/oedisi_transients
https://doi.org/10.11578/dc.20240718.2

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Citation Formats

Ramesh, Meghana. Data-Driven Protection Software to classify fault locations by protective zone in distribution systems with high PV penetration. Computer Software. https://github.com/pnnl/oedisi_transients. USDOE. 18 Jul. 2024. Web. doi:10.11578/dc.20240718.2.
Ramesh, Meghana. (2024, July 18). Data-Driven Protection Software to classify fault locations by protective zone in distribution systems with high PV penetration. [Computer software]. https://github.com/pnnl/oedisi_transients. https://doi.org/10.11578/dc.20240718.2.
Ramesh, Meghana. "Data-Driven Protection Software to classify fault locations by protective zone in distribution systems with high PV penetration." Computer software. July 18, 2024. https://github.com/pnnl/oedisi_transients. https://doi.org/10.11578/dc.20240718.2.
@misc{ doecode_136461,
title = {Data-Driven Protection Software to classify fault locations by protective zone in distribution systems with high PV penetration},
author = {Ramesh, Meghana},
abstractNote = {The software contains (a) the source codes to generate Point-on-Wave (PoW) transient data for any feeder model in Alternative Transient Program (ATP) format. Codes provide options to change different steady state settings, including the loading condition and PV capacity and transient state setting like faults type, location and initiation time (b) data post-processing source code to converted data from native format to COMTRADE, csv, HDF5 (c) Docker container to train CNN to classify fault locations by protective zone. The container takes dataset and other training parameters (sampling rate, training epochs, batch size etc) as input to train CNN. The container writes back the trained CNN model, training and testing metrics and plots to the local workstation},
doi = {10.11578/dc.20240718.2},
url = {https://doi.org/10.11578/dc.20240718.2},
howpublished = {[Computer Software] \url{https://doi.org/10.11578/dc.20240718.2}},
year = {2024},
month = {jul}
}