Abstract
Identification and zonal classification of the faults is a decisive factor in the relay’s decision to trip or not. Different types of
fault like three-phase, line-to-line-to-ground and single-line-to-ground can occur at various locations in the feeder. These
faults are seen as the variation in the instantaneous values of three-phase voltages and currents, i.e., waveforms, that are
measured at the relay location. The objective of this work is to develop a machine learning model that can
identify a fault and classify it to various protection zones based on measured waveforms. In this work, a data-driven relay
based on Convolutional Neural Network (CNN) is proposed for fault identification in distribution feeders with high
penetration solar PV. The proposed CNN model takes local current and voltage waveforms as input and classify it into
fault, no-fault or a capacitor switching. Further, the CNN also attempts to identify fault zones based on the images of
waveforms. The overall testing accuracy of the trained model exceeds 95%.
- Developers:
-
Ramesh, Meghana [1] ; McDermott, Thomas [1]
- Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
- Release Date:
- 2024-04-30
- Project Type:
- Open Source, Publicly Available Repository
- Software Type:
- Scientific
- Licenses:
-
BSD 2-clause "Simplified" License
- Sponsoring Org.:
-
USDOEPrimary Award/Contract Number:AC05-76RL01830
- Code ID:
- 126780
- Site Accession Number:
- Battelle IPID 32719-E
- Research Org.:
- Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
- Country of Origin:
- United States
Citation Formats
Ramesh, Meghana, and McDermott, Thomas.
Convolution Neural Network for Fault Identification in Distribution Feeder with High Penetration Solar PV.
Computer Software.
https://github.com/pnnl/DPVCNN.
USDOE.
30 Apr. 2024.
Web.
doi:10.11578/dc.20240430.4.
Ramesh, Meghana, & McDermott, Thomas.
(2024, April 30).
Convolution Neural Network for Fault Identification in Distribution Feeder with High Penetration Solar PV.
[Computer software].
https://github.com/pnnl/DPVCNN.
https://doi.org/10.11578/dc.20240430.4.
Ramesh, Meghana, and McDermott, Thomas.
"Convolution Neural Network for Fault Identification in Distribution Feeder with High Penetration Solar PV." Computer software.
April 30, 2024.
https://github.com/pnnl/DPVCNN.
https://doi.org/10.11578/dc.20240430.4.
@misc{
doecode_126780,
title = {Convolution Neural Network for Fault Identification in Distribution Feeder with High Penetration Solar PV},
author = {Ramesh, Meghana and McDermott, Thomas},
abstractNote = {Identification and zonal classification of the faults is a decisive factor in the relay’s decision to trip or not. Different types of
fault like three-phase, line-to-line-to-ground and single-line-to-ground can occur at various locations in the feeder. These
faults are seen as the variation in the instantaneous values of three-phase voltages and currents, i.e., waveforms, that are
measured at the relay location. The objective of this work is to develop a machine learning model that can
identify a fault and classify it to various protection zones based on measured waveforms. In this work, a data-driven relay
based on Convolutional Neural Network (CNN) is proposed for fault identification in distribution feeders with high
penetration solar PV. The proposed CNN model takes local current and voltage waveforms as input and classify it into
fault, no-fault or a capacitor switching. Further, the CNN also attempts to identify fault zones based on the images of
waveforms. The overall testing accuracy of the trained model exceeds 95%.},
doi = {10.11578/dc.20240430.4},
url = {https://doi.org/10.11578/dc.20240430.4},
howpublished = {[Computer Software] \url{https://doi.org/10.11578/dc.20240430.4}},
year = {2024},
month = {apr}
}