Spatial-Temporal Recurrent Graph Neural Networks for Fault Diagnostics in Power Distribution Systems
Fault diagnostics are extremely important to decide proper actions toward fault isolation and system restoration. The growing integration of inverter-based distributed energy resources imposes strong influences on fault detection using traditional overcurrent relays. This paper utilizes emerging gra...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10119158/ |
| _version_ | 1797805957282004992 |
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| author | Bang L. H. Nguyen Tuyen V. Vu Thai-Thanh Nguyen Mayank Panwar Rob Hovsapian |
| author_facet | Bang L. H. Nguyen Tuyen V. Vu Thai-Thanh Nguyen Mayank Panwar Rob Hovsapian |
| author_sort | Bang L. H. Nguyen |
| collection | DOAJ |
| description | Fault diagnostics are extremely important to decide proper actions toward fault isolation and system restoration. The growing integration of inverter-based distributed energy resources imposes strong influences on fault detection using traditional overcurrent relays. This paper utilizes emerging graph learning techniques to build new temporal recurrent graph neural network models for fault diagnostics. The temporal recurrent graph neural network structures can extract the spatial-temporal features from data of voltage measurement units installed at the critical buses. From these features, fault event detection, fault type/phase classification, and fault location are performed. Compared with previous works, the proposed temporal recurrent graph neural networks provide a better generalization for fault diagnostics. Moreover, the proposed scheme retrieves the voltage signals instead of current signals so that there is no need to install relays at all lines of the distribution system. Therefore, the proposed scheme is generalizable and not limited by the number of relays installed. The effectiveness of the proposed method is comprehensively evaluated on the Potsdam microgrid and IEEE 123-node system in comparison with other neural network structures. |
| first_indexed | 2024-03-13T06:00:30Z |
| format | Article |
| id | doaj.art-c535c74f25c341bb8a28a20b6deb43b3 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-13T06:00:30Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-c535c74f25c341bb8a28a20b6deb43b32023-06-12T23:01:25ZengIEEEIEEE Access2169-35362023-01-0111460394605010.1109/ACCESS.2023.327329210119158Spatial-Temporal Recurrent Graph Neural Networks for Fault Diagnostics in Power Distribution SystemsBang L. H. Nguyen0https://orcid.org/0000-0002-1832-7621Tuyen V. Vu1Thai-Thanh Nguyen2Mayank Panwar3Rob Hovsapian4National Renewable Energy Laboratory, Golden, CO, USADepartment of Electrical and Computer Engineering, Clarkson University, Potsdam, NY, USANew York Power Authority, White Plains, NY, USANational Renewable Energy Laboratory, Golden, CO, USANational Renewable Energy Laboratory, Golden, CO, USAFault diagnostics are extremely important to decide proper actions toward fault isolation and system restoration. The growing integration of inverter-based distributed energy resources imposes strong influences on fault detection using traditional overcurrent relays. This paper utilizes emerging graph learning techniques to build new temporal recurrent graph neural network models for fault diagnostics. The temporal recurrent graph neural network structures can extract the spatial-temporal features from data of voltage measurement units installed at the critical buses. From these features, fault event detection, fault type/phase classification, and fault location are performed. Compared with previous works, the proposed temporal recurrent graph neural networks provide a better generalization for fault diagnostics. Moreover, the proposed scheme retrieves the voltage signals instead of current signals so that there is no need to install relays at all lines of the distribution system. Therefore, the proposed scheme is generalizable and not limited by the number of relays installed. The effectiveness of the proposed method is comprehensively evaluated on the Potsdam microgrid and IEEE 123-node system in comparison with other neural network structures.https://ieeexplore.ieee.org/document/10119158/Fault detectionfault locationmicrogrid protectiondeep neural networkgraph learning |
| spellingShingle | Bang L. H. Nguyen Tuyen V. Vu Thai-Thanh Nguyen Mayank Panwar Rob Hovsapian Spatial-Temporal Recurrent Graph Neural Networks for Fault Diagnostics in Power Distribution Systems IEEE Access Fault detection fault location microgrid protection deep neural network graph learning |
| title | Spatial-Temporal Recurrent Graph Neural Networks for Fault Diagnostics in Power Distribution Systems |
| title_full | Spatial-Temporal Recurrent Graph Neural Networks for Fault Diagnostics in Power Distribution Systems |
| title_fullStr | Spatial-Temporal Recurrent Graph Neural Networks for Fault Diagnostics in Power Distribution Systems |
| title_full_unstemmed | Spatial-Temporal Recurrent Graph Neural Networks for Fault Diagnostics in Power Distribution Systems |
| title_short | Spatial-Temporal Recurrent Graph Neural Networks for Fault Diagnostics in Power Distribution Systems |
| title_sort | spatial temporal recurrent graph neural networks for fault diagnostics in power distribution systems |
| topic | Fault detection fault location microgrid protection deep neural network graph learning |
| url | https://ieeexplore.ieee.org/document/10119158/ |
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