Spatial-Temporal Recurrent Graph Neural Networks for Fault Diagnostics in Power Distribution Systems

Nguyen, Bang L. H.; Vu, Tuyen V.; Nguyen, Thai-Thanh; Panwar, Mayank; Hovsapian, Rob

doi:10.1109/access.2023.3273292

Spatial-Temporal Recurrent Graph Neural Networks for Fault Diagnostics in Power Distribution Systems

Journal Article · Fri May 05 00:00:00 EDT 2023 · IEEE Access

DOI:https://doi.org/10.1109/access.2023.3273292· OSTI ID:1985632

^[1]; Vu, Tuyen V. ^[2]; Nguyen, Thai-Thanh ^[3]; Panwar, Mayank ^[1]; Hovsapian, Rob ^[1]

National Renewable Energy Laboratory (NREL), Golden, CO (United States)
Clarkson Univ., Potsdam, NY (United States)
New York Power Authority, White Plains, NY (United States)

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.

View Accepted Manuscript (DOE)

Research Organization:: National Renewable Energy Laboratory (NREL), Golden, CO (United States)

Sponsoring Organization:: National Science Foundation (NSF); US Office of Naval Research (ONR); USDOE

Grant/Contract Number:: AC36-08GO28308

OSTI ID:: 1985632

Report Number(s):: NREL/JA-5C00-86593; MainId:87366; UUID:09023a2d-3758-4af4-b17e-f4cab21e8a02; MainAdminID:69789

Journal Information:: IEEE Access, Journal Name: IEEE Access Vol. 11; ISSN 2169-3536

Publisher:: IEEECopyright Statement

Country of Publication:: United States

Language:: English

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Spatial-Temporal Recurrent Graph Neural Networks for Fault Diagnostics in Power Distribution Systems

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