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

Bang Nguyen, Tuyen Vu, Thai-Thanh Nguyen, Mayank Panwar, Rob Hovsapian

Research output: Contribution to journalArticlepeer-review

6 Scopus Citations

Abstract

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.

Original languageAmerican English
Pages (from-to)46039-46050
Number of pages12
JournalIEEE Access
Volume11
DOIs
StatePublished - 2023

Bibliographical note

Publisher Copyright:
© 2013 IEEE.

NREL Publication Number

  • NREL/JA-5C00-86593

Keywords

  • deep neural network
  • Fault detection
  • fault location
  • graph learning
  • microgrid protection

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