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

arXiv: https://arxiv.org/abs/2210.15177 · HTML: https://mlsanity.com/paper/2210.15177/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Oct 2022.

## Built on by (most cited first, 7 of 7)

- [A Heterogeneous Graph-Based Multi-Task Learning for Fault Event Diagnosis in Smart Grid](https://mlsanity.com/paper/2309.09921/) (2023)
- [Graph Neural Networks for Electric and Hydraulic Data Fusion to Enhance Short-term Forecasting of Pumped-storage Hydroelectricity](https://mlsanity.com/paper/2404.03368/) (2024)
- [Assessing the Generalization of Graph Neural Networks for Fault Location Across Increasing Distributed Energy Resource Penetration Levels](https://mlsanity.com/paper/2607.29293/) (2026)
- [Generalization of Graph Neural Network Models for Distribution Grid Fault Detection](https://mlsanity.com/paper/2510.03571/) (2025)
- [How Does Research Evolve? Tracing Cross-Domain Trajectories in NLP, ML, and CV Through Claim-Grounded Typed Citations](https://mlsanity.com/paper/2606.22342/) (2026)
- [EvEMTBench: An Open Benchmark for Machine Learning in Power System Protection](https://mlsanity.com/paper/2609.28149/) (2026)
- [Reachability-Based Formal Verification of Graph Neural Networks with Node and Edge Features](https://mlsanity.com/paper/2609.30079/) (2026)

## Abstract (arXiv)

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 a 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.