# Dynamic Fault Characteristics Evaluation in Power Grid

arXiv: https://arxiv.org/abs/2311.16522 · HTML: https://mlsanity.com/paper/2311.16522/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 12 references that are in the map.
- First posted on arXiv 28 Nov 2023.

## Builds on (most cited first, 2 of 2)

- [Modeling Relational Data with Graph Convolutional Networks](https://mlsanity.com/paper/1703.06103/) (2017)
- [Heterogeneous Graph Transformer](https://mlsanity.com/paper/2003.01332/) (2020)

## Built on by (most cited first, 1 of 1)

- [A-LAMP: Agentic LLM-Based Framework for Automated MDP Modeling and Policy Generation](https://mlsanity.com/paper/2512.11270/) (2025)

## Abstract (arXiv)

To enhance the intelligence degree in operation and maintenance, a novel method for fault detection in power grids is proposed. The proposed GNN-based approach first identifies fault nodes through a specialized feature extraction method coupled with a knowledge graph. By incorporating temporal data, the method leverages the status of nodes from preceding and subsequent time periods to help current fault detection. To validate the effectiveness of the node features, a correlation analysis of the output features from each node was conducted. The results from experiments show that this method can accurately locate fault nodes in simulation scenarios with a remarkable accuracy. Additionally, the graph neural network based feature modeling allows for a qualitative examination of how faults spread across nodes, which provides valuable insights for analyzing fault nodes.