# Guiding Cascading Failure Search with Interpretable Graph Convolutional Network

arXiv: https://arxiv.org/abs/2001.11553 · HTML: https://mlsanity.com/paper/2001.11553/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jan 2020.

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

- [MaxK-GNN: Extremely Fast GPU Kernel Design for Accelerating Graph Neural Networks Training](https://mlsanity.com/paper/2312.08656/) (2023)
- [Accel-GCN: High-Performance GPU Accelerator Design for Graph Convolution Networks](https://mlsanity.com/paper/2308.11825/) (2023)
- [I-GCN: A Graph Convolutional Network Accelerator with Runtime Locality Enhancement through Islandization](https://mlsanity.com/paper/2203.03606/) (2022)
- [PowerGraph: A power grid benchmark dataset for graph neural networks](https://mlsanity.com/paper/2402.02827/) (2024)
- [GCoD: Graph Convolutional Network Acceleration via Dedicated Algorithm and Accelerator Co-Design](https://mlsanity.com/paper/2112.11594/) (2021)
- [Toward Dynamic Stability Assessment of Power Grid Topologies using Graph Neural Networks](https://mlsanity.com/paper/2206.06369/) (2022)
- [Towards dynamic stability analysis of sustainable power grids using graph neural networks](https://mlsanity.com/paper/2212.11130/) (2022)

## Abstract (arXiv)

Power system cascading failures become more time variant and complex because of the increasing network interconnection and higher renewable energy penetration. High computational cost is the main obstacle for a more frequent online cascading failure search, which is essential to improve system security. In this work, we show that the complex mechanism of cascading failures can be well captured by training a graph convolutional network (GCN) offline. Subsequently, the search of cascading failures can be significantly accelerated with the aid of the trained GCN model. We link the power network topology with the structure of the GCN, yielding a smaller parameter space to learn the complex mechanism. We further enable the interpretability of the GCN model by a layer-wise relevance propagation (LRP) algorithm. The proposed method is tested on both the IEEE RTS-79 test system and China's Henan Province power system. The results show that the GCN guided method can not only accelerate the search of cascading failures, but also reveal the reasons for predicting the potential cascading failures.