Work

The research on cascading failure prediction in AC-DC hybrid power grids based on deep learning

Year: 2026

Type: article

Abstract: Accurate prediction of cascading faults in power grids is critical for ensuring their stability and preventing large-scale outages. This paper presents Hybrid Graph-Temporal Transformer (HGTT), for predicting cascading faults in AC/DC hybrid power grids. The HGTT model integrates Graph Attention Networks (GAT) and Temporal Transformers, effectively capturing both spatial and temporal dependencies. Key innovations include an attention mechanism that accounts for electrical distances between nodes, and a causal attention-based temporal feature extraction module. Additionally, two self-supervised tasks reduce reliance on labeled data. Experimental results show that HGTT improves prediction accuracy by over 15% and reduces labeled data requirements by 50%.

Source: Discover Computing

DOI: 10.1007/s10791-026-10130-z

Authors: Wenchao Qin, Xufei Liu, Yiran Tao

Institution: Yunnan Power Grid Co., Ltd. (China)

Language: English

Topic: Power System Optimization and Stability

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $1,520