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\begin{document}
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\title{A Causal Graph Attention Learning for Interpretable Cascading Failure
Predictions in Power Systems with Renewable Generation via HVDC}



\author[1]{Shiqu Xiao}%
\author[1]{Yang Fu}%
\author[2]{Xiangjing Su}%
\author[1]{Shaohua Zhang}%
\author[3]{Jiajia Yang}%
\author[4]{Cuo Zhang}%
\author[5]{Zhao Yang Dong}%
\affil[1]{Shanghai University}%
\affil[2]{Shanghai University of Electric Power}%
\affil[3]{University of New South Wales}%
\affil[4]{The University of Sydney}%
\affil[5]{Nanyang Technological University}%


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  \date{April 25, 2026}


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\begin{abstract}
The emergence of high-voltage direct current integrated (HVDC) power
systems introduces significant out-of-distribution (OOD) challenges to
machine learning-based analysis of cascading failures (CFs). Existing
algorithms often suffer from poor interpretability and generalization in
handling diverse CF propagation modes. To address these issues, this
paper proposes a novel Causal Graph Attention (CGAT) model that captures
critical information from CF events to accurately predict propagation
paths and impacts. Specifically, by employing a graph attention
mechanism, the model extracts both causal and trivial features as
distinctive attention dual subgraphs, which aids in differentiating
shortcuts in causal variables and improving prediction accuracy. These
subgraphs also provide visual representations that highlight the key
elements influencing CF propagation. By incorporating interventions, the
CGAT model enhance prediction capabilities across a variety of scenarios
that include HVDC control strategies and renewable energy sources (RES).
Extensive experiments on augmented IEEE 30-bus and 2737-sop systems
demonstrate the effectiveness and superiority of the proposed CGAT
model.%
\end{abstract}%



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