用量子增强的Transformer提升电网电压稳定评估的抗攻击能力
QSTAformer: A Quantum-Enhanced Transformer for Robust Short-Term Voltage Stability Assessment against Adversarial Attacks
- 将量子电路嵌入注意力机制，构建新型量子增强模型
- 在IEEE 39节点系统上实现高精度、低复杂度与强抗干扰性
- 首次系统研究量子机器学习在电网评估中的对抗脆弱性
短期电压稳定评估（STVSA）对电力系统安全运行至关重要。尽管传统机器学习方法表现优异，但在对抗攻击下仍显脆弱。本文提出QSTAformer——一种专用于STVSA的量子增强Transformer架构，将参数化量子电路（PQC）嵌入注意力机制中，提升模型鲁棒性。设计了针对性的对抗训练策略，有效防御白盒与灰盒攻击。同时对比多种PQC结构，分析其表达能力、收敛速度与效率之间的权衡。据我们所知，这是首个系统研究基于量子机器学习的STVSA对抗脆弱性的工作。在IEEE 39节点系统上的案例研究表明，QSTAformer在保持高精度的同时，显著降低计算复杂度并增强鲁棒性，展现出在对抗环境下安全、可扩展的评估潜力。
原文摘要 · Abstract (English)
Short-term voltage stability assessment (STVSA) is critical for secure power system operation. While classical machine learning-based methods have demonstrated strong performance, they still face challenges in robustness under adversarial conditions. This paper proposes QSTAformer-a tailored quantum-enhanced Transformer architecture that embeds parameterized quantum circuits (PQCs) into attention mechanisms-for robust and efficient STVSA. A dedicated adversarial training strategy is developed to defend against both white-box and gray-box attacks. Furthermore, diverse PQC architectures are benchmarked to explore trade-offs between expressiveness, convergence, and efficiency. To the best of our knowledge, this is the first work to systematically investigate the adversarial vulnerability of quantum machine learning-based STVSA. Case studies on the IEEE 39-bus system demonstrate that QSTAformer achieves competitive accuracy, reduced complexity, and stronger robustness, underscoring its potential for secure and scalable STVSA under adversarial conditions.
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