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QSTAformer: A quantum-enhanced Transformer for robust short-term voltage stability assessment against adversarial attacks

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Listed:
  • Li, Yang
  • Ma, Chong
  • Li, Yuanzheng
  • Li, Sen
  • Chen, Yanbo
  • Dong, Zhaoyang

Abstract

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.

Suggested Citation

  • Li, Yang & Ma, Chong & Li, Yuanzheng & Li, Sen & Chen, Yanbo & Dong, Zhaoyang, 2026. "QSTAformer: A quantum-enhanced Transformer for robust short-term voltage stability assessment against adversarial attacks," Applied Energy, Elsevier, vol. 405(C).
    • Handle: RePEc:eee:appene:v:405:y:2026:i:c:s0306261925019269
    DOI: 10.1016/j.apenergy.2025.127196
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    References listed on IDEAS

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