An approach to characterizing the power system security region by integrating distributionally robust optimization and Transformer-based deep learning

Yuekai CHEN; Zhejing BAO; Miao YU

doi:10.1631/ENG.ITEE.2026.0024

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An approach to characterizing the power system security region by integrating distributionally robust optimization and Transformer-based deep learning

Regular Papers | Updated：2026-05-27

- An approach to characterizing the power system security region by integrating distributionally robust optimization and Transformer-based deep learning
  Enhanced Publication
- 融合分布鲁棒优化与Transformer深度学习的电力系统安全域表征方法
- ENGINEERING Information Technology & Electronic Engineering Vol. 27, Issue 5, Pages: 1-13(2026)
- Affiliations：
  
  College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
- Author bio：
  
  ✉Zhejing BAO, zjbao@zju.edu.cn
- Funds：
- DOI：10.1631/ENG.ITEE.2026.0024
  CLC： TM71;TP18
- Received：21 January 2026，
  
  Revised：2026-04-16，
  
  Online First：18 May 2026，
  
  Published：2026-05
- Accepted：
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Yuekai CHEN, Zhejing BAO, Miao YU. An approach to characterizing the power system security region by integrating distributionally robust optimization and Transformer-based deep learning[J]. ENGINEERING Information Technology & Electronic Engineering, 2026, 27(5): 1-13.
DOI：

Yuekai CHEN, Zhejing BAO, Miao YU. An approach to characterizing the power system security region by integrating distributionally robust optimization and Transformer-based deep learning[J]. ENGINEERING Information Technology & Electronic Engineering, 2026, 27(5): 1-13. DOI： 10.1631/ENG.ITEE.2026.0024.

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An approach to characterizing the power system security region by integrating distributionally robust optimization and Transformer-based deep learning

DOI：10.1631/ENG.ITEE.2026.0024