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A CGAN-based adversarial attack method for data-driven state estimation
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AI
DOI：10.1016/j.ijepes.2025.110878.png)
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Abstract
En 中文
• Security threats for power system state estimation problem are analyzed, especially cyber-attacks oriented to data-driven state estimation methods. • Adversarial attack methods for power system state estimation, as well as injection of adversarial samples, are analyzed. Several useful conclusions for practical applications have been drawn. • A CGAN-based adversarial attack method for data-driven state estimation is proposed and optimized to reveal security threats in power systems. Comparison with existing work and other methods demonstrates the superiority and effectiveness of proposed method.
Keywords:
Adversarial attacks
Conditional generative adversarial networks
Data-driven algorithms
Generative adversarial networks
State estimation
Journal
I
IF:
5
Papers:
1.1W
Citations:
3.1W
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