Attack Power System State Estimation by Implicitly Learning the Underlying Models

Author:

Costilla-Enriquez Napoleon1ORCID,Weng Yang1ORCID

Affiliation:

1. School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ, USA

Funder

U.S. Department of Energy’s Office of Energy Efficiency and Renewable Energy (EERE) through the solar Energy Technologies Office

Department of Energy

National Science Foundation

Fondo de Sustentabilidad Energetica CONACYT-SENER, Mexico

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Computer Science

Cited by 13 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Physical model learning based false data injection attack on power system state estimation;Sustainable Energy, Grids and Networks;2024-12

2. DipDNN: Preserving Inverse Consistency and Approximation Efficiency for Invertible Learning;Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining;2024-08-24

3. AC False Data Injection Attack Based on Robust Tensor Principle Component Analysis;IEEE Transactions on Industrial Informatics;2024-08

4. Adversarial Artificial Intelligence in Blind False Data Injection in Smart Grid AC State Estimation;IEEE Transactions on Industrial Informatics;2024-06

5. A Review and Analysis of Attack and Countermeasure Approaches for Enhancing Smart Grid Cybersecurity;2024 28th International Electrical Power Distribution Conference (EPDC);2024-04-23

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