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False Data Injection Cyber-Attacks Detection for Multiple DC Microgrid Clusters

delete2022-03-01
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OA
AI
S
Sen Tan *
P
Peilin Xie
J
Josep M. Guerrero
J
Juan C. Vásquez
DOI:10.1016/j.apenergy.2021.118425delete
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摘要

摘要

En 中文
DC microgrids are considered as the next generation of power systems because of the possibility of connecting various renewable energy sources to different types of loads based on distributed networks. However, due to the strong reliance on communication networks, DC microgrids are vulnerable to intentional cyber-attacks. Therefore, in this paper, a robust cyber-attack detection scheme is proposed for DC microgrid systems. Utilizing the parity-based method, a multi-objective optimization problem is formulated to achieve robust detection against electrical parameter perturbations and unknown disturbances. An analytical solution is then provided using the singular value decomposition approach. With the disturbance decoupling scheme, the presented detection strategy can monitor the system with only local knowledge of the DC microgrid. The proposed method is easy to design and with less computation complexity. The performances of the provided scheme are validated by simulation tests and experimental results.
Keyword:
Cyber-attacks
DC microgrids
Robust detection
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期刊

Applied Energy 封面图
Applied Energy
IF:
11
论文数:
2.6W
被引数:
17.8W

机构

A
aalborg university
学者数:
1.6W
论文数: 1.7W
被引数: 22
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引用论文

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Detecting Stealthy False Data Injection Using Machine Learning in Smart Grid
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Coordinated Data-Injection Attack and Detection in the Smart Grid
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errCui, Shuguang; Han, Zhu; Kar, Soummya; Kim, Tung T.; Poor, H. Vincent; Tajer, Ali
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