期刊
KNOWLEDGE AND INFORMATION SYSTEMS
卷 52, 期 3, 页码 815-834出版社
SPRINGER LONDON LTD
DOI: 10.1007/s10115-016-1019-8
关键词
False data separation; Data recovery; Data security; Optimization; Grassmann manifold
资金
- National Natural Science Foundation of China [61502347, 61502504, 61628209]
- Nature Science Foundation of Hubei Province of China [2016CFB384, 2016CFA030]
- Fundamental Research Funds for the Central Universities [2042015kf0038]
- Research Funds of Renmin University of China [15XNLF09]
- Research Funds for Introduced Talents of Wuhan University
The smart grid is emerging as an efficient paradigm for electric power generation, transmission, and consumption, based on optimized decision making and control that leverage the measurement data of sensors and meters in the grid. False data injection is a new type of power grid attacks aiming to tamper such important data. For the security and robustness of the grid, it is critical to separate the false data injected by such attacks and recover the original measurement data. Nonetheless, the existing approaches often neglect the true changes on original measurement data that are caused by the real perturbations on grid states and hence have a risk of removing these true changes as injected false data during the data recovery. In this paper, we preserve these true changes by modeling the false data problem as a rank-bounded L-1 norm optimization and propose both offline and online algorithms to filter out the injected false data and recover original measurement data. Trace-driven simulations verify the efficacy of our solution.
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