Detection of False Data Injection Attacks in Power Grid Based on Coding Schemes

To solve the problem of the detection of false data injection attacks in power grid, we propose coding schemes to counter such attacks based on the DC linear and AC nonlinear models of power grid system, respectively. For the DC linear model, we design a data-driven encoding and decoding scheme, and provide the optimal data assignment on the encoding and decoding matrices according to the conditions whether there are communication constraints. For the AC nonlinear model, we construct a stretching matrix to make the attacked redundant measurements produce large detection residues. Then, the corresponding encoding and decoding schemes are put forward, and the conditions for the selection of the decoding matrix are also proposed. Finally, we utilize the real world load data in IEEE-14 bus system to verify the effectiveness of the proposed methods for DC linear and AC nonlinear models, respectively.