False data injection attack and defense method based on graph neural network parameter estimation priority

Abstract

This invention discloses a spoofing data injection attack and defense method based on graph neural network parameter estimation priority, belonging to the field of power system network security technology. During power grid operation, dynamic disturbances are applied to the parameters of target branches. The topology of the main grid and distribution network is constructed as a single graph structure. A first graph neural network is used to jointly learn measurement features and predict the parameters of each branch after the disturbance. An attack vector is generated based on the set of target branches, the target measurement set, and the parameter prediction results. The attack vector is embedded into the full measurement space, and state estimation is performed in combination with the parameter prediction results to obtain the residual statistics and state estimation offset features after the attack. The post-attack measurement vector, offset features, residual statistics, and parameter prediction results are concatenated into a node input feature vector, which is input into a second graph neural network to output the attack probability of each branch, and the set of attacked branches is obtained by filtering. This invention achieves rapid estimation of branch parameters under dynamic disturbances and accurate location of attack branches.

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