Spatio-Temporal Synchrophasor Data Characterization for Mitigating False Data Injection in Smart Grids

As electric power grids' dependence on wide area monitoring systems (WAMS) is expected to increase significantly in the near future, the cyber security concerns of WAMS must be carefully addressed. False data injection attack (FDIA) is a typical cyber-physical attack of WAMS in modern smart grids. This paper presents a data mining-based approach to identify FDIA on frequency data of WAMS by revealing the spatio-temporal signatures of synchrophasor measurements. Specifically, recurrence quantification analysis (RQA) is utilized to extract temporal signatures of frequency measurements while the spatial signatures are derived by using statistical method. Three FDIA scenarios, i.e., “Source ID Mix”, time mirroring and time dilation attacks are simulated. Experimental results by using synchrophasor measurements archived in FNET /GridEye demonstrate the practicability of the proposed methodology for mitigating FDIA on frequency measurements of power systems.