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Most traditional false data injection attack (FDIA) detection approaches relyon a key assumption, i.e., the power system can be accurately modeled. However,the transmission line parameters are dynamic and cannot be accurately knownduring operation and thus the involved modeling errors should not be neglected.In this paper, an illustrative case has revealed that modeling errors intransmission lines significantly weaken the detection effectiveness ofconventional FDIA approaches. To tackle this issue, we propose an FDIAdetection mechanism from the perspective of transfer learning. Specifically,the simulated power system is treated as a source domain, which providesabundant simulated normal and attack data. The real world's running systemwhose transmission line parameters are unknown is taken as a target domainwhere sufficient real normal data are collected for tracking the latest systemstates online. The designed transfer strategy that aims at making full use ofdata in hand is divided into two optimization stages. In the first stage, adeep neural network (DNN) is built by simultaneously optimizing severalwell-designed objective terms with both simulated data and real data, and thenit is fine-tuned via real data in the second stage. Several case studies on theIEEE 14-bus and 118-bus systems verify the effectiveness of the proposedmechanism.

Detecting False Data Injection Attacks in Smart Grids with Modeling Errors: A Deep Transfer Learning Based Approach