  Most traditional false data injection attack (FDIA) detection approaches rely
on static system parameters or a single known snapshot of dynamic ones.
However, such a setting significantly weakens the practicality of these
approaches when facing the fact that the system parameters are dynamic and
cannot be accurately known during operation due to the presence of
uncertainties in practical smart grids. In this paper, we propose an FDIA
detection mechanism from the perspective of transfer learning. Specifically,
the known initial/approximate system is treated as a source domain, which
provides abundant simulated normal and attack data. The real world's unknown
running system is taken as a target domain where sufficient real normal data
are collected for tracking the latest system states online. The designed
transfer strategy that aims at making full use of data in hand is divided into
two optimization stages. In the first stage, a deep neural network (DNN) is
built by simultaneously optimizing several well-designed terms with both
simulated data and real data, and then it is fine-tuned via real data in the
second stage. Several case studies on the IEEE 14-bus power system verify the
effectiveness of the proposed mechanism.
