  Influenced by deep penetration of the new generation of information
technology, power systems have gradually evolved into highly coupled
cyber-physical systems (CPS). Among many possible power CPS network attacks, a
false data injection attacks (FDIAs) is the most serious. Taking account of the
fact that the existing knowledge-driven detection process for FDIAs has been in
a passive detection state for a long time and ignores the advantages of
data-driven active capture of features, an active and passive hybrid detection
method for power CPS FDIAs with improved adaptive Kalman filter (AKF) and
convolutional neural networks (CNN) is proposed in this paper. First, we
analyze the shortcomings of the traditional AKF algorithm in terms of filtering
divergence and calculation speed. The state estimation algorithm based on
non-negative positive-definite adaptive Kalman filter (NDAKF) is improved, and
a passive detection method of FDIAs is constructed, with similarity Euclidean
distance detection and residual detection at its core. Then, combined with the
advantages of gate recurrent unit (GRU) and CNN in terms of temporal memory and
feature-expression ability, an active detection method of FDIAs based on a
GRU-CNN hybrid neural network is proposed. Finally, the results of joint
knowledge-driven and data-driven parallel detection are used to define a mixed
fixed-calculation formula, and an active and passive hybrid detection method of
FDIAs is established, considering the characteristic constraints of the
parallel mode. A simulation system example of power CPS FDIAs verifies the
effectiveness and accuracy of the method proposed in this paper.
