  Existing power cyber-physical system (CPS) risk prediction results are
inaccurate as they fail to reflect the actual physical characteristics of the
components and the specific operational status. A new method based on dependent
Markov chain for power CPS risk area prediction is proposed in this paper. The
load and constraints of the non-uniform power CPS coupling network are first
characterized, and can be utilized as a node state judgment standard.
Considering the component node isomerism and interdependence between the
coupled networks, a power CPS risk regional prediction model based on dependent
Markov chain is then constructed. A cross-adaptive gray wolf optimization
algorithm improved by adaptive position adjustment strategy and cross-optimal
solution strategy is subsequently developed to optimize the prediction model.
Simulation results using the IEEE 39-BA 110 test system verify the
effectiveness and superiority of the proposed method.
