  This paper introduces an advanced stochastic hybrid system modeling framework
for modern power systems (MPS) to identify, classify, and evaluate hidden
contingencies, which cannot be detected by normal observation sensors. The
stochastic hybrid system (SHS) model is designed to capture the dynamics of the
internal states of individual nodes, considering their structural properties,
and coupling variables under various local and network-level contingencies.
Hidden contingencies are identified using a probing approach that measures
changes in the eigenvalues of the SHS model and detects deviations from normal
operation. Next, contingencies are categorized into three distinct groups
according to their impact on MPS: physical contingencies, control network
contingencies, and sensing and measurement network contingencies. This
classification enables a proactive evaluation of contingencies. The
practicality and efficacy of the proposed methodology are validated through
simulation experiments on the electrical network of two real-world systems.
These simulations underscore the approach's capacity to enhance the resilience
of power systems against a spectrum of hidden contingencies.
