With the increasing integration of new energy sources

the risk assessment of cascading faults in the power grid has become increasingly crucial for its safe and stable operation. The stochastic variability of new energy sources and the characteristics of power electronics integration contribute to a heightened level of randomness and complexity in cascading faults. Traditional cascading fault models fail to effectively capture the disconnection of new energy integration units due to voltage fluctuations

and the risk assessment for large-scale power grids still grapples with the trade-off between accuracy and complexity. To address this challenge

this paper proposes a cascading fault risk assessment method based on a physical information neural network. Firstly

we establish uncertainty models for the random disconnection of new energy sources and line overloads. Simultaneously

we introduce a fault graph chain to describe the state transition characteristics of electrical quantities

topological relationships

and other physical features during cascading fault processes. Secondly

we present cascading fault risk indicators corresponding to the fault graph. Through data-driven empirical replay calculation

we integrate physical information into a neural network

aiming to approximate the nonlinear mapping relationship between fault graphs and cascading fault risk indicators

facilitating rapid risk assessment. Finally

the effectiveness of the proposed method is validated through case studies.