## 1 Introduction  
In recent years, global warming has increased the frequency of tropical cyclones, including typhoons and extreme storm surges, in China's coastal regions. Extreme weather conditions during tropical cyclones can disrupt offshore wind power output and trigger transmission line failures, threatening the safety and reliability of the power grid. Unlike other extreme weather events, tropical cyclones follow distinct spatiotemporal paths. As a tropical cyclone passes, the impact on grid equipment evolves over time, creating complex spatiotemporal patterns of grid anomalies that complicate risk assessment. Therefore, a quantitative risk assessment method is urgently needed that accounts for the spatiotemporal evolution of grid anomalies, which forms the basis for disaster emergency planning and strengthens resilience. The primary cause of power grid operational risk is large-scale or significant disturbances. As distributed energy resources are increasingly integrated into modern power grids, operational risks have shifted from traditional overloads due to single equipment failures to multidimensional anomalies caused by complex, large-scale distributed energy disturbances. Random fluctuations in distributed flexible resources like electric vehicles [1], photovoltaics [2, 3], and wind power [4, 5] can, via the amplification effect of large-scale disturbances, push the grid's operation beyond safe stability limits. Besides the clustering effect of large-scale distributed resources on operational indicators, major disturbances can also introduce operational risks to the grid [6]. Extreme weather events, given their frequency and destructiveness, have garnered widespread attention in power grid risk assessment in recent years [7; 8; 9; 10]. Risk assessment of power grids under extreme weather conditions generally involves two key steps: (1) scenario set construction; and (2) risk index calculation. The first step integrates extreme weather forecasts, analyzes the abnormal characteristics of power grid equipment, and generates hypothetical scenarios describing various equipment states. The second step involves calculating risk indices such as power flow deviation, voltage deviation, and load loss under these scenarios to quantify and assess grid risk. Extreme weather factors can cause physical damage through mechanical actions, leading to equipment failures. Extreme weather factors can also cause abnormal output variations in weather-sensitive renewable energy, such as ramping in wind power. Consequently, researchers have extensively studied equipment-level mechanisms of abnormal impacts and their quantitative characterization. In studying transmission line anomalies, [11] proposed a method for assessing risk levels by constructing a hierarchical analysis model linking meteorological factors such as lightning, icing, and strong winds to risk levels. In [12], the authors analyzed how thermal stress from lightning contributes to line aging and breakage. In studying renewable energy anomalies, [13] analyzed atmospheric circulation patterns that lead to ramping of wind power, demonstrating that thermally driven land-sea wind interactions may be the primary cause. In the context of tropical cyclones, [14] quantifies the dynamic impact of extreme wind speeds on wind turbines through the analysis of offshore wind turbine results under extreme load conditions. In addition, [15] establishes a model for calculating the instantaneous failure rate of transmission lines and towers based on a tropical cyclone wind field model. After constructing such a multi-factor interaction mechanism model, subsequent fault propagation can be simulated through a cascading model. Modeling cascading failures in power systems is crucial for assessing grid vulnerability and developing prevention and control strategies. The traditional OPA(ORNL-PSERC-Alaska) model simulates the evolution of cascading failures based on quasi-steady-state assumptions.