Risk Assessment of Power System Cascading Failures Based on Sequential Monte Carlo Simulation and Fuzzy Clustering Analysis

Cascading failures in power systems represent a significant threat to grid reliability and can lead to widespread blackouts. Traditional deterministic methods often fall short in capturing the inherent uncertainties and dynamic progression of such events. This paper proposes a comprehensive risk assessment framework that integrates Sequential Monte Carlo Simulation (SMCS) with fuzzy clustering techniques. The approach begins by developing a multi-stage probabilistic model that accounts for the stochastic nature of fault initiation, location, recloser operation, and clearance time. SMCS is then employed to simulate the temporal evolution of system states, capturing the dynamic interactions between time-varying load profiles and failure propagation. A multi-dimensional risk index system is established to evaluate consequences including stability loss, voltage/frequency deviations, line overloads, and load/generation losses. To extract meaningful insights from the voluminous simulation output, Principal Component Analysis (PCA) is utilized for dimensionality reduction, followed by Fuzzy C-Means (FCM) clustering to classify failure modes and identify critical system vulnerabilities. The proposed methodology is validated using the IEEE 36-node test system, with results demonstrating its effectiveness in identifying high-risk components (particularly Line 54) and quantifying the risk levels of different failure sequences. This approach provides valuable insights for system planning and operational defense strategies.