Research on the Identification of Simulation Error Regions Based on Graph Information in Power Systems

Given the vast scale of power systems, rapidly and accurately identifying simulation error regions to ensure the fidelity of regional models is crucial for the validity of simulation results. To address this challenge, this study introduces a graph-based error region identification method. First, time–frequency features are extracted from measurement trajectories, and a directed weighted adjacency matrix is constructed by integrating power flow data and physical network topology to characterize error propagation patterns. Next, the Markov Clustering (MCL) algorithm is applied to automatically partition the system into subregions, revealing areas with strong internal coupling and weak external connections. Within each identified subregion, a disturbance depth index is formulated using the root mean square error (RMSE) and Pearson correlation coefficient between simulated and measured trajectories, quantitatively assessing deviations between hybrid dynamic simulations and actual measurements to precisely localize regions exhibiting significant inconsistencies. Case studies demonstrate the effectiveness of the proposed method in capturing inter-bus coupling and isolating error-prone areas. Moreover, its responsiveness and computational efficiency make it highly suitable for real-time applications in parameter calibration and operational optimization of power systems.