Brown Measure Based Spectral Distribution Analysis for Spatial-Temporal Localization of Cascading Events in Power Grids

Real-time detection and analysis of cascading events are crucial to avoiding large blackouts in power systems. Based on spectral distribution analysis (SDA) of online monitoring data, this article proposes an approach for spatial-temporal localization of cascading events in a modern grid. The anomaly detection problem is built upon hypothesis testing, where a test statistic is designed in advance. The empirical spectral distribution (ESD) of the test statistic is compared with its theoretical counterpart, i.e., the asymptotic spectral distribution (ASD) obtained by Brown Measure. The proposed approach is sensitive and capable of temporally locating the occurrence time of each subevent (including severe disturbances, failures, and some typical physical attacks) in a cascading event. Simultaneously, the spatial information of each subevent is given. It is experimentally justified that the proposed approach is robust to normal fluctuations, oscillations, and bad data. Besides, it can be applied in both large-scale and small-scale systems utilizing the tensor product method. Case studies with simulated data in an ACTIVSg500 System and real-world online monitoring data verify the effectiveness of the proposed approach.