Discovering and Labeling Power System Events in Synchrophasor Data with Matrix Profile

An increasing number of phasor measurement units (PMUs) are being installed to improve power systems' reliability and visibility throughout the world. Due to the high sampling speed, PMUs generate a large volume of streaming synchrophasor data. This huge dataset calls for robust and efficient data analytic tools to discover and label system events, which will greatly enhance the stability of power systems. In this work, we introduce a novel event discovery and labeling framework based on matrix profile. This framework is model-free, fast, scalable, and only requires one user-defined parameter. Since matrix profiles are built by measuring the similarities between subsequences of a time series, our approach has great potential in automatically labeling the system events in the synchrophasor data. Case studies are carried out on real-world PMU data to validate the effectiveness of the proposed framework.