Data-Driven Event Identification Using Deep Graph Neural Network and PMU Data

Phasor measurement units (PMUs) are being widely installed on power transmission systems, which provides a unique opportunity to enhance wide-area situational awareness. One essential application is to utilize PMU data for real-time event identification. However, taking full advantage of all PMU data in event identification is still an open problem. Hence, we propose a novel event identification method using multiple PMU measurements and deep graph neural network techniques. Unlike the previous models that rely on data from single PMU and ignore the interactive relationships between different PMUs or use multiple PMUs but determine the functional connectivity manually, our method performs interactive relationship inference in a data-driven manner. To ensure the optimality of the interactive inference procedure, the proposed method learns the interactive graph jointly with the event identification model. Moreover, instead of generating a single statistical graph to represent pair-wise relationships among PMUs during different events, our approach produces different event identification-specific graphs for different power system events, which handles the uncertainty of event location. To test the proposed data-driven approach, a large real-world dataset from tens of PMU sources and the corresponding event logs have been utilized in this work. The numerical results validate that our method has higher identification accuracy compared to the existing methods