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