A multi-rate sampling PMU-based event classification in active distribution grids with spectral graph neural network

Phasor measurement units (PMUs) are time-synchronized measurement devices that have been proliferated in transmission networks during the last two decades. Recently, there have been efforts to bring this technology to distribution grids for different applications such as three-phase state estimation, fault and event analyses, and phase identification. Streamed time-synchronized voltage and current phasor data can be used for events classification and region identification along distribution feeders to determine the type and location of events, which are important features of any fault and event detection, location, and isolation software. In this paper, the spectral theory-based graph convolution is used for event classification and region identification. The proposed model uses modified graph convolution filters to aggregate the regional multi-rate samples of PMU data, i.e., voltage magnitude and angles from several nodes. Besides these temporal data of the measured nodes, the physical configuration of the network containing edge features are given to the graph convolution network (GCN) to not only classify the event type, but also identify the affected region and location. The proposed graph-based method is tested on a standard test system with capacitor and distributed energy resources-related events, malfunction of voltage regulator, sudden load changes, and different types of faults. The results are compared with baseline methods, Chebyshev graph neural network (GNN), decision tree, logistic regression and K-nearest neighbor using the accuracy, recall, precision and F-1 score metrics. Furthermore, performance sensitivity analysis is carried out with respect to the number of installed PMUs, measurement noise level, size of available historical data, availability of network edge features, and different designs of GNN.