Spatiotemporal Graph Convolutional Neural Network-Based Forecasting-Aided State Estimation Using Synchrophasors

Power system state estimation is a primary and major method for monitoring power grids in real time. Massive synchrophasor data contains temporal correlations and spatial characteristics based on the physical constraints of the power system. The spectral-domain convolution method based on the graph Fourier transform is used to construct a multilayer graph convolution neural network model to predict the short-term states of a power system, including the latest state, when the power system is in the quasi-steady state. Combining the advantages of linear state estimation, a forecasting-aided state estimation method that can take advantage of predicted values and phase measurement units is designed to obtain the real-time state. Furthermore, predicted innovations analysis method are proposed to identify system mutations and bad data. Enough simulation tests validate that the proposed method can accurately estimate the real-time state of a power system.