State Estimation of Energy Internet Using SCADA and PMU Data Based on Graph Convolutional Networks

The real-time state estimation is crucial to guarantee the stable operation of energy Internet (EI) which has variable loads and distributed power generations. Therefore, this paper proposes a real-time transient state estimation method for EI based on graph convolutional networks (GCN). Using data of SCADA and limited phasor measurement unit (PMU), the GCN in the proposed method fuses the heterogeneous data of EI buses with the adjacency matrix that represents the topology of EI. Then the transient states of EI buses without PMU measurement are estimated by SCADA data and adjacent PMU data through the training of GCN model. The case study on the simulation data of an IEEE 9 bus system that considers fault injection and disturbances verifies the effectiveness of the proposed approach. The result shows that the proposed approach achieves fast and accurate state estimation of all EI buses during the transient process of faults and disturbances.