State Estimation of Power System Based on a Message Passing Neural Network

In recent years, data-driven methods have been widely used in power system state estimation. However, the existing data-driven state estimation models can only deal with Euclidian data and cannot effectively mine non-Euclidian data such as topology information. Therefore, the existing data-driven state estimation models have poor adaptability when the topology changes frequently. This paper proposes a power system state estimation model based on Message Passing Neural Network (MPNN). Firstly, topology information and measurement information are used to construct graph data sets. Secondly, the state estimation model is obtained by training the message passing graph neural network based on graph data in different topologies. Finally, the state quantity of the current section can be obtained by inputting the graph data under the topology into the trained network model during online application. The results are compared with the weighted least square method, the weighted least absolute value method, the deep neural network algorithm and the convolutional neural network algorithm. The results show that the algorithm can better adapt to the time-varying characteristics of power grid topology. © 2023 Power System Technology Press. All rights reserved.