An Improved Graph Convolutional Network-based State Estimation Method of Distribution Network

In order to improve reliability of distribution network and ensure its stable operation, the realtime state estimation of distribution network plays an important role in improving the reliability of distribution network and ensuring its stable operation. To perform state estimation with high accuracy under insufficient measuring information of distribution network, a new state estimation method for distribution network based on improved physical data fusion of graph convolutional network (abbr. GCN) was proposed. Firstly, in the proposed method a small number of phasor measuring units were utilized to divide the distribution network into partitions, then according to the maximum diameter after the partitioning the number of convolution modules required by the convolution network was determined. Secondly, the expressing method of traditional adjacent matrix in GCN was modified, thus by use of the convolution network all the state variables in the partitioned subsystems of distribution network were expressed by the measured quantities. By means of typical computing example of IEEE 33 bus system, the effectiveness of the proposed method was verified. Meanwhile, through the comparison test with traditional Gauss-Newton optimization algorithm and that with traditional deep learning network, the results show that using the proposed method not only the computation complexity can be transferred to off-line phase, but also it can be independent of the pseudo measurement with high redundancy, so the proposed method possesses higher estimation accuracy and computing speed.