Parameter Identification in Power Transmission Systems Based on Graph Convolution Network

Parameter Identification plays an important role in electric power transmission systems. Existing approaches for parameter identification tasks typically have two limitations: (1) They generally ignored development trend of historical data, and did not mine characteristics of corresponding power grid branches. (2) They did not consider the constraints of power grid topology, and treated different branches independently. Therefore, they could not characterize correlations between the center node and its neighborhoods. To overcome these limitations, this work proposes a multi-task graph convolutional neural network (MT-GCN) which utilizes the graph convolutional network (GCN) and the fully convolutional network (FCN) as building blocks for parameter identification. Specially, GCN can extract the structure information to enhance local feature extraction. FCN is a decoding module following GCN module, and it is used to identify the parameters of each branch according to its characteristics. Compared with previous methods, the proposed method is significantly improved in accuracy. Besides, this method is robust to measurement noise and errors, and can cope with multiple conditions in real power transmission systems.

Automated summary

Parameter Identification is important in electric power transmission systems. Existing approaches have limitations in considering development trend of historical data and power grid topology constraints. This work proposes a multi-task graph convolutional neural network (MT-GCN) using GCN and FCN as building blocks, which significantly improves accuracy and robustness in real power transmission systems.