Temporal Graph Super Resolution on Power Distribution Network Measurements

• Published in: IEEE access 2021, Vol.9, p.70628-70638
• Main Authors: Wang, Zhisheng, Chen, Ying, Huang, Shaowei, Zhang, Xuemin, Liu, Xiaopeng
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Convolution; Electric power distribution; Feeders; Flow stability; graph convolution; Industrial applications; Power distribution; power distribution network; Power flow; Power measurement; Power systems; Sensors; State estimation; Super resolution; Superresolution; Thermal sensors; Topology; Voltage measurement
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2021.3054034

The applications of super-resolution (SR) technology in the field of image completion are successful. Nevertheless, industry applications demand not only image completion but also the topology and time-series completion. In this article, the SR technology on a topology graph is studied in the scenario of recovering measurements in power distribution systems for cost saving and security & stability improvement. The power flow and voltage magnitude measurements on feeders are reported at different frequencies. In this article, a new data completion method considering distribution system topology is proposed. Firstly, the graph convolutional neural network (GCN) is used for spatial-temporal convolution on a graph, and then the power system state estimation (SE) is used introducing the physical constraints. This method realizes the super-resolution of distribution system measurements, improves the state awareness of distribution systems. Hence, it helps to improve the efficiency of distribution network operation and to reduce equipment failures.