Temporal Graph Super Resolution on Power Distribution Network Measurements

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 scenar...

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Bibliographic Details
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
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Summary: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.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2021.3054034