Temporal Graph Super Resolution on Power Distribution Network Measurements

Abstract

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 paper, 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 paper, 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.

State Estimation for Reconfigurable Distribution Systems Using Graph Attention Network

“…In practice, the dataset consists of various data sources such as AMI, SCADA system, and PMU. The super-resolution method proposed in [41] can recover asynchronous data into high-resolution data for neural network model training.…”

A Review of Graph Neural Networks and Their Applications in Power Systems

“…In [102], the novel hybrid forms of GNNs are designed to test whether medium-voltage distribution networks satisfy the safe property of the topology or not. To improve the state awareness of distribution networks, [103] uses the spatial-based GCNs to realize the super-resolution of measurements such as topology graphs.…”

M-SRPCNN: A Fully Convolutional Neural Network Approach for Handling Super Resolution Reconstruction on Monthly Energy Consumption Environments

“…Lu and Jin [15] proposed to use a CNN and a GAN that respects the overall value in the prediction, Liu et al [16] proposed to use SRP to reconstruct missing values using a CNN called SRPCNN. Zhang et al [17] proposed to treat consumption as images and use a GAN called SRGAN to reconstruct the higher resolution load profile, Zhang et al [18] proposed a GAN called DISRGAN applied to photovoltaic plants treating the consumption as images, Ren et al [19] proposed a CNN to upsample low resolution sources into high resolution sources and Wang et al [20] proposed to apply a Graph CNN (GCN) for spatial-temporal convolutions by modeling consumption data as graphs in order to reconstruct higher resolutions from lower resolutions. Other non-linear modeling approaches that proved to be successful in modeling systems in various fields were proposed like Pozna and Precup [21], Zall and Kangavari [22], Hedrea et al [23]; also, the works of Ahmed et al [24], Precup et al [25], Yuhana et al [26] obtained good results in the topic.…”