Adaptive Graph Convolutional Network-Based Distribution System State Estimation
17-21 July 2022
Denver, CO, USA
The management and control of the power systems rely on reliable and timely distribution system state estimation, which is present to be challenging due to significant voltage variations caused by high renewables. To tackle this problem, a graph convolutional network (AGCN) is proposed for the distribution system state estimation (DSSE) by considering highly volatile renewable generation. In particular, the AGCN can enable prompt state estimation for viable system states. In the proposed model, the graph convolutional layer can capture the correlations of the nodal power injections so that enhanced estimation accuracy can be achieved. Moreover, the node-embedding technique is employed in the graph convolutional layer to represent the nonlinear correlation nature, through which the proposed model is allowed to cover general scenarios in the application. The simulation results have been provided to verify the accuracy and effectiveness of the proposed model through IEEE 33-node and the 118-node distribution systems.
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|Document Type||Conference paper|
|Department||Department of Electrical and Electronic Engineering|
1.Southern University of Science and Technology,Department of Electrical and Electronic Engineering,Shenzhen,China
2.The Hong Kong Polytechnic University,Department of Electrical Engineering,Hong Kong
|First Author Affilication||Department of Electrical and Electronic Engineering|
|Corresponding Author Affilication||Department of Electrical and Electronic Engineering|
|First Author's First Affilication||Department of Electrical and Electronic Engineering|
Wu，Huayi,Jia，Youwei,Xu，Zhao. Adaptive Graph Convolutional Network-Based Distribution System State Estimation[C],2022:1-5.
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