In this paper, we propose to use graph convolutional neural networks (GCNNs) to learn state estimators from data. The resulting estimators are distributed and ...
This paper proposes to use graph convolutional neural networks (GCNNs) to learn state estimators from data, and shows the promise of GCNNs in distributed state ...
Jan 3, 2023 · In this paper, we propose to use graph convolutional neural networks (GCNNs) to learn state estimators from data. The resulting estimators are ...
The applied method can outperform the applied Gauss-Newton method on the distribution system SE task with limited measurements and noisy data.
Jul 23, 2022 · This paper introduces an original graph neural network based SE implementation over the augmented factor graph of the nonlinear power system SE.
Missing: Convolutional | Show results with:Convolutional
Feb 23, 2023 · This article proposes GNN-based state estimators by modeling the state estimation problem in distribution systems as node-level prediction problems.
Missing: Power Convolutional
Jun 27, 2024 · The main trend in these methods involves utilizing artificial neural networks (ANNs) to describe either a portion or the entirety of the power ...
This article proposes a distribution network state estimation method based on an improved deep graph convolutional network (GCN). This method uses physical ...
Title: State Estimation for Power Distribution System using Graph Neural Networks · Clarkson University · Los Alamos National Laboratory · Entrust ...
Missing: Convolutional | Show results with:Convolutional
We develop a thorough approach by utilizing Graph Convolutional Neural Networks (GCNNs) and attention mechanism in PSSE based on Supervisory Control and Data ...