We propose graph convolutional networks (GCNs) for the task of classifying signals supported on graphs. An important element of the GCN design is filter design.

The GCNs are designed to recognize different spatiotemporal patterns from high-dimensional data defined on a graph. The application of the proposed methods to ...

We propose graph convolutional networks (GCNs) for the task of classifying signals supported on graphs. An important element of the GCN design is filter design.

Fault detection. Predicting Power Outages Using Graph Neural Networks. ... Fault Detection and Isolation in Industrial Networks using Graph Convolutional Neural ...

We investigate several classification algorithms, including logistic regression, random forest, and graph convolutional neural networks. The methods can be ...

The graph convolutional neural network has two essential components that distinguish itself from other convolutional neural networks: Graph Fourier Transform ( ...

Abstract—Power system operations under contingency need to solve large-scale complex nonlinear optimization problems in a short amount of time, ...

2024/09/05 · This paper proposes graph convolutional neural networks (GCN) with self-supervised learning to identify the critical branches ...

2024/06/04 · Here, we present a graph reinforcement learning model for outage management in the distribution network to enhance its resilience.

SafePowerGraph integrates multiple PF and OPF simulators and assesses GNN performance under diverse scenarios, including energy price variations and power line ...