Graph Neural Networks for Transmission Grid Topology Control: Busbar Information Asymmetry and Heterogeneous Representations

• URL: http://arxiv.org/abs/2501.07186v3
• Date: Fri, 03 Oct 2025 11:03:54 GMT
• Title: Graph Neural Networks for Transmission Grid Topology Control: Busbar Information Asymmetry and Heterogeneous Representations
• Authors: Matthijs de Jong, Jan Viebahn, Yuliya Shapovalova,
• Abstract summary: Topology control is an appealing method for relieving congestion, but traditional approaches for topology discovery have proven too slow for practical application.<n>Recent research has focused on machine learning (ML) as an efficient alternative.<n>This study investigates the effect of the graph representation on GNN effectiveness for topology control.
• Score: 0.07646713951724009
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Factors such as the proliferation of renewable energy and electrification contribute to grid congestion as a pressing problem. Topology control is an appealing method for relieving congestion, but traditional approaches for topology discovery have proven too slow for practical application. Recent research has focused on machine learning (ML) as an efficient alternative. Graph neural networks (GNNs) are particularly well-suited for topology control applications due to their ability to model the graph structure of power grids. This study investigates the effect of the graph representation on GNN effectiveness for topology control. We identify the busbar information asymmetry problem inherent to the popular homogeneous graph representation. We propose a heterogeneous graph representation that resolves this problem. We apply GNNs with both representations and a fully connected neural network (FCNN) baseline on an imitation learning task. The models are evaluated by classification accuracy and grid operation ability. We find that heterogeneous GNNs perform best on in-distribution network configurations, followed by FCNNs, and lastly, homogeneous GNNs. We also find that both GNN types generalize better to out-of-distribution network configurations than FCNNs.

Related papers

• Generalization of Graph Neural Network Models for Distribution Grid Fault Detection [2.183622338864934]
  This paper systematically benchmarks various Graph Neural Networks (GNNs) in an RNN+GNN pipeline model.<n>To the best of our knowledge, we are the first to (i) propose to use GraphSAGE and Graph Attention (GAT, GATv2) in an RGNN for fault diagnosis.<n>Our experimental results on the IEEE 123-node distribution network show that RGATv2 has superior generalization capabilities.
  arXiv  Detail & Related papers  (2025-10-03T23:46:31Z)
• Graph Neural Networks at a Fraction [1.8175282137722093]
  This paper introduces Quaternion Message Passing Neural Networks (QMPNNs), a framework that leverages quaternion space to compute node representations. We present a novel perspective on Graph Lottery Tickets, redefining their applicability within the context of GNNs and QMPNNs.
  arXiv  Detail & Related papers  (2025-02-10T03:55:09Z)
• Degree-based stratification of nodes in Graph Neural Networks [66.17149106033126]
  We modify the Graph Neural Network (GNN) architecture so that the weight matrices are learned, separately, for the nodes in each group. This simple-to-implement modification seems to improve performance across datasets and GNN methods.
  arXiv  Detail & Related papers  (2023-12-16T14:09:23Z)
• Relation Embedding based Graph Neural Networks for Handling Heterogeneous Graph [58.99478502486377]
  We propose a simple yet efficient framework to make the homogeneous GNNs have adequate ability to handle heterogeneous graphs. Specifically, we propose Relation Embedding based Graph Neural Networks (RE-GNNs), which employ only one parameter per relation to embed the importance of edge type relations and self-loop connections.
  arXiv  Detail & Related papers  (2022-09-23T05:24:18Z)
• Anomal-E: A Self-Supervised Network Intrusion Detection System based on Graph Neural Networks [0.0]
  This paper investigates Graph Neural Networks (GNNs) application for self-supervised network intrusion and anomaly detection. GNNs are a deep learning approach for graph-based data that incorporate graph structures into learning. We present Anomal-E, a GNN approach to intrusion and anomaly detection that leverages edge features and graph topological structure in a self-supervised process.
  arXiv  Detail & Related papers  (2022-07-14T10:59:39Z)
• Network In Graph Neural Network [9.951298152023691]
  We present a model-agnostic methodology that allows arbitrary GNN models to increase their model capacity by making the model deeper. Instead of adding or widening GNN layers, NGNN deepens a GNN model by inserting non-linear feedforward neural network layer(s) within each GNN layer.
  arXiv  Detail & Related papers  (2021-11-23T03:58:56Z)
• Power Flow Balancing with Decentralized Graph Neural Networks [4.812718493682454]
  We propose an end-to-end framework based on a Graph Neural Network (GNN) to balance the power flows in a generic grid. The proposed framework is efficient and, compared to other solvers based on deep learning, is robust to perturbations not only to the physical quantities on the grid components, but also to the topology.
  arXiv  Detail & Related papers  (2021-11-03T12:14:56Z)
• A Unified View on Graph Neural Networks as Graph Signal Denoising [49.980783124401555]
  Graph Neural Networks (GNNs) have risen to prominence in learning representations for graph structured data. In this work, we establish mathematically that the aggregation processes in a group of representative GNN models can be regarded as solving a graph denoising problem. We instantiate a novel GNN model, ADA-UGNN, derived from UGNN, to handle graphs with adaptive smoothness across nodes.
  arXiv  Detail & Related papers  (2020-10-05T04:57:18Z)
• Graph Neural Networks: Architectures, Stability and Transferability [176.3960927323358]
  Graph Neural Networks (GNNs) are information processing architectures for signals supported on graphs. They are generalizations of convolutional neural networks (CNNs) in which individual layers contain banks of graph convolutional filters.
  arXiv  Detail & Related papers  (2020-08-04T18:57:36Z)
• Graph Neural Networks for Motion Planning [108.51253840181677]
  We present two techniques, GNNs over dense fixed graphs for low-dimensional problems and sampling-based GNNs for high-dimensional problems. We examine the ability of a GNN to tackle planning problems such as identifying critical nodes or learning the sampling distribution in Rapidly-exploring Random Trees (RRT) Experiments with critical sampling, a pendulum and a six DoF robot arm show GNNs improve on traditional analytic methods as well as learning approaches using fully-connected or convolutional neural networks.
  arXiv  Detail & Related papers  (2020-06-11T08:19:06Z)
• Binarized Graph Neural Network [65.20589262811677]
  We develop a binarized graph neural network to learn the binary representations of the nodes with binary network parameters. Our proposed method can be seamlessly integrated into the existing GNN-based embedding approaches. Experiments indicate that the proposed binarized graph neural network, namely BGN, is orders of magnitude more efficient in terms of both time and space.
  arXiv  Detail & Related papers  (2020-04-19T09:43:14Z)
• Graphs, Convolutions, and Neural Networks: From Graph Filters to Graph Neural Networks [183.97265247061847]
  We leverage graph signal processing to characterize the representation space of graph neural networks (GNNs) We discuss the role of graph convolutional filters in GNNs and show that any architecture built with such filters has the fundamental properties of permutation equivariance and stability to changes in the topology. We also study the use of GNNs in recommender systems and learning decentralized controllers for robot swarms.
  arXiv  Detail & Related papers  (2020-03-08T13:02:15Z)
• Generalization and Representational Limits of Graph Neural Networks [46.20253808402385]
  We prove that several important graph properties cannot be computed by graph neural networks (GNNs) that rely entirely on local information. We provide the first data dependent generalization bounds for message passing GNNs. Our bounds are much tighter than existing VC-dimension based guarantees for GNNs, and are comparable to Rademacher bounds for recurrent neural networks.
  arXiv  Detail & Related papers  (2020-02-14T18:10:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.