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A Review of Graph Neural Networks and Their Applications in Power Systems
[article]
2021
arXiv
pre-print
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In this paper, a comprehensive overview of graph neural networks (GNNs) in power systems is proposed. ...
Recently, many publications generalizing deep neural networks for graph-structured data in power systems have emerged. ...
In this paper, a comprehensive overview of graph neural networks (GNNs) in power systems is proposed. ...
arXiv:2101.10025v2
fatcat:6sptisxciza45kx67ah3xwww4i
2019 Index IEEE Transactions on Signal and Information Processing over Networks Vol. 5
2019
IEEE Transactions on Signal and Information Processing over Networks
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., +, TSIPN March 2019 152-167
Convolutional neural nets
Automatic Modulation Classification Using Convolutional Neural Network
With Features Fusion of SPWVD and BJD. ...
., +, TSIPN March 2019 15-30 Wigner distribution Automatic Modulation Classification Using Convolutional Neural Network With Features Fusion of SPWVD and BJD. ...
doi:10.1109/tsipn.2019.2959414
fatcat:ixpx5rg5l5hshkt2ppvie3afqe
Supporting Future Electrical Utilities: Using Deep Learning Methods in EMS and DMS Algorithms
[article]
2023
arXiv
pre-print
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Electrical utilities can benefit from this review by re-implementing or enhancing the algorithms traditionally used in energy management systems (EMS) and distribution management systems (DMS). ...
This necessitates the development of near real-time power system algorithms, demanding lower computational complexity regarding the power system size. ...
For example, GNNs have been used in combination with heterogeneous power system factor graphs to solve the state estimation problem, both linear [30] and nonlinear [31] . ...
arXiv:2303.00428v1
fatcat:7hekkf7lfvha7d5hjmhnqjmjie
Real Time State Estimation of Power Grids Using Convolutional Neural Networks and State Forecasting Via Recurrent Neural Networks
[article]
2021
arXiv
pre-print
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To address this problem, this research proposes a state estimation method for power grids using Convolutional Neural Networks (CNN). ...
Furthermore, the research also proposes Power System State Forecasting for improving system awareness and resilience. The forecasting is carried out using a model of Recurrent Neural Network (RNN). ...
To address this problem, this research proposes a state estimation method for power grids using Convolutional Neural Networks (CNN). ...
arXiv:2106.13084v1
fatcat:vjpb3km7ujf77bwojfbxxyks5u
A Review of Graph Neural Networks and Their Applications in Power Systems
2022
Journal of Modern Power Systems and Clean Energy
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., graph convolutional networks, are summarized. ...
In this paper, a comprehensive overview of graph neural networks (GNNs) in power systems is proposed. ...
The classical GNNs mainly include graph convolutional networks (GCNs), graph recurrent neural networks (GRNNs), graph attention networks (GATs), graph generative networks (GGNs), spatial-temporal graph ...
doi:10.35833/mpce.2021.000058
fatcat:nbzvs2tskjgpni53fn4h6k5y3i
Graph Neural Networks for Distributed Power Allocation in Wireless Networks: Aggregation Over-the-Air
[article]
2023
arXiv
pre-print
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In this paper, we aim to design low signaling overhead distributed power allocation schemes by using graph neural networks (GNNs), which are scalable to the number of wireless links. ...
To further reduce the signaling overhead, we propose the Air message passing recurrent neural network (Air-MPRNN), where each node utilizes the graph embedding and local state in the previous frame to ...
Among different GNN architectures, spatial convolutional graph neural network [19] is one of the most widely used architectures in solving the power allocation problems for wireless networks [20] . ...
arXiv:2207.08498v2
fatcat:aoyu6nbspfds3g6maymm536sdu
Solving Statistical Mechanics on Sparse Graphs with Feedback Set Variational Autoregressive Networks
[article]
2020
arXiv
pre-print
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of the FVS, then learns a variational distribution parameterized using neural networks to approximate the original Boltzmann distribution. ...
lattices our approach is significantly faster and more accurate than recently proposed variational autoregressive networks using convolution neural networks. ...
On this problem we set a baseline using variational autoregressive network using convolution neural networks [8] . ...
arXiv:1906.10935v2
fatcat:zt3notnp7fewtitf2pq2bat4oa
Application of Multiattention Mechanism in Power System Branch Parameter Identification
2021
Complexity
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To overcome these limitations, we propose a novel multitask Graph Transformer Network (GTN), which combines a graph neural network and a multiattention mechanism to construct our model. ...
single branch characteristics, but they are only used to identify a single target and cannot make full use of the historical information of power grid data. (2) Deep learning methods can complete model ...
Acknowledgments is work was supported by the project of SGCC "Data and Model Driven Parameter Identification for the Branches in Power Grid" (5108-2020190227A-0-0-00) ...
doi:10.1155/2021/1834428
fatcat:zymvbie4pvhtxgyimrmi2pkbim
Predicting Dynamic Stability of Power Grids using Graph Neural Networks
[article]
2022
arXiv
pre-print
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2021 pre-print arXiv:2108.08230v1 fatcat:fsmbtkz3tja4tis5g3sj4v37vmOther Versions
We investigate the feasibility of applying graph neural networks (GNN) to predict dynamic stability of synchronisation in complex power grids using the single-node basin stability (SNBS) as a measure. ...
To do so, we generate two synthetic datasets for grids with 20 and 100 nodes respectively and estimate SNBS using Monte-Carlo sampling. ...
APPENDIX E CONVOLUTIONAL NEURAL NETWORKS Convolutional Neural Networks (CNNs) are one of the key technologies for image recognition. ...
arXiv:2108.08230v2
fatcat:l7ltkqbkxzcebnmx7cbyhhodve
Quantum Graph Neural Networks
[article]
2019
arXiv
pre-print
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to be executed on distributed quantum systems over a quantum network. ...
Along with this general class of ansatze, we introduce further specialized architectures, namely, Quantum Graph Recurrent Neural Networks (QGRNN) and Quantum Graph Convolutional Neural Networks (QGCNN) ...
The authors would like to thank Edward Farhi, Jae Yoo, and Li Li for useful discussions. ...
arXiv:1909.12264v1
fatcat:tnovpmgworfwdd6qgp5hw4cexa
An Overview on the Application of Graph Neural Networks in Wireless Networks
[article]
2021
arXiv
pre-print
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2021 pre-print arXiv:2107.03029v2 fatcat:wzzeiicojrfqvinlcs2dsczapiOther Versions
To effectively exploit the information of graph-structured data as well as contextual information, graph neural networks (GNNs) have been introduced to address a series of optimization problems of wireless ...
In recent years, with the rapid enhancement of computing power, deep learning methods have been widely applied in wireless networks and achieved impressive performance. ...
Graph Convolutional Neural Networks Graph convolutional neural networks (GCNs) implement convolutional operation on graph-structured data. ...
arXiv:2107.03029v3
fatcat:2hf2gelxrjbwpiwzyjw5cat5wu
2020 Index IEEE Transactions on Neural Networks and Learning Systems Vol. 31
2020
IEEE Transactions on Neural Networks and Learning Systems
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., +, TNNLS Nov. 2020 4726-4736 Stubborn State Estimation for Delayed Neural Networks Using Saturating Output Errors. ...
., +, TNNLS Dec. 2020 5456-5467 Stubborn State Estimation for Delayed Neural Networks Using Saturating Output Errors. ...
Zhao, X., +, TNNLS Oct. 2020 3777-3787 On the Working Principle of the Hopfield Neural Networks and its Equivalence to the GADIA in Optimization. Uykan, Z., ...
doi:10.1109/tnnls.2020.3045307
fatcat:34qoykdtarewhdscxqj5jvovqy
Graph Neural Network and Koopman Models for Learning Networked Dynamics: A Comparative Study on Power Grid Transients Prediction
[article]
2022
arXiv
pre-print
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The other class of models is based on the graph convolutional neural networks which are adept at capturing the inherent spatio-temporal correlations within the power network. ...
networks using only streaming measurements and the network topology as input. ...
In dynamic state estimation, the states of the system at the current time-point are estimated using available measurements and some knowledge of the system model. ...
arXiv:2202.08065v1
fatcat:iszdezbr5rhjjepuhc5vvxgufm
Temporal Graph Super Resolution on Power Distribution Network Measurements
2021
IEEE Access
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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. ...
And uses the distribution network state estimation to increase precision. ...
doi:10.1109/access.2021.3054034
fatcat:cn44k6aeqrcrpk73yivnufb52q
Large-Scale Graph Reinforcement Learning in Wireless Control Systems
[article]
2022
arXiv
pre-print
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The interference and fading patterns among plants and controllers in the network, however, induce a time-varying graph that can be used to construct policy representations based on graph neural networks ...
Modern control systems routinely employ wireless networks to exchange information between spatially distributed plants, actuators and sensors. ...
Graph Neural Networks GNNs can be viewed as a generalization of the popular convolutional neural network (CNN) model. ...
arXiv:2201.09859v2
fatcat:42mramfrird6dd3iupdqemjmyq
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