Graph Neural Networks for Decentralized Controllers

Gama, Fernando; Tolstaya, Ekaterina; Ribeiro, Alejandro

doi:10.1109/icassp39728.2021.9414563

Cited by 16 publications

(4 citation statements)

References 21 publications

(51 reference statements)

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“…Since power grids have an underlying graph structure, the recent development of graph representation learning 22,23 introduces promising methods to use machine learning in this domain. There are a number of applications dealing with GNNs and different power flow-related tasks [24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39] and to predict transient dynamics in microgrids. 40 There is also literature using conventional ML methods dealing with the basin stability 41,42 in the context of power grids.…”

Section: E Related Work On Power Grid Property Predictionmentioning

confidence: 99%

Toward dynamic stability assessment of power grid topologies using graph neural networks

Nauck,

Lindner,

Schürholt

et al. 2023

Chaos: An Interdisciplinary Journal of Nonlinear Science

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To mitigate climate change, the share of renewable energies in power production needs to be increased. Renewables introduce new challenges to power grids regarding the dynamic stability due to decentralization, reduced inertia, and volatility in production. Since dynamic stability simulations are intractable and exceedingly expensive for large grids, graph neural networks (GNNs) are a promising method to reduce the computational effort of analyzing the dynamic stability of power grids. As a testbed for GNN models, we generate new, large datasets of dynamic stability of synthetic power grids and provide them as an open-source resource to the research community. We find that GNNs are surprisingly effective at predicting the highly non-linear targets from topological information only. For the first time, performance that is suitable for practical use cases is achieved. Furthermore, we demonstrate the ability of these models to accurately identify particular vulnerable nodes in power grids, so-called troublemakers. Last, we find that GNNs trained on small grids generate accurate predictions on a large synthetic model of the Texan power grid, which illustrates the potential for real-world applications.

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Section: E Related Work On Power Grid Property Predictionmentioning

confidence: 99%

Toward dynamic stability assessment of power grid topologies using graph neural networks

Nauck,

Lindner,

Schürholt

et al. 2023

Chaos: An Interdisciplinary Journal of Nonlinear Science

View full text Add to dashboard Cite

show abstract

“…Existing works assume that each robot has access to perceptions of the immediate surroundings and that it can communicate with neighbor robots. Therefore, the GNN applied here is to control the entire swarm to transmit, receive, and process these messages between neighbor robots in order to decide on actions [44,75,102,123,143,152,259,260,276]. Autonomous vehicles are a type of more advanced robots with rich sensors, and have the capability to drive autonomously and safely on the road.…”

Section: Robotics and Autonomous Vehiclementioning

confidence: 99%

Graph Neural Networks in IoT: A Survey

Dong¹,

Tang²,

Wang³

et al. 2022

Preprint

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The Internet of Things (IoT) boom has revolutionized almost every corner of people's daily lives: healthcare, home, transportation, manufacturing, supply chain, and so on. With the recent development of sensor and communication technologies, IoT devices including smart wearables, cameras, smartwatches, and autonomous vehicles can accurately measure and perceive their surrounding environment. Continuous sensing generates massive amounts of data and presents challenges for machine learning. Deep learning models (e.g., convolution neural networks and recurrent neural networks) have been extensively employed in solving IoT tasks by learning patterns from multi-modal sensory data. Graph Neural Networks (GNNs), an emerging and fast-growing family of neural network models, can capture complex interactions within sensor topology and have been demonstrated to achieve state-of-the-art results in numerous IoT learning tasks. In this survey, we present a comprehensive review of recent advances in the application of GNNs to the IoT field, including a deep dive analysis of GNN design in various IoT sensing environments, an overarching list of public data and source code from the collected publications, and future research directions. To keep track of newly published works, we collect representative papers and their open-source implementations and create a Github repository at GNN4IoT. CCS Concepts: • Human-centered computing → Ubiquitous and mobile computing systems and tools; • Computing methodologies → Neural networks.

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“…In the dynamic graph setting, we use an example involving agents flocking together in a decentralized manner (Gama et al (2021)). In this example, there are agents that seek to move in the same direction at some velocity without hitting each other.…”

Section: Dynamic Graphsmentioning

confidence: 99%

Robust Graph Neural Networks via Probabilistic Lipschitz Constraints

Arghal¹,

Lei²,

Bidokhti³

2021

Preprint

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Graph neural networks (GNNs) have recently been demonstrated to perform well on a variety of network-based tasks such as decentralized control and resource allocation, and provide computationally efficient methods for these tasks which have traditionally been challenging in that regard. However, like many neural-network based systems, GNNs are susceptible to shifts and perturbations on their inputs, which can include both node attributes and graph structure. In order to make them more useful for real-world applications, it is important to ensure their robustness postdeployment. Motivated by controlling the Lipschitz constant of GNN filters with respect to the node attributes, we propose to constrain the frequency response of the GNN's filter banks. We extend this formulation to the dynamic graph setting using a continuous frequency response constraint, and solve a relaxed variant of the problem via the scenario approach. This allows for the use of the same computationally efficient algorithm on sampled constraints, which provides PAC-style guarantees on the stability of the GNN using results in scenario optimization. We also highlight an important connection between this setup and GNN stability to graph perturbations, and provide experimental results which demonstrate the efficacy and broadness of our approach.

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Graph Neural Networks for Decentralized Controllers

Cited by 16 publications

References 21 publications

Toward dynamic stability assessment of power grid topologies using graph neural networks

Toward dynamic stability assessment of power grid topologies using graph neural networks

Graph Neural Networks in IoT: A Survey

Robust Graph Neural Networks via Probabilistic Lipschitz Constraints

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