NetworkDynamics.jl—Composing and simulating complex networks in Julia

Lindner, Michael; Lincoln, Lucas; Drauschke, Fenja; Koulen, Julia Monika; Würfel, Hans; Plietzsch, Anton; Hellmann, Frank

doi:10.1063/5.0051387

Cited by 15 publications

(11 citation statements)

References 32 publications

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“…The code for the computation of SNBS is written in Julia [57] and the dynamic simulations rely on the package DifferentialEquations.jl [58]. For simulating more realistic power grids in future work we recommend the additional use of NetworkDynamics.jl [59] and PowerDynamics.jl [60]. Software packages used for ML-applications are listed in table A1.…”

Section: Data Availability Statementmentioning

confidence: 99%

Predicting basin stability of power grids using graph neural networks

et al. 2022

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The prediction of dynamical stability of power grids becomes more important and challenging with increasing shares of renewable energy sources due to their decentralized structure, reduced inertia and volatility. 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. Those datasets are used to train and evaluate the performance of eight different GNN-models. All models use the full graph without simplifications as input and predict SNBS in a nodal-regression-setup. We show that SNBS can be predicted in general and the performance significantly changes using different GNN-models. Furthermore, we observe interesting transfer capabilities of our approach: GNN-models trained on smaller grids can directly be applied on larger grids without the need of retraining.

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Section: Data Availability Statementmentioning

confidence: 99%

Predicting basin stability of power grids using graph neural networks

et al. 2022

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“…for (7). It is challenging to understand the quality of the first estimator rigorously without further information on the summand.…”

Section: Sampling Based Approximationsmentioning

confidence: 99%

“…The method developed here is designed with applications to power grids in mind. In parallel to this paper we present a software stack based on the capabilities of the Julia language [4,5], that allows tuning dynamical properties in power systems [6][7][8]. In this context a behavioral approach is particularly natural.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Behavioral Distance and Tuning - Reducing and aggregating complex systems

Hellmann¹,

Ekaterina²,

Kurths³

et al. 2021

Preprint

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Given a complex system with a given interface to the rest of the world, what does it mean for a the system to behave close to a simpler specification describing the behavior at the interface? We give several definitions for useful notions of distances between a complex system and a specification by combining a behavioral and probabilistic perspective. These distances can be used to tune a complex system to a specification. We show that our approach can successfully tune non-linear networked systems to behave like much smaller networks, allowing us to aggregate large sub-networks into one or two effective nodes. Finally, we discuss similarities and differences between our approach and H∞ model reduction.

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“…The code for the computation of SNBS is written in Julia [50] and the dynamic simulations rely on the package DifferentialEquations.jl [51]. For simulating more realistic power grids in future work we recommend the additional use of NetworkDynamics.jl [52] and PowerDynamics.jl [53]. Software packages used for MLapplications are listed in Table V:…”

Section: Appendix a Source Codementioning

confidence: 99%

Predicting Basin Stability of Power Grids using Graph Neural Networks

Nauck,

Lindner,

Schürholt

et al. 2021

Preprint

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The prediction of dynamical stability of power grids becomes more important and challenging with increasing shares of renewable energy sources due to their decentralized structure, reduced inertia and volatility. 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. Those datasets are used to train and evaluate the performance of eight different GNN-models. All models use the full graph without simplifications as input and predict SNBS in a nodal-regressionsetup. We show that SNBS can be predicted in general and the performance significantly changes using different GNN-models. Furthermore, we observe interesting transfer capabilities of our approach: GNN-models trained on smaller grids can directly be applied on larger grids without the need of retraining.

show abstract

NetworkDynamics.jl—Composing and simulating complex networks in Julia

Cited by 15 publications

References 32 publications

Predicting basin stability of power grids using graph neural networks

Predicting basin stability of power grids using graph neural networks

Probabilistic Behavioral Distance and Tuning - Reducing and aggregating complex systems

Predicting Basin Stability of Power Grids using Graph Neural Networks

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