Predicting Basin Stability of Power Grids using Graph Neural Networks

Abstract

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.

arXiv comment17 pages, 25 pages including appendix, 18 pictures plus tikz pictures

References in corpus (24)

• Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
• Simplifying Graph Convolutional Networks
• The Kuramoto model in complex networks
• Analysis of a power grid using the Kuramoto-like model
• Graph Neural Networks with convolutional ARMA filters
• Topology Adaptive Graph Convolutional Networks
• Design Space for Graph Neural Networks
• A Random Growth Model for Power Grids and Other Spatially Embedded Infrastructure Networks
• Emergence of synchronization in multiplex networks of mobile Rössler oscillators
• Deciphering the imprint of topology on nonlinear dynamical network stability
• Basin stability measure of different steady states in coupled oscillators
• Community consistency determines the stability transition window of power-grid nodes
• Building blocks of the basin stability of power grids
• Stochastic basins of attraction and generalized committor functions
• PowerDynamics.jl -- An experimentally validated open-source package for the dynamical analysis of power grids
• Power grid stability under perturbation of single nodes: Effects of heterogeneity and internal nodes
• Transient chaos enforces uncertainty in the British power grid
• Multistability and Variations in Basin of Attraction in Power-grid Systems
• Power-grid stability predictions using transferable machine learning
• Bounding the first exit from the basin: Independence Times and Finite-Time Basin Stability
• Monte Carlo Basin Bifurcation Analysis
• NetworkDynamics.jl -- Composing and simulating complex networks in Julia
• On structural and dynamical factors determining the integrated basin instability of power-grid nodes
• Multitask Learning on Graph Neural Networks: Learning Multiple Graph Centrality Measures with a Unified Network

Cited by in corpus (6)

• Power Flow Balancing with Decentralized Graph Neural Networks
• Toward Dynamic Stability Assessment of Power Grid Topologies using Graph Neural Networks
• A Framework for Synthetic Power System Dynamics
• Reinforcement Learning Optimizes Power Dispatch in Decentralized Power Grid
• Coexistence of asynchronous and clustered dynamics in noisy inhibitory neural networks
• Heterogeneous Graph Neural Networks for Short-term State Forecasting in Power Systems across Domains and Time Scales: A Hydroelectric Power Plant Case Study