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.

17 pages, 25 pages including appendix, 18 pictures plus tikz pictures

References in corpus (9)

• Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
• Simplifying Graph Convolutional Networks
• Analysis of a power grid using the Kuramoto-like model
• Design Space for Graph Neural Networks
• Basin stability measure of different steady states in coupled oscillators
• PowerDynamics.jl -- An experimentally validated open-source package for the dynamical analysis of power grids
• Transient chaos enforces uncertainty in the British power grid
• Power-grid stability predictions using transferable machine learning
• NetworkDynamics.jl -- Composing and simulating complex networks in Julia

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