PowerGraph: A power grid benchmark dataset for graph neural networks
Anna Varbella Kenza Amara Blazhe Gjorgiev Giovanni Sansavini
Public Graph Neural Networks (GNN) benchmark datasets facilitate the use of
GNN and enhance GNN applicability to diverse disciplines. The community
currently lacks public datasets of electrical power grids for GNN applications.
Indeed, GNNs can potentially capture complex power grid phenomena over
alternative machine learning techniques. Power grids are complex engineered
networks that are naturally amenable to graph representations. Therefore, GNN
have the potential for capturing the behavior of power grids over alternative
machine learning techniques. To this aim, we develop a graph dataset for
cascading failure events, which are the major cause of blackouts in electric
power grids. Historical blackout datasets are scarce and incomplete. The
assessment of vulnerability and the identification of critical components are
usually conducted via computationally expensive offline simulations of
cascading failures. Instead, we propose using machine learning models for the
online detection of cascading failures leveraging the knowledge of the system
state at the onset of the cascade. We develop PowerGraph, a graph dataset
modeling cascading failures in power grids, designed for two purposes, namely,
i) training GNN models for different graph-level tasks including multi-class
classification, binary classification, and regression, and ii) explaining GNN
models. The dataset generated via a physics-based cascading failure model
ensures the generality of the operating and environmental conditions by
spanning diverse failure scenarios. In addition, we foster the use of the
dataset to benchmark GNN explainability methods by assigning ground-truth
edge-level explanations. PowerGraph helps the development of better GNN models
for graph-level tasks and explainability, critical in many domains ranging from
chemistry to biology, where the systems and processes can be described as
graphs.