Power Failure Cascade Prediction using Graph Neural Networks

TL;DR

This paper introduces a graph neural network model for predicting power failure cascades in electrical grids, outperforming influence models and significantly reducing computational time.

Contribution

The paper presents a novel flow-free graph neural network approach for cascade prediction that is more accurate and efficient than existing influence models.

Findings

Outperforms influence models in predicting cascade size and timing.

Reduces computational time by nearly 100 times.

Effective across various power injection scenarios.

Abstract

We consider the problem of predicting power failure cascades due to branch failures. We propose a flow-free model based on graph neural networks that predicts grid states at every generation of a cascade process given an initial contingency and power injection values. We train the proposed model using a cascade sequence data pool generated from simulations. We then evaluate our model at various levels of granularity. We present several error metrics that gauge the model's ability to predict the failure size, the final grid state, and the failure time steps of each branch within the cascade. We benchmark the graph neural network model against influence models. We show that, in addition to being generic over randomly scaled power injection values, the graph neural network model outperforms multiple influence models that are built specifically for their corresponding loading profiles. Finally, we show that the proposed model reduces the computational time by almost two orders of magnitude.