Power Flow Balancing with Decentralized Graph Neural Networks

Abstract

We propose an end-to-end framework based on a Graph Neural Network (GNN) to balance the power flows in a generic grid. The optimization is framed as a supervised vertex regression task, where the GNN is trained to predict the current and power injections at each grid branch that yield a power flow balance. By representing the power grid as a line graph with branches as vertices, we can train a GNN that is more accurate and robust to changes in the underlying topology. In addition, by using specialized GNN layers, we are able to build a very deep architecture that accounts for large neighborhoods on the graph, while implementing only localized operations. We perform three different experiments to evaluate: i) the benefits of using localized rather than global operations and the tendency to oversmooth when using deep GNN models; ii) the resilience to perturbations in the graph topology; and iii) the capability to train the model simultaneously on multiple grid topologies and the consequential improvement in generalization to new, unseen grids. The proposed framework is efficient and, compared to other solvers based on deep learning, is robust to perturbations not only to the physical quantities on the grid components, but also to the topology.

Reidentification of Objects From Aerial Photos With Hybrid Siamese Neural Networks

“…2) Electric Pole Maintenance: Maintenance and control of energy grids (and their assets) has become a widespread problem over the last decades due to the aging of components, the heterogeneity of the grids (e.g., multiple types of renewable sources), and the interlocking of the grids inside smart cities [15]. In energy grids, deep learning has found widespread use, ranging from optimal flow analysis [16] to anomaly detection [17] and energy forecasting [18]. Concerning electric poles in particular, a lot of attention has gone into ways of mapping them periodically, including manned [1] and unmanned [2] aerial flights, and remote sensing pipelines [3] (we refer to [2] for a more in-depth overview).…”