Data-Driven Graph Convolutional Neural Networks for Power System Contingency Analysis

We develop a graph convolutional neural network for power system contingency analysis. In contrast to other methods, the proposed architecture is purely data-driven and does not require knowledge of the power grid's underlying topology. Instead, the estimation of multiple correlation-based graphs enables a pinpoint exploitation of various power system intrinsic structures. The architecture is tested on two large real-world type power grids containing over 6000 approximated output variables. The evaluation shows that the proposed method requires only a fraction of the training parameters to still perform significantly better than the baseline methods, especially when only few training samples are available.