Leveraging power grid topology in machine learning assisted optimal power flow. (arXiv:2110.00306v3 [cs.LG] UPDATED)

Machine learning assisted optimal power flow (OPF) aims to reduce the
computational complexity of these non-linear and non-convex constrained
optimization problems by consigning expensive (online) optimization to offline
training. The majority of work in this area typically employs fully connected
neural networks (FCNN). However, recently convolutional (CNN) and graph (GNN)
neural networks have also been investigated, in effort to exploit topological
information within the power grid. Although promising results have been
obtained, there lacks a systematic comparison between these architectures …

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