  Recently, there has been a surge of interest in adopting deep neural networks
(DNNs) for solving the optimal power flow (OPF) problem in power systems.
Computing optimal generation dispatch decisions using a trained DNN takes
significantly less time when compared to using conventional optimization
solvers. However, a major drawback of existing work is that the machine
learning models are trained for a specific system topology. Hence, the DNN
predictions are only useful as long as the system topology remains unchanged.
Changes to the system topology (initiated by the system operator) would require
retraining the DNN, which incurs significant training overhead and requires an
extensive amount of training data (corresponding to the new system topology).
To overcome this drawback, we propose a DNN-based OPF predictor that is trained
using a meta-learning (MTL) approach. The key idea behind this approach is to
find a common initialization vector that enables fast training for any system
topology. The developed OPF-predictor is validated through simulations using
benchmark IEEE bus systems. The results show that the MTL approach achieves
significant training speeds-ups and requires only a few gradient steps with a
few data samples to achieve high OPF prediction accuracy.
