  Power distribution networks are approaching their voltage stability
boundaries due to the severe voltage violations and the inadequate reactive
power reserves caused by the increasing renewable generations and dynamic
loads. In the broad endeavor to resolve this concern, we focus on enhancing
voltage stability through stochastic distribution network reconfiguration
(SDNR), which optimizes the (radial) topology of a distribution network under
uncertain generations and loads. We propose a deep learning method to solve
this computationally challenging problem. Specifically, we build a
convolutional neural network model to predict the relevant voltage stability
index from the SDNR decisions. Then we integrate this prediction model into
successive branch reduction algorithms to reconfigure a radial network with
optimized performance in terms of power loss reduction and voltage stability
enhancement. Numerical results on two IEEE network models verify the
significance of enhancing voltage stability through SDNR and the computational
efficiency of the proposed method.
