Deep-Learning-Aided Voltage-Stability-Enhancing Stochastic Distribution Network Reconfiguration

TL;DR

This paper introduces a deep learning approach to optimize distribution network reconfiguration for improved voltage stability under uncertain renewable generation and load conditions, achieving efficient and effective network performance.

Contribution

It presents a novel convolutional neural network model integrated into reconfiguration algorithms to enhance voltage stability and reduce power loss in distribution networks.

Findings

Improved voltage stability through SDNR demonstrated on IEEE models.

Deep learning method reduces computational time significantly.

Reconfigured networks show reduced power losses.

Abstract

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.