Distribution Network Reconfiguration for Short-Term Voltage Stability Enhancement: An Efficient Deep Learning Approach

Abstract

The rapid growth of renewables and dynamic loads has highlighted the short-term voltage stability (STVS) issue in distribution network operation. With the advances in metering, communication and control technologies, deep learning-based distribution network reconfiguration (DNR) becomes available to maintain secure and economic system operation. However, STVS is traditionally evaluated for a specific network topology using time-domain simulations, which cannot be expressed explicitly as a function for optimization, making it intractable to consider STVS in DNR. In this paper, an efficient deep learning-based method is proposed to address this issue. An STVS evaluation network is customized from deep convolution neural networks (CNNs) and trained to learn the relationship between network topology and STVS performance from historical data. To find the optimal topology, a well-trained evaluation network is applied in DNR, where STVS with various network topologies is evaluated without resorting to time-domain simulations. Then, the number of candidate topologies is significantly reduced by a threshold of STVS performance, which enables the direct solution of the DNR for STVS enhancement. The application of the proposed method in large-scale systems is also discussed via integration with heuristic algorithms. Case studies of a modified 69-bus distribution system and a real large-scale distribution system validate the necessity of considering STVS enhancement in DNR and the effectiveness of the proposed solution method.