Deep-Learning-Aided Voltage-Stability-Enhancing Stochastic Distribution Network Reconfiguration
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AbstractPower 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.
All Author(s) ListWanjun Huang, Changhong Zhao
Journal nameIEEE Transactions on Power Systems
Year2024
Month3
Volume Number39
Issue Number2
PublisherIEEE
Pages2827 - 2836
ISSN0885-8950
eISSN1558-0679
LanguagesEnglish-United States

Last updated on 2024-16-10 at 14:12