论文标题
深度学习辅助电压稳定性增强随机分配网络重新配置
Deep-Learning-Aided Voltage-Stability-Enhancing Stochastic Distribution Network Reconfiguration
论文作者
论文摘要
由于严重的电压违规行为以及由于可再生后代和动态载荷增加而导致的反应性功率储量不足，电源分配网络正在接近其电压稳定性边界。为了解决这一问题的广泛努力，我们专注于通过随机分配网络重新配置（SDNR）增强电压稳定性，从而优化了不确定的世代和负载下分布网络的（径向）拓扑。我们提出了一种深度学习方法来解决这个计算具有挑战性的问题。具体而言，我们构建了一个卷积神经网络模型，以预测SDNR决策中相关的电压稳定性指数。然后，我们将该预测模型集成到连续的分支还原算法中，以重新配置具有优化性能的径向网络，以降低功率降低和电压稳定性增强。两个IEEE网络模型的数值结果验证了通过SDNR增强电压稳定性以及所提出方法的计算效率的重要性。
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.
