基于卷积神经网络的热起动潮流计算
Hot-Starting the Ac Power Flow with Convolutional Neural Networks
摘要：
获得良好的初始条件来求解基于Newton-Raphson（NR）的交流潮流（ACPF）问题是一项非常困难的任务。在本文中，我们提出了一个框架，以获得初始总线电压幅度和相位值，减少解决方案的迭代和时间为NR的ACPF模型，使用直流潮流（DCPF）的结果和一维卷积神经网络（1D CNN）。我们通过从负载需求分布中采样，并通过计算每个样本的DCPF和ACPF结果，生成用于训练1D CNN的数据集。在IEEE 118节点和\textsc{Pegase} 2869节点系统上的实验表明，该算法分别减少了33.56%和30.06%的求解时间，减少了66.47%和49.52%的求解迭代次数。我们包括1D CNN架构和使用的超参数，这可以通过对这个主题的未来研究来扩展。
英文摘要：
Obtaining good initial conditions to solve the Newton-Raphson (NR) based ac power flow (ACPF) problem can be a very difficult task. In this paper, we propose a framework to obtain the initial bus voltage magnitude and phase values that decrease the solution iterations and time for the NR based ACPF model, using the dc power flow (DCPF) results and one dimensional convolutional neural networks (1D CNNs). We generate the dataset used to train the 1D CNNs by sampling from a distribution of load demands, and by computing the DCPF and ACPF results for each sample. Experiments on the IEEE 118-bus and \textsc{Pegase} 2869-bus study systems show that we can achieve 33.56\% and 30.06\% reduction in solution time, and 66.47% and 49.52% reduction in solution iterations per case, respectively. We include the 1D CNN architectures and the hyperparameters used, which can be expanded on by the future studies on this topic.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
