论文标题
用卷积神经网络热启动交流电流
Hot-Starting the Ac Power Flow with Convolutional Neural Networks
论文作者
论文摘要
获得良好的初始条件来解决基于牛顿 - 拉夫森（NR）的AC功率流（ACPF）问题可能是一项非常困难的任务。在本文中，我们提出了一个框架，以使用DC功率流（DCPF）结果和一个维度卷积神经网络（1D CNN），以减少基于NR的ACPF模型的初始总线电压幅度和相值。我们通过从负载需求的分布中进行采样以及计算每个样本的DCPF和ACPF结果来生成用于训练1D CNN的数据集。 IEEE 118-BUS和\ textSc {Pegase} 2869-BUS研究系统的实验表明，我们可以分别降低溶液时间的33.56 \％和30.06 \％\％，分别减少66.47％和49.52％的溶液迭代率。我们包括1D CNN体系结构和所使用的超参数，可以通过对此主题的未来研究来扩展。
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.
