This paper presents a novel, automated, generative adversarial networks (GAN) based synthetic feeder generation mechanism, abbreviated as FeederGAN. FeederGAN digests real feeder models represented by directed graphs via a deep learning framework powered by GAN and graph convolutional networks (GCN). Information of a distribution feeder circuit is extracted from its model input files so that the device connectivity is mapped onto the adjacency matrix and the device characteristics, such as circuit types (i.e., 3-phase, 2-phase, and 1-phase) and component attributes (e.g., length and current ratings), are mapped onto the attribute matrix. Then, Wasserstein distance is used to optimize the GAN and GCN is used to discriminate the generated graphs from the actual ones. A greedy method based on graph theory is developed to reconstruct the feeder using the generated adjacency and attribute matrices. Our results show that the GAN generated feeders resemble the actual feeder in both topology and attributes verified by visual inspection and by empirical statistics obtained from actual distribution feeders.

中文翻译：

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FeederGAN：通过深度图对抗网络生成合成馈线

本文提出了一种新颖的、自动化的、生成对抗网络 (GAN) 基于合成馈线生成机制，缩写为 FeederGAN。FeederGAN 通过由 GAN 和图卷积网络 (GCN) 提供支持的深度学习框架，消化由有向图表示的真实馈线模型。从模型输入文件中提取配电馈线电路的信息，以便将设备连接映射到邻接矩阵和设备特性，例如电路类型（即 3 相、2 相和 1 相）和组件属性（例如，长度和电流等级）被映射到属性矩阵上。然后，Wasserstein 距离用于优化 GAN，GCN 用于区分生成的图和实际的图。开发了一种基于图论的贪婪方法，使用生成的邻接矩阵和属性矩阵重建馈线。我们的结果表明，GAN 生成的馈线在拓扑和属性方面类似于实际馈线，这些属性通过视觉检查和从实际分布馈线获得的经验统计数据进行验证。