  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.
