FeederGAN: Synthetic Feeder Generation via Deep Graph Adversarial Nets

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). From power system feeder model input files, device connectivity is mapped to the adjacency matrix while 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 to the attribute matrix. Then, Wasserstein distance is used to optimize the GAN and GCN is used to discriminate the generated graph from the actual. A greedy method based on graph theory is developed to reconstruct the feeder from the generated adjacency and attribute matrix. Our results show that the generated feeders resemble the actual feeder in both topology and attributes verified by visual inspection and by empirical statistics obtained from actual distribution feeders.