Synthetic Active Distribution System Generation via Unbalanced Graph Generative Adversarial Network

by   Rong Yan, et al.

Real active distribution networks with associated smart meter (SM) data are critical for power researchers. However, it is practically difficult for researchers to obtain such comprehensive datasets from utilities due to privacy concerns. To bridge this gap, an implicit generative model with Wasserstein GAN objectives, namely unbalanced graph generative adversarial network (UG-GAN), is designed to generate synthetic three-phase unbalanced active distribution system connectivity. The basic idea is to learn the distribution of random walks both over a real-world system and across each phase of line segments, capturing the underlying local properties of an individual real-world distribution network and generating specific synthetic networks accordingly. Then, to create a comprehensive synthetic test case, a network correction and extension process is proposed to obtain time-series nodal demands and standard distribution grid components with realistic parameters, including distributed energy resources (DERs) and capacity banks. A Midwest distribution system with 1-year SM data has been utilized to validate the performance of our method. Case studies with several power applications demonstrate that synthetic active networks generated by the proposed framework can mimic almost all features of real-world networks while avoiding the disclosure of confidential information.


page 1

page 9


Synthetic Dynamic PMU Data Generation: A Generative Adversarial Network Approach

This paper concerns with the production of synthetic phasor measurement ...

Variational Autoencoder Generative Adversarial Network for Synthetic Data Generation in Smart Home

Data is the fuel of data science and machine learning techniques for sma...

Smart Home Energy Management: VAE-GAN synthetic dataset generator and Q-learning

Recent years have noticed an increasing interest among academia and indu...

FeederGAN: Synthetic Feeder Generation via Deep Graph Adversarial Nets

This paper presents a novel, automated, generative adversarial networks ...

Multi-MotifGAN (MMGAN): Motif-targeted Graph Generation and Prediction

Generative graph models create instances of graphs that mimic the proper...

TabFairGAN: Fair Tabular Data Generation with Generative Adversarial Networks

With the increasing reliance on automated decision making, the issue of ...

Please sign up or login with your details

Forgot password? Click here to reset