Synthetic Active Distribution System Generation via Unbalanced Graph Generative Adversarial Network

• URL: http://arxiv.org/abs/2108.00599v1
• Date: Mon, 2 Aug 2021 02:17:01 GMT
• Title: Synthetic Active Distribution System Generation via Unbalanced Graph Generative Adversarial Network
• Authors: Rong Yan, Yuxuan Yuan, Zhaoyu Wang, Guangchao Geng, Quanyuan Jiang
• Abstract summary: An implicit generative model with Wasserstein GAN objectives 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. 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.
• Score: 2.2749157557381245
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• TDNetGen: Empowering Complex Network Resilience Prediction with Generative Augmentation of Topology and Dynamics [14.25304439234864]
  We introduce a novel resilience prediction framework for complex networks, designed to tackle this issue through generative data augmentation of network topology and dynamics. Experiment results on three network datasets demonstrate that our proposed framework TDNetGen can achieve high prediction accuracy up to 85%-95%.
  arXiv  Detail & Related papers  (2024-08-19T09:20:31Z)
• Generative Modeling for Tabular Data via Penalized Optimal Transport Network [2.0319002824093015]
  Wasserstein generative adversarial network (WGAN) is a notable improvement in generative modeling. We propose POTNet, a generative deep neural network based on a novel, robust, and interpretable marginally-penalized Wasserstein (MPW) loss.
  arXiv  Detail & Related papers  (2024-02-16T05:27:05Z)
• Fitting Low-rank Models on Egocentrically Sampled Partial Networks [4.111899441919165]
  We propose an approach to fit general low-rank models for egocentrically sampled networks. This method offers the first theoretical guarantee for egocentric partial network estimation. We evaluate the technique on several synthetic and real-world networks and show that it delivers competitive performance in link prediction tasks.
  arXiv  Detail & Related papers  (2023-03-09T03:20:44Z)
• Distributed Traffic Synthesis and Classification in Edge Networks: A Federated Self-supervised Learning Approach [83.2160310392168]
  This paper proposes FS-GAN to support automatic traffic analysis and synthesis over a large number of heterogeneous datasets. FS-GAN is composed of multiple distributed Generative Adversarial Networks (GANs) FS-GAN can classify data of unknown types of service and create synthetic samples that capture the traffic distribution of the unknown types.
  arXiv  Detail & Related papers  (2023-02-01T03:23:11Z)
• Evaluating Distribution System Reliability with Hyperstructures Graph Convolutional Nets [74.51865676466056]
  We show how graph convolutional networks and hyperstructures representation learning framework can be employed for accurate, reliable, and computationally efficient distribution grid planning. Our numerical experiments show that the proposed Hyper-GCNNs approach yields substantial gains in computational efficiency.
  arXiv  Detail & Related papers  (2022-11-14T01:29:09Z)
• Learning Autonomy in Management of Wireless Random Networks [102.02142856863563]
  This paper presents a machine learning strategy that tackles a distributed optimization task in a wireless network with an arbitrary number of randomly interconnected nodes. We develop a flexible deep neural network formalism termed distributed message-passing neural network (DMPNN) with forward and backward computations independent of the network topology.
  arXiv  Detail & Related papers  (2021-06-15T09:03:28Z)
• Multi-Source Data Fusion Outage Location in Distribution Systems via Probabilistic Graph Models [1.7205106391379026]
  We propose a multi-source data fusion approach to locate outage events in partially observable distribution systems. A novel aspect of the proposed approach is that it takes multi-source evidence and the complex structure of distribution systems into account. Our method can radically reduce the computational complexity of outage location inference in high-dimensional spaces.
  arXiv  Detail & Related papers  (2020-12-04T22:34:20Z)
• GANs with Conditional Independence Graphs: On Subadditivity of Probability Divergences [70.30467057209405]
  Generative Adversarial Networks (GANs) are modern methods to learn the underlying distribution of a data set. GANs are designed in a model-free fashion where no additional information about the underlying distribution is available. We propose a principled design of a model-based GAN that uses a set of simple discriminators on the neighborhoods of the Bayes-net/MRF.
  arXiv  Detail & Related papers  (2020-03-02T04:31:22Z)
• Neural Network Approximation of Graph Fourier Transforms for Sparse Sampling of Networked Flow Dynamics [13.538871180763156]
  Infrastructure monitoring is critical for safe operations and sustainability. Water distribution networks (WDNs) are large-scale networked critical systems with complex cascade dynamics which are difficult to predict. Existing approaches use multi-objective optimisation to find the minimum set of essential monitoring points, but lack performance guarantees and a theoretical framework. Here, we first develop Graph Fourier Transform (GFT) operators to compress networked contamination spreading dynamics to identify the essential principle data collection points with inference performance guarantees. We then build autoencoder (AE) inspired neural networks (NN) to generalize the GFT sampling process and under-sample further from the initial
  arXiv  Detail & Related papers  (2020-02-11T20:18:37Z)
• Distribution Approximation and Statistical Estimation Guarantees of Generative Adversarial Networks [82.61546580149427]
  Generative Adversarial Networks (GANs) have achieved a great success in unsupervised learning. This paper provides approximation and statistical guarantees of GANs for the estimation of data distributions with densities in a H"older space.
  arXiv  Detail & Related papers  (2020-02-10T16:47:57Z)
• Brainstorming Generative Adversarial Networks (BGANs): Towards Multi-Agent Generative Models with Distributed Private Datasets [70.62568022925971]
  generative adversarial networks (GANs) must be fed by large datasets that adequately represent the data space. In many scenarios, the available datasets may be limited and distributed across multiple agents, each of which is seeking to learn the distribution of the data on its own. In this paper, a novel brainstorming GAN (BGAN) architecture is proposed using which multiple agents can generate real-like data samples while operating in a fully distributed manner.
  arXiv  Detail & Related papers  (2020-02-02T02:58:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.