FeederGAN: Synthetic Feeder Generation via Deep Graph Adversarial Nets

Liang, Ming; Meng, Yao; Wang, Jiyu; Lubkeman, David; Liu, Ning

doi:10.1109/tsg.2020.3025259

Cited by 15 publications

(6 citation statements)

References 14 publications

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“…In ref. 37 , the authors use generative adversarial networks (GANs) to create synthetic power networks. However, the approach is inherently data intensive and requires a large number of samples for training, making it practically challenging to use.…”

Section: Related Workmentioning

confidence: 99%

Ensembles of realistic power distribution networks

Meyur

Vullikanti

Swarup

et al. 2022

Proc. Natl. Acad. Sci. U.S.A.

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The power grid is going through significant changes with the introduction of renewable energy sources and the incorporation of smart grid technologies. These rapid advancements necessitate new models and analyses to keep up with the various emergent phenomena they induce. A major prerequisite of such work is the acquisition of well-constructed and accurate network datasets for the power grid infrastructure. In this paper, we propose a robust, scalable framework to synthesize power distribution networks that resemble their physical counterparts for a given region. We use openly available information about interdependent road and building infrastructures to construct the networks. In contrast to prior work based on network statistics, we incorporate engineering and economic constraints to create the networks. Additionally, we provide a framework to create ensembles of power distribution networks to generate multiple possible instances of the network for a given region. The comprehensive dataset consists of nodes with attributes, such as geocoordinates; type of node (residence, transformer, or substation); and edges with attributes, such as geometry, type of line (feeder lines, primary or secondary), and line parameters. For validation, we provide detailed comparisons of the generated networks with actual distribution networks. The generated datasets represent realistic test systems (as compared with standard test cases published by Institute of Electrical and Electronics Engineers (IEEE)) that can be used by network scientists to analyze complex events in power grids and to perform detailed sensitivity and statistical analyses over ensembles of networks.

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Section: Related Workmentioning

confidence: 99%

Ensembles of realistic power distribution networks

Meyur

Vullikanti

Swarup

et al. 2022

Proc. Natl. Acad. Sci. U.S.A.

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“…The proposed model proved that FLbased solutions can trade off between model performance and privacy concerns. Reference [141] proposed a GAN-based synthetic feeder generation mechanism to ingest power system distribution feeder models using a device-as-node representation. The disadvantage is that this model does not reduce the dimension of the data, so the computational burden of the proposed architecture is relatively high [141].…”

Section: B Federated Learningmentioning

confidence: 99%

“…Reference [141] proposed a GAN-based synthetic feeder generation mechanism to ingest power system distribution feeder models using a device-as-node representation. The disadvantage is that this model does not reduce the dimension of the data, so the computational burden of the proposed architecture is relatively high [141]. From these studies, the challenging points can be summarized in two major points: 1) The limited bandwidth of wireless communication of the current IoT devices.…”

Section: B Federated Learningmentioning

confidence: 99%

Deep Learning in Smart Grid Technology: A Review of Recent Advancements and Future Prospects

et al. 2021

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The current electric power system witnesses a significant transition into Smart Grids (SG) as a promising landscape for high grid reliability and efficient energy management. This ongoing transition undergoes rapid changes, requiring a plethora of advanced methodologies to process the big data generated by various units. In this context, SG stands tied very closely to Deep Learning (DL) as an emerging technology for creating a more decentralized and intelligent energy paradigm while integrating high intelligence in supervisory and operational decision-making. Motivated by the outstanding success of DL-based prediction methods, this article attempts to provide a thorough review from a broad perspective on the state-of-the-art advances of DL in SG systems. Firstly, a bibliometric analysis has been conducted to categorize this review's methodology. Further, we taxonomically delve into the mechanism behind some of the trending DL algorithms. We then showcase the DL enabling technologies in SG, such as federated learning, edge intelligence, and distributed computing. Finally, challenges and research frontiers are provided to serve as guidelines for future work in the futuristic power grid domain. This study's core objective is to foster the synergy between these two fields for decision-makers and researchers to accelerate DL's practical deployment for SG systems. INDEX TERMSSmart grid, deep learning, deep neural networks, edge computing, distributed and federated learning, power systems. NOMENCLATURE Abbreviations DDL Distributed deep learning DL Deep learning DRL Deep reinforcement learning DRN Deep residual network EI Edge intelligence EPS Electric power systems FL Federated learning IoT Internet of things LSTM Long short-term memory neural network The associate editor coordinating the review of this manuscript and approving it for publication was Shadi Alawneh . NN Neural network PVPF Photovoltaic power forecasting D. RESEARCH METHODOLOGY AND SYSTEMATIC REVIEW PROTOCOLStarting from September 2019, the multiple-methods approach was conducted [24]. The collection of the mainstream research papers on SG/AI from Web of Science (WoS), Scopus, IEEE Xplore, Science Direct, and Google scholar was conducted as the largest databases of peerreviewed articles. Only peer-reviewed articles written in English, providing experimental results, and having a unique identifier from the mentioned databases were taken into consideration, including reviews, research articles, patent reports, and conference proceedings. The adopted methodology for conducting this review article employs a combination of keywords categorized into three main groups, specifically, 'Deep Learning', 'Smart Grid', and 'Prediction'. The search methodology focuses on the recent research articles from 2015-2020 to identify the comprehensive statues of the AI applications on SG. The filtering process results in 220 research papers from 600 related papers selected based on their relevance by reading the title, abstract, conclusion,

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“…A recent publication applied the Wasserstein algorithm on graph convolutional neural networks to generate synthetic distribution networks. FeederGAN [125] encoded various component attributes, including device length, conductor capacity, distance from the source, tree level, phasing, and load value. Geographical information of feeders was removed to simplify the modeled graph structures.…”

Section: B Generative Adversarial Approachesmentioning

confidence: 99%

“…Expert design was excluded from Table 4 due to its manual approach. Only two references applied deep graph generation: [131] for transmission and [125] for distribution systems. This research gap suggests deep graph learning could be explored and implemented in the coming years, especially as it continuously learns through examples based on a data-driven process (refer to Section VI).…”

Section: A Comparative Summary Of Literaturementioning

confidence: 99%

Synthetic Benchmarks for Power Systems

Mohammadi

Saleh

2021

IEEE Access

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Power system benchmarks are transmission and distribution networks used to evaluate novel control algorithms and simulate grid evolution scenarios. These benchmarks range in size, system characteristics, and use cases. Although active working groups have created and published many benchmarks, these networks are not all representative of a given region and may not consider certain aspects such as increased penetration levels of distributed energy resources. To address these issues, synthetic benchmark networks and methodologies for generating them have been developed by various research groups. This paper provides a comprehensive survey of procedures commonly used to generate synthetic networks and a detailed account of the various metrics used to define and validate benchmarks. Existing models are categorized into different approaches, including expert design, anonymized clustering, statistical sampling, and heuristic algorithms. Deep graph generation based techniques are also presented and recommended for the network generation problem. A comparative summary is provided to highlight the different existing works in this area and reveal research gaps, along with a list of published datasets and their characteristics.

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FeederGAN: Synthetic Feeder Generation via Deep Graph Adversarial Nets

Cited by 15 publications

References 14 publications

Ensembles of realistic power distribution networks

Ensembles of realistic power distribution networks

Deep Learning in Smart Grid Technology: A Review of Recent Advancements and Future Prospects

Synthetic Benchmarks for Power Systems

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