Recurrent Graph Convolutional Network-Based Multi-Task Transient Stability Assessment Framework in Power System

Huang, Jiyu; Guan, Lin; Su, Yinsheng; Yao, Haicheng; Guo, Mengxuan; Zhong, Zhi

doi:10.1109/access.2020.2991263

Cited by 75 publications

(27 citation statements)

References 37 publications

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“…Although this study mainly focuses on deriving distribution characteristics of power flows, the main methodology can be further used for anomaly detection in the future. Moreover, the work [109] integrates GCN with long short-term memory (LSTM) network to construct the recurrent graph convolutional network (RGCN). Experiments on both IEEE 39 Bus and IEEE 300 Bus system verify the effectiveness of the proposed RGCN model for collective anomaly detection so as to ensure the stability of power grids.…”

Section: Collectivementioning

confidence: 99%

Graph Neural Networks for Anomaly Detection in Industrial Internet of Things

Dai

Tang

2022

IEEE Internet Things J.

132

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The Industrial Internet of Things (IIoT) plays an important role in digital transformation of traditional industries towards Industry 4.0. By connecting sensors, instruments and other industry devices to the Internet, IIoT facilitates the data collection, data analysis, and automated control, thereby improving the productivity and efficiency of the business as well as the resulting economic benefits. Due to the complex IIoT infrastructure, anomaly detection becomes an important tool to ensure the success of IIoT. Due to the nature of IIoT, graph-level anomaly detection has been a promising means to detect and predict anomalies in many different domains such as transportation, energy and factory, as well as for dynamically evolving networks. This paper provides a useful investigation on graph neural networks (GNN) for anomaly detection in IIoTenabled smart transportation, smart energy and smart factory. In addition to the GNN-empowered anomaly detection solutions on point, contextual, and collective types of anomalies, useful datasets, challenges and open issues for each type of anomalies in the three identified industry sectors (i.e., smart transportation, smart energy and smart factory) are also provided and discussed, which will be useful for future research in this area. To demonstrate the use of GNN in concrete scenarios, we show three case studies in smart transportation, smart energy, and smart factory, respectively.

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Section: Collectivementioning

confidence: 99%

Graph Neural Networks for Anomaly Detection in Industrial Internet of Things

Dai

Tang

2022

IEEE Internet Things J.

132

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“…Despite the model's universality potential to cope with other networked industrial control systems, it has been found that this hybrid design can not detect unknown attack/fault types [114]. A novel Graph Neural Network (GNN) based framework, combining graph convolutional network (GCN) and LSTM model has been proposed for multi-task multi-task transient stability classification [115]. The hybrid design of GNN is found able to effectively analyze complex spatio-temporal patterns in the IEEE 39 Bus system and IEEE 300 Bus system [115].…”

Section: H Hybrid Models-basedmentioning

confidence: 99%

“…A novel Graph Neural Network (GNN) based framework, combining graph convolutional network (GCN) and LSTM model has been proposed for multi-task multi-task transient stability classification [115]. The hybrid design of GNN is found able to effectively analyze complex spatio-temporal patterns in the IEEE 39 Bus system and IEEE 300 Bus system [115]. The fly in the ointment is that the GNN-based models require [116].…”

Section: H Hybrid Models-basedmentioning

confidence: 99%

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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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“…GT explores the relationship between the structural properties and the eigenvalues and eigenvectors of the corresponding matrices [11]. GT has been widely applied in various areas, including data analysis [11]- [13], communication [14], [15], traffic networks [16], [17], and energy networks [18], [19]. Typically, GT represents a network in a mathematical graph as G = G(V, E), where V denotes vertices (e.g., demand nodes, reservoirs, and tanks in a WDN) with n elements, and E represents edges (e.g., pipes, pumps, and valves in a WDN) with m elements [5].…”

Section: Introductionmentioning

confidence: 99%

Shortest-Path-Based Two-Phase Design Model for Hydraulically Efficient Water Distribution Network: Preparing for Extreme Changes in Water Availability

Lee

Jung

2021

IEEE Access

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Environmental issues can cause changes in source water availability in water distribution networks (WDNs). Thus, an efficient connection between the source and consumers is important for securing water serviceability, which can generally be achieved by minimizing energy losses. In this study, a novel two-phase design (TPD) model is proposed to design an energy-efficient WDN by maximizing a hydraulic geodesic index (HGI), which is the weighted shortest path from the source to the demand node. Before applying the TPD model for WDN design, a correlation analysis between the system HGI, hydraulic performance, and graph theory indices is conducted using 33 J-City networks to verify the proposed HGI. Next, the TPD model is used to determine the optimal layout of the grid network (Phase I). Based on this layout, the optimal diameter set is identified in Phase II. The TPD is thereafter compared with the traditional single-phase design (SPD) model, which determines the optimal layout and diameter simultaneously, and a least-cost model for each phase in the grid network layout and pipe-sizing problem. The correlation analysis clearly indicates that the system HGI with the weighted graph theory successfully determines the hydraulic performance without any hydraulic analysis. Furthermore, TPD is advantageous for designing energy-efficient, hydraulically and structurally sustainable, and resilient networks, as compared to SPD and the least-cost model. The TPD model is expected to provide a better opportunity to prepare for extreme water availability changes by enhancing the hydraulic performance and efficiency through a better connection between the source and nodes.

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Recurrent Graph Convolutional Network-Based Multi-Task Transient Stability Assessment Framework in Power System

Cited by 75 publications

References 37 publications

Graph Neural Networks for Anomaly Detection in Industrial Internet of Things

Graph Neural Networks for Anomaly Detection in Industrial Internet of Things

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

Shortest-Path-Based Two-Phase Design Model for Hydraulically Efficient Water Distribution Network: Preparing for Extreme Changes in Water Availability

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