Probabilistic Power Flow Solution with Graph Convolutional Network

Wang, Dawei; Zheng, Kedi; Chen, Qixin; Luo, Gang; Zhang, Xuan

doi:10.1109/isgt-europe47291.2020.9248786

Cited by 32 publications

(15 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The GCN model adopted in this paper is essentially obtained from [107]. Another similar work [108] investigates the adoption of GCNs for power flow calculation based on IEEE Case 69 data. 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.…”

Section: Collectivementioning

confidence: 99%

Graph Neural Networks for Anomaly Detection in Industrial Internet of Things

Dai

Tang

2022

IEEE Internet Things J.

114

View full text Add to dashboard Cite

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.

show abstract

Section: Collectivementioning

confidence: 99%

Graph Neural Networks for Anomaly Detection in Industrial Internet of Things

Dai

Tang

2022

IEEE Internet Things J.

114

View full text Add to dashboard Cite

show abstract

“…Graph convolutional networks (GCNs) [124] learn the features of the given data, which are represented as a graph, by inspecting its neighboring nodes. In the literature, there are significant efforts [125,126] that consider system topology to represent the power grid as a graph in order to apply a GCN architecture. More specifically, DL models should be implemented in the future in order to:…”

Section: Deep Learning (Dl)mentioning

confidence: 99%

Classifying resilience approaches for protecting smart grids against cyber threats

Syrmakesis

Alcaraz

Hatziargyriou

2022

Int. J. Inf. Secur.

View full text Add to dashboard Cite

Smart grids (SG) draw the attention of cyber attackers due to their vulnerabilities, which are caused by the usage of heterogeneous communication technologies and their distributed nature. While preventing or detecting cyber attacks is a well-studied field of research, making SG more resilient against such threats is a challenging task. This paper provides a classification of the proposed cyber resilience methods against cyber attacks for SG. This classification includes a set of studies that propose cyber-resilient approaches to protect SG and related cyber-physical systems against unforeseen anomalies or deliberate attacks. Each study is briefly analyzed and is associated with the proper cyber resilience technique which is given by the National Institute of Standards and Technology in the Special Publication 800-160. These techniques are also linked to the different states of the typical resilience curve. Consequently, this paper highlights the most critical challenges for achieving cyber resilience, reveals significant cyber resilience aspects that have not been sufficiently considered yet and, finally, proposes scientific areas that should be further researched in order to enhance the cyber resilience of SG.

show abstract

“…Related work on power grid property prediction Since power grids have an underlying graph structure, the recent development of graph representation learning [Bronstein et al, 2021, Hamilton, 2020 makes it possible to use machine learning for analyzing power grids. There are a number of applications using Graph Neural Networks (GNNs) for different power flow-related tasks [Donon et al, 2019, Kim et al, 2019, Bolz et al, 2019, Retiére et al, 2020, Wang et al, 2020, Owerko et al, 2020, Misyris et al, 2020, Liu et al, 2021 and to predict transient dynamics in microgrids Yu et al [2022]. In [Nauck et al, 2022] small GNNs are used to predict the dynamic stability on small datasets.…”

Section: Introductionmentioning

confidence: 99%

Predicting basin stability of power grids using graph neural networks

et al. 2022

View full text Add to dashboard Cite

The prediction of dynamical stability of power grids becomes more important and challenging with increasing shares of renewable energy sources due to their decentralized structure, reduced inertia and volatility. We investigate the feasibility of applying graph neural networks (GNN) to predict dynamic stability of synchronisation in complex power grids using the single-node basin stability (SNBS) as a measure. To do so, we generate two synthetic datasets for grids with 20 and 100 nodes respectively and estimate SNBS using Monte-Carlo sampling. Those datasets are used to train and evaluate the performance of eight different GNN-models. All models use the full graph without simplifications as input and predict SNBS in a nodal-regression-setup. We show that SNBS can be predicted in general and the performance significantly changes using different GNN-models. Furthermore, we observe interesting transfer capabilities of our approach: GNN-models trained on smaller grids can directly be applied on larger grids without the need of retraining.

show abstract

Probabilistic Power Flow Solution with Graph Convolutional Network

Cited by 32 publications

References 15 publications

Graph Neural Networks for Anomaly Detection in Industrial Internet of Things

Graph Neural Networks for Anomaly Detection in Industrial Internet of Things

Classifying resilience approaches for protecting smart grids against cyber threats

Predicting basin stability of power grids using graph neural networks

Contact Info

Product

Resources

About