Data-driven short-term voltage stability assessment based on spatial-temporal graph convolutional network

Luo, Yi; Lü, Chao; Zhu, Lipeng; Song, Jie

doi:10.1016/j.ijepes.2020.106753

Cited by 52 publications

(23 citation statements)

References 17 publications

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“…Experiments were conducted on open-circuit fault datasets to evaluate the performance of the proposed method. Furthermore, a STGCNbased approach was proposed in [105] to investigate the shortterm voltage stability with consideration faults (i.e., context anomalies). Realistic experiments on Guangdong Power Grid were conducted to evaluate the proposed method.…”

Section: B Contextualmentioning

confidence: 99%

Graph Neural Networks for Anomaly Detection in Industrial Internet of Things

Dai

Tang

2022

IEEE Internet Things J.

112

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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: B Contextualmentioning

confidence: 99%

Graph Neural Networks for Anomaly Detection in Industrial Internet of Things

Dai

Tang

2022

IEEE Internet Things J.

112

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“…Reference [27] develops a temporal self-adaptive TSA system by using long short-term memory network (LSTM), which can learn the time dependence of the input temporal sequences. A spatial-temporal graph convolutional network is put forward for TSA of power systems in [28]. Reference [29] proposes a TSA and instability mode prediction model based on convolutional neural network (CNN).…”

Section: Mahalanobis Kernel Network Topologymentioning

confidence: 99%

A Critical Review of Data-Driven Transient Stability Assessment of Power Systems: Principles, Prospects and Challenges

Zhang

Zhu

2021

Energies

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Transient stability assessment (TSA) has always been a fundamental means for ensuring the secure and stable operation of power systems. Due to the integration of new elements such as power electronics, electric vehicles and renewable power generations, dynamic characteristics of power systems are becoming more and more complex, which makes TSA an increasingly urgent task. Since traditional time-domain simulations and direct method cannot meet the actual operation requirements of power systems, data-driven TSA has attracted growing attention from both academia and industry. This paper makes a comprehensive review from the following four aspects: feature extraction and selection, model construction, online learning and rule extraction; and then, summarizes the challenges and prospects for future research; finally, draws the conclusions of this review. This review will be beneficial for relevant researchers to better understand the research status, key technologies, and existing challenges in the field.

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“…References [43,44] set different fault occurrence situations in PSD-BPA. e modeling and simulations in [45][46][47] were carried out using PSD-BPA. By setting system load levels, dynamic load rates, and fault clearing time with different probabilities, the system operating status under various fault scenarios was simulated respectively, and then training data sets were obtained.…”

Section: Samples Acquisitionmentioning

confidence: 99%

“…is method could capture the evolution of multiple temporal and spatial trends of STVS and predict STVS results. Reference [46] proposed a spatio-temporal GCN to extract the spatio-temporal features dynamically presented after faults. Firstly, GCN was used to integrate network topology information into the learning model to utilize spatial information; then, a one-dimensional convolutional neural network (1D-CNN) was used to mine temporal correlations.…”

Section: Feature Extraction and Feature Selection Of Stvsamentioning

confidence: 99%

“…Reference [50] proposed an STVS online prediction method that combined graph convolutional networks (GCNs) and LSTM, which could capture multiple spatio-temporal STVS evolution trends and predict STVS states. Reference [46] proposed a model framework containing spatio-temporal information. Based on GCN and 1D-CNN, it successfully connected temporal data and topological information to construct a higherperformance classification model.…”

Section: Data-driven Stvsa Based On Deep Learningmentioning

confidence: 99%

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A Review of Data-Driven Short-Term Voltage Stability Assessment of Power Systems: Concept, Principle, and Challenges

Cao

2021

Mathematical Problems in Engineering

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With the rapid growth of power market reform and power demand, the power transmission capacity of a power grid is approaching its limit and the secure and stable operation of power systems becomes increasingly important. In particular, in modern power grids, the proportion of dynamic loads with fast recovery characteristics such as air conditioners, refrigerators, and industrial motors is increasing. As well as there is an increasing proportion of different forms of renewable energy in power systems. Therefore, the short-term voltage stability (STVS) of power systems cannot be ignored. This article comprehensively sorts out the STVS problems of power systems from the perspective of data-driven methods and discusses existing challenges.

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Data-driven short-term voltage stability assessment based on spatial-temporal graph convolutional network

Cited by 52 publications

References 17 publications

Graph Neural Networks for Anomaly Detection in Industrial Internet of Things

Graph Neural Networks for Anomaly Detection in Industrial Internet of Things

A Critical Review of Data-Driven Transient Stability Assessment of Power Systems: Principles, Prospects and Challenges

A Review of Data-Driven Short-Term Voltage Stability Assessment of Power Systems: Concept, Principle, and Challenges

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