Data-Driven Short-Term Voltage Stability Assessment Based on Spatial-Temporal Graph Convolutional Network

• URL: http://arxiv.org/abs/2103.03729v1
• Date: Fri, 5 Mar 2021 15:00:47 GMT
• Title: Data-Driven Short-Term Voltage Stability Assessment Based on Spatial-Temporal Graph Convolutional Network
• Authors: Yonghong Luo, Chao Lu, Lipeng Zhu, Jie Song
• Abstract summary: Post-fault dynamics of short-term voltage stability (SVS) present spatial-temporal characteristics. This paper develops a novel spatial-temporal graph convolutional network (STGCN) to address this problem. It can result in higher assessment accuracy, better robustness and adaptability than conventional methods.
• Score: 14.837629132539902
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Post-fault dynamics of short-term voltage stability (SVS) present spatial-temporal characteristics, but the existing data-driven methods for online SVS assessment fail to incorporate such characteristics into their models effectively. Confronted with this dilemma, this paper develops a novel spatial-temporal graph convolutional network (STGCN) to address this problem. The proposed STGCN utilizes graph convolution to integrate network topology information into the learning model to exploit spatial information. Then, it adopts one-dimensional convolution to exploit temporal information. In this way, it models the spatial-temporal characteristics of SVS with complete convolutional structures. After that, a node layer and a system layer are strategically designed in the STGCN for SVS assessment. The proposed STGCN incorporates the characteristics of SVS into the data-driven classification model. It can result in higher assessment accuracy, better robustness and adaptability than conventional methods. Besides, parameters in the system layer can provide valuable information about the influences of individual buses on SVS. Test results on the real-world Guangdong Power Grid in South China verify the effectiveness of the proposed network.

Related papers

• A Novel Spatiotemporal Coupling Graph Convolutional Network [0.18130068086063336]
  This paper presents a novel Graph Contemporalal Networks (GCNs)-based dynamic estimator namely Spatio Coupling GCN (SCG) model with the three-fold ideas as below. The results demonstrate that SCG realizes higher accuracy compared with the state-of-the-arts, illustrating it can learn powerful representations to users and cloud services.
  arXiv  Detail & Related papers  (2024-08-09T02:02:01Z)
• STG-Mamba: Spatial-Temporal Graph Learning via Selective State Space Model [11.211981320116323]
  We introduce Spatial-Temporal Graph Mamba (STG-Mamba) as the first exploration of leveraging the powerful selective state space models for STG learning. STG-Mamba treats STG Network as a system, and meticulously explore the STG system's dynamic state evolution across temporal dimension. It not only surpasses existing state-of-the-art methods in terms of STG forecasting performance, but also effectively alleviate the computational bottleneck of large-scale graph networks.
  arXiv  Detail & Related papers  (2024-03-19T04:02:57Z)
• ST-MLP: A Cascaded Spatio-Temporal Linear Framework with Channel-Independence Strategy for Traffic Forecasting [47.74479442786052]
  Current research on Spatio-Temporal Graph Neural Networks (STGNNs) often prioritizes complex designs, leading to computational burdens with only minor enhancements in accuracy. We propose ST-MLP, a concise cascaded temporal-temporal model solely based on Multi-Layer Perceptron (MLP) modules and linear layers. Empirical results demonstrate that ST-MLP outperforms state-of-the-art STGNNs and other models in terms of accuracy and computational efficiency.
  arXiv  Detail & Related papers  (2023-08-14T23:34:59Z)
• Towards Spatio-temporal Sea Surface Temperature Forecasting via Static and Dynamic Learnable Personalized Graph Convolution Network [9.189893653029076]
  This paper proposes a novel static and dynamic learnable personalized graph convolution network (SD-LPGC) Specifically, two graph learning layers are first constructed to respectively model the stable long-term and short-term evolutionary patterns hidden in the SST signals. Then, a learnable personalized convolution layer is designed to fuse this information.
  arXiv  Detail & Related papers  (2023-04-12T14:35:38Z)
• Space-Time Graph Neural Networks with Stochastic Graph Perturbations [100.31591011966603]
  Space-time graph neural networks (ST-GNNs) learn efficient graph representations of time-varying data. In this paper we revisit the properties of ST-GNNs and prove that they are stable to graph stabilitys. Our analysis suggests that ST-GNNs are suitable for transfer learning on time-varying graphs.
  arXiv  Detail & Related papers  (2022-10-28T16:59:51Z)
• Spatio-Temporal Graph Few-Shot Learning with Cross-City Knowledge Transfer [58.6106391721944]
  Cross-city knowledge has shown its promise, where the model learned from data-sufficient cities is leveraged to benefit the learning process of data-scarce cities. We propose a model-agnostic few-shot learning framework for S-temporal graph called ST-GFSL. We conduct comprehensive experiments on four traffic speed prediction benchmarks and the results demonstrate the effectiveness of ST-GFSL compared with state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-05-27T12:46:52Z)
• Space-Time Graph Neural Networks [104.55175325870195]
  We introduce space-time graph neural network (ST-GNN) to jointly process the underlying space-time topology of time-varying network data. Our analysis shows that small variations in the network topology and time evolution of a system does not significantly affect the performance of ST-GNNs.
  arXiv  Detail & Related papers  (2021-10-06T16:08:44Z)
• Spatio-Temporal Graph Scattering Transform [54.52797775999124]
  Graph neural networks may be impractical in some real-world scenarios due to a lack of sufficient high-quality training data. We put forth a novel mathematically designed framework to analyze-temporal data.
  arXiv  Detail & Related papers  (2020-12-06T19:49:55Z)
• Efficient and Stable Graph Scattering Transforms via Pruning [86.76336979318681]
  Graph scattering transforms ( GSTs) offer training-free deep GCN models that extract features from graph data. The price paid by GSTs is exponential complexity in space and time that increases with the number of layers. The present work addresses the complexity limitation of GSTs by introducing an efficient so-termed pruned (p) GST approach.
  arXiv  Detail & Related papers  (2020-01-27T16:05:56Z)

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.