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Fast Transient Stability Prediction Using Grid-informed Temporal and Topological Embedding Deep Neural Network

arXiv 2201.09245 ↗First posted 23 Jan 2022eess.SY cs.LG cs.SYcited by 0

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Abstract (arXiv)

Transient stability prediction is critically essential to the fast online assessment and maintaining the stable operation in power systems. The wide deployment of phasor measurement units (PMUs) promotes the development of data-driven approaches for transient stability assessment. This paper proposes the temporal and topological embedding deep neural network (TTEDNN) model to forecast transient stability with the early transient dynamics. The TTEDNN model can accurately and efficiently predict the transient stability by extracting the temporal and topological features from the time-series data of the early transient dynamics. The grid-informed adjacency matrix is used to incorporate the power grid structural and electrical parameter information. The transient dynamics simulation environments under the single-node and multiple-node perturbations are used to test the performance of the TTEDNN model for the IEEE 39-bus and IEEE 118-bus power systems. The results show that the TTEDNN model has the best and most robust prediction performance. Furthermore, the TTEDNN model also demonstrates the transfer capability to predict the transient stability in the more complicated transient dynamics simulation environments.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 22 of its 75 references that are in the map. First posted on arXiv 23 Jan 2022.

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22 of 75 references.

References · 22

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  3. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  4. 2017Graph Attention Networkscited by 8,854
  5. 2016Layer Normalizationcited by 5,579
  6. 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,845
  7. 2019A Comprehensive Survey on Graph Neural Networkscited by 2,906
  8. 2018CBAM: Convolutional Block Attention Modulecited by 2,463
  9. 2016WaveNet: A Generative Model for Raw Audiocited by 1,710
  10. 2016Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translationcited by 1,471
  11. 2018An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modelingcited by 1,447
  12. 2018Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognitioncited by 1,199
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  1. 2017Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecastingcited by 1,125
  2. 2017Deep Interest Network for Click-Through Rate Predictioncited by 541
  3. 2021A Review of Graph Neural Networks and Their Applications in Power Systemscited by 62
  4. 2018Real-time Power System State Estimation and Forecasting via Deep Neural Networkscited by 32
  5. 2016Intelligent Time-Adaptive Transient Stability Assessment Systemcited by 12
  6. 2021Power-grid stability predictions using transferable machine learningcited by 6
  7. 2014Lyapunov Functions Family Approach to Transient Stability Assessmentcited by 4
  8. 2015A Framework for Robust Assessment of Power Grid Stability and Resiliencycited by 4
  9. 2021A critical review of data-driven transient stability assessment of power systems: principles, prospects and challengescited by 4
  10. 2021Imbalanced Sample Generation and Evaluation for Power System Transient Stability Using CTGANcited by 4

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