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

arXiv: https://arxiv.org/abs/2201.09245 · HTML: https://mlsanity.com/paper/2201.09245/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 75 references that are in the map.
- First posted on arXiv 23 Jan 2022.

## Builds on (most cited first, 22 of 22)

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- [A Review of Graph Neural Networks and Their Applications in Power Systems](https://mlsanity.com/paper/2101.10025/) (2021)
- [Real-time Power System State Estimation and Forecasting via Deep Neural Networks](https://mlsanity.com/paper/1811.06146/) (2018)
- [Intelligent Time-Adaptive Transient Stability Assessment System](https://mlsanity.com/paper/1610.08235/) (2016)
- [Power-grid stability predictions using transferable machine learning](https://mlsanity.com/paper/2105.07562/) (2021)
- [Lyapunov Functions Family Approach to Transient Stability Assessment](https://mlsanity.com/paper/1409.1889/) (2014)
- [A Framework for Robust Assessment of Power Grid Stability and Resiliency](https://mlsanity.com/paper/1504.04684/) (2015)
- [A critical review of data-driven transient stability assessment of power systems: principles, prospects and challenges](https://mlsanity.com/paper/2111.00978/) (2021)
- [Imbalanced Sample Generation and Evaluation for Power System Transient Stability Using CTGAN](https://mlsanity.com/paper/2112.08836/) (2021)

## 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.