# Intelligent Time-Adaptive Transient Stability Assessment System

arXiv: https://arxiv.org/abs/1610.08235 · HTML: https://mlsanity.com/paper/1610.08235/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Oct 2016.

## Built on by (most cited first, 12 of 12)

- [DeepONet-Grid-UQ: A Trustworthy Deep Operator Framework for Predicting the Power Grid's Post-Fault Trajectories](https://mlsanity.com/paper/2202.07176/) (2022)
- [DAE-PINN: A Physics-Informed Neural Network Model for Simulating Differential-Algebraic Equations with Application to Power Networks](https://mlsanity.com/paper/2109.04304/) (2021)
- [A Deep-Learning Intelligent System Incorporating Data Augmentation for Short-Term Voltage Stability Assessment of Power Systems](https://mlsanity.com/paper/2112.03265/) (2021)
- [Data-Driven Short-Term Voltage Stability Assessment Based on Spatial-Temporal Graph Convolutional Network](https://mlsanity.com/paper/2103.03729/) (2021)
- [Noise-Resilient Quantum Machine Learning for Stability Assessment of Power Systems](https://mlsanity.com/paper/2104.04855/) (2021)
- [Attention-based Deep Neural Networks for Battery Discharge Capacity Forecasting](https://mlsanity.com/paper/2202.06738/) (2022)
- [glassoformer: a query-sparse transformer for post-fault power grid voltage prediction](https://mlsanity.com/paper/2201.09145/) (2022)
- [Revisiting data-driven dynamic security assessment with a tabular foundation model](https://mlsanity.com/paper/2607.16031/) (2026)
- [Fast Transient Stability Prediction Using Grid-informed Temporal and Topological Embedding Deep Neural Network](https://mlsanity.com/paper/2201.09245/) (2022)
- [Optimization Algorithms in Smart Grids: A Systematic Literature Review](https://mlsanity.com/paper/2301.07512/) (2023)
- [Graph Embedding Dynamic Feature-based Supervised Contrastive Learning of Transient Stability for Changing Power Grid Topologies](https://mlsanity.com/paper/2308.00537/) (2023)
- [Operator Learning for Power Systems Simulation](https://mlsanity.com/paper/2510.09704/) (2025)

## Abstract (arXiv)

Online identification of post-contingency transient stability is essential in power system control, as it facilitates the grid operator to decide and coordinate system failure correction control actions. Utilizing machine learning methods with synchrophasor measurements for transient stability assessment has received much attention recently with the gradual deployment of wide-area protection and control systems. In this paper, we develop a transient stability assessment system based on the long short-term memory network. By proposing a temporal self-adaptive scheme, our proposed system aims to balance the trade-off between assessment accuracy and response time, both of which may be crucial in real-world scenarios. Compared with previous work, the most significant enhancement is that our system learns from the temporal data dependencies of the input data, which contributes to better assessment accuracy. In addition, the model structure of our system is relatively less complex, speeding up the model training process. Case studies on three power systems demonstrate the efficacy of the proposed transient stability assessment system.