# A critical review of data-driven transient stability assessment of power systems: principles, prospects and challenges

arXiv: https://arxiv.org/abs/2111.00978 · HTML: https://mlsanity.com/paper/2111.00978/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Nov 2021.

## Built on by (most cited first, 4 of 4)

- [Attention-based Deep Neural Networks for Battery Discharge Capacity Forecasting](https://mlsanity.com/paper/2202.06738/) (2022)
- [Fast Transient Stability Prediction Using Grid-informed Temporal and Topological Embedding Deep Neural Network](https://mlsanity.com/paper/2201.09245/) (2022)
- [Research on short-term load forecasting model based on VMD and IPSO-ELM](https://mlsanity.com/paper/2410.05300/) (2024)
- [Real-Time Surrogate Modeling for Fast Transient Prediction in Inverter-Based Microgrids Using CNN and LightGBM](https://mlsanity.com/paper/2603.29255/) (2026)

## Abstract (arXiv)

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.