# Deep Learning for Power System Security Assessment

arXiv: https://arxiv.org/abs/1904.09029 · HTML: https://mlsanity.com/paper/1904.09029/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Mar 2019.

## Built on by (most cited first, 3 of 3)

- [Semi-Supervised Multi-Task Learning Based Framework for Power System Security Assessment](https://mlsanity.com/paper/2407.08886/) (2024)
- [Versatile and Robust Transient Stability Assessment via Instance Transfer Learning](https://mlsanity.com/paper/2102.10296/) (2021)
- [Anomaly Detection with Machine Learning Algorithms in Large-Scale Power Grids](https://mlsanity.com/paper/2602.10888/) (2026)

## Abstract (arXiv)

Security assessment is among the most fundamental functions of power system operator. The sheer complexity of power systems exceeding a few buses, however, makes it an extremely computationally demanding task. The emergence of deep learning methods that are able to handle immense amounts of data, and infer valuable information appears as a promising alternative. This paper has two main contributions. First, inspired by the remarkable performance of convolutional neural networks for image processing, we represent for the first time power system snapshots as 2-dimensional images, thus taking advantage of the wide range of deep learning methods available for image processing. Second, we train deep neural networks on a large database for the NESTA 162-bus system to assess both N-1 security and small-signal stability. We find that our approach is over 255 times faster than a standard small-signal stability assessment, and it can correctly determine unsafe points with over 99% accuracy.