# Power-grid stability predictions using transferable machine learning

arXiv: https://arxiv.org/abs/2105.07562 · HTML: https://mlsanity.com/paper/2105.07562/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 76 references that are in the map.
- First posted on arXiv 17 May 2021.

## Builds on (most cited first, 8 of 8)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Small vulnerable sets determine large network cascades in power grids](https://mlsanity.com/paper/1804.06432/) (2018)
- [On structural and dynamical factors determining the integrated basin instability of power-grid nodes](https://mlsanity.com/paper/1906.08629/) (2019)
- [Asymmetry underlies stability in power grids](https://mlsanity.com/paper/2103.10952/) (2021)
- [Emergence of scale-free blackout sizes in power grids](https://mlsanity.com/paper/2007.06967/) (2020)
- [Relational flexibility of network elements based on inconsistent community detection](https://mlsanity.com/paper/1904.05523/) (2019)
- [A machine learning approach to predicting dynamical observables from network structure](https://mlsanity.com/paper/1910.00544/) (2019)

## Built on by (most cited first, 7 of 7)

- [Predicting Basin Stability of Power Grids using Graph Neural Networks](https://mlsanity.com/paper/2108.08230/) (2021)
- [Attention-based Deep Neural Networks for Battery Discharge Capacity Forecasting](https://mlsanity.com/paper/2202.06738/) (2022)
- [Toward Dynamic Stability Assessment of Power Grid Topologies using Graph Neural Networks](https://mlsanity.com/paper/2206.06369/) (2022)
- [Just-In-Time Learning for Operational Risk Assessment in Power Grids](https://mlsanity.com/paper/2209.12762/) (2022)
- [Fast Transient Stability Prediction Using Grid-informed Temporal and Topological Embedding Deep Neural Network](https://mlsanity.com/paper/2201.09245/) (2022)
- [Reinforcement Learning Optimizes Power Dispatch in Decentralized Power Grid](https://mlsanity.com/paper/2407.15165/) (2024)
- [On zero-shot learning in neural state estimation of power distribution systems](https://mlsanity.com/paper/2408.05787/) (2024)

## Abstract (arXiv)

Complex network analyses have provided clues to improve power-grid stability with the help of numerical models. The high computational cost of numerical simulations, however, has inhibited the approach, especially when it deals with the dynamic properties of power grids such as frequency synchronization. In this study, we investigate machine learning techniques to estimate the stability of power-grid synchronization. We test three different machine learning algorithms -- random forest, support vector machine, and artificial neural network -- training them with two different types of synthetic power grids consisting of homogeneous and heterogeneous input-power distribution, respectively. We find that the three machine learning models better predict the synchronization stability of power-grid nodes when they are trained with the heterogeneous input-power distribution than the homogeneous one. With the real-world power grids of Great Britain, Spain, France, and Germany, we also demonstrate that the machine learning algorithms trained on synthetic power grids are transferable to the stability prediction of the real-world power grids, which implies the prospective applicability of machine learning techniques on power-grid studies.