Power-grid stability predictions using transferable machine learning

TL;DR

This paper explores machine learning models to predict power-grid stability efficiently, demonstrating transferability from synthetic to real-world grids and outperforming traditional simulation methods.

Contribution

It introduces transferable machine learning approaches for power-grid stability prediction, trained on synthetic data, applicable to real-world grids, reducing computational costs.

Findings

ML models outperform traditional simulations in speed

Transferability of models from synthetic to real grids confirmed

Heterogeneous input-power data improves prediction accuracy

Abstract

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.