  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.
