  With the increase of uncertain elements in power systems and extensive
deployment of online monitoring devices, it is necessary to search a more
real-time and robust voltage stability assessment method. This study, using PMU
monitoring data, explores a novel data-driven approach for long-term voltage
stability assessment based on variational autoencoder (VAE). Our method is
capable of extracting the most representative features by an unsupervised data
mining method in a probabilistic learning way. Different from most of familiar
feature extraction methods, it regularizes latent features in an expected
stochastic distribution. Furthermore, a statistical indicator by sampling
latent features after variance reduction is proposed to assess long-term
voltage stability. Our approach is tested in various simulated power systems
with different load increment models. Other cases show the accuracy and speed
of our approach for estimating voltage collapse point. These testing cases
successfully demonstrate the accuracy and effectiveness of our approach.
