Abstract:  ABSTRACT： In the field of machine learning, transient stability assessment can be considered as a two-class problem of estimating the stability boundary through large number of fault samples. This paper proposes a method of deep learning to solve this problem. The method consists of four stages: firstly, using samples to construct the original input feature for describing the dynamic characteristics of the power system；secondly, variational auto-encoders （VAE） is used to perform unsupervised learning on the original input feature to obtain high-order features；thirdly, the supervised training of convolution neural network （CNN） is carried out to obtain the relationship between high order characteristic and transient stability of power system；finally, the model is applied to the transient stability assessment of power system. Simulation on the New England 39-bus test system shows that the proposed approach has high accuracy, rare misclassification of unstable sample and excellent robustness with noise for transient stability assessment （TSA）. Therefore, it is suitable for quasi-real-time online transient stability assessment based on wide-area measurement information.

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