Deep belief network based nonlinear representation learning for transient stability assessment

Transient stability assessment is examined in a data driven framework. The original transient stability data are embedded into a low-dimensional representation space using a deep belief network (DBN) based nonlinear representation learning method. Specifically, unsupervised pre-training is used to learn the data distribution first, and then the expected classification accuracy (ECA) index is used to fine-tune the parameters of the DBN. The structure of power grid is also considered in the learning process. In the representation space, a simple linear model is utilized to classify the unstable cases from stable ones. The proposed method is demonstrated in a regional power system in central China and gets remarkably better testing results compared with a SVM benchmark. The most unique advantage of the proposed approach is that it can learn high level abstract representations automatically to avoid the potential negligence and mistakes introduced by human feature engineering, hence gain more accurate and robust results.