Identifying Vulnerable Set of Cascading Failure in Power Grid Using Deep Learning Framework

Abstract

The cascading failure is a typical failure propagation process which can cause significant consequence to the power system. It can be triggered by the vulnerable set composed of combinations of transmission lines with specific failures. So it is of great significance to identify the vulnerable set. In this paper, we propose an identification model for the vulnerable set under deep learning framework. The main part of the model consists of autoencoder and classification network for reducing dimensionality and identifying vulnerable set respectively. The model is trained by the data generated from cascading failure simulation platform. We conduct experiments on IEEE 30-Bus and 200-Bus systems with different initial failures to validate the identification and generalization capability. And the time consumption is also discussed to demonstrate the efficiency of the model. All of the indicators prove that the model is capable of identifying the vulnerable set effectively.