Real-time monitoring of long-term voltage stability via convolutional neural network

Convolutional Neural Network (CNN) is one of the most promising deep learning technique that has achieved great success in many areas. In this paper, we implement a CNN for real-time monitoring of long-term voltage stability margin (VSM). To clarify our motivations of introducing CNN in this problem, we first discuss the essence and the complicity of VSM prediction, and summarize the limitations of existing methods, then point out the input structure of CNN inherently contains the topology information of power network, thus could have great potential in solving the problem. An CNN architecture, together with an input encoding method that strengthens topology information, is proposed and tested on IEEE 30-bus system. Preliminary results show that it can achieve better prediction performance comparing to some existing methods, and can be successfully employed in online voltage stability monitoring.