Achieving 100x Acceleration for N-1 Contingency Screening With Uncertain Scenarios Using Deep Convolutional Neural Network - Du, Yan; Li, Fangxing; Li, Jiang; Zheng, Tongxin (IEEE transactions on power systems, 2019)
The increasing penetration of renewable energy makes the traditional N-1 contingency screening highly challenging when a large number of uncertain scenarios need to be combined with contingency screening. In this letter, a novel data-driven method, similar to image-processing technique, is proposed for accelerating N-1 contingency screening of power systems based on the deep convolutional neural network (CNN) method for calculating AC power flows under N-1 contingency and uncertain scenarios. Once the deep CNN is well trained, it has high generalization and works in a nearly computation-free fashion for unseen instances, such as topological changes in the N-1 cases and uncertain renewable scenarios. The proposed deep CNN is implemented on several standard IEEE test systems to verify its accuracy and computational efficiency. The proposed study constitutes a solid demonstration of the considerable potential of the data-driven deep CNN in future online applications.