Achieving 100x Acceleration for N-1 Contingency Screening With Uncertain Scenarios Using Deep Convolutional Neural Network

• Published in: IEEE transactions on power systems Vol. 34; no. 4; pp. 3303 - 3305
• Main Authors: Du, Yan, Li, Fangxing, Li, Jiang, Zheng, Tongxin
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.07.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: AC power flow; Acceleration; Artificial neural networks; Contingency; Convolution; data-driven; deep convolutional neural network (deep CNN); Feature extraction; image processing; Kernel; Load flow; N-1 contingency screening; Neural networks; Screening; Security; Training
• DOI: 10.1109/TPWRS.2019.2914860
• ISSN: 0885-8950

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.