期刊
IEEE TRANSACTIONS ON POWER SYSTEMS
卷 35, 期 3, 页码 2399-2411出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TPWRS.2019.2957377
关键词
Transient analysis; Power system stability; Stability analysis; Trajectory; Thermal stability; Convolutional neural networks; deep learning; phasor measurements; trajectories; transient stability
资金
- Research Grants Council of Hong Kong Special Administrative Region under the Theme-based Research Scheme [T23-701/14-N]
- National Natural Science Foundation of China [51677097, U1766214]
This paper develops a hierarchical deep learning machine (HDLM) to efficiently achieve both quantitative and qualitative online transient stability prediction (TSP). For the sake of improving its online efficiency, multiple generators' fault-on trajectories as well as the two closest data-points in pre-/post-fault stages are acquired by PMUs to form its raw inputs. An anti-noise graphical transient characterization technique is tactfully designed to transform multiplex trajectories into 2-D images, within which system-wide transients are concisely described. Then, following the divide-and-conquer philosophy, the HDLM trains a two-level convolutional neural network (CNN) based regression model. With stability margin regressions hierarchically refined, it manages to perform reliable and adaptive online TSP almost immediately after fault clearance. Test results on the IEEE 39-bus test system and the real-world Guangdong Power Grid in South China demonstrate the HDLM's superior performances on both stability status and stability margin predictions.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据