标题:Real-time transient stability prediction using incremental learning algorithm
作者:Chu, Xiaodong ;Liu, Yutian
通讯作者:Chu, X.
作者机构:[Chu, Xiaodong ;Liu, Yutian ] School of Electrical Engineering, Shandong University, Jinan 250061, China
会议名称:2004 IEEE Power Engineering Society General Meeting
会议日期:6 June 2004 through 10 June 2004
来源:2004 IEEE Power Engineering Society General Meeting
出版年:2004
卷:2
页码:1565-1568
关键词:Artificial neural network; Incremental learning; On-line dynamic security assessment; Transient stability prediction
摘要:Real-time transient stability prediction is an essential and challenging step of response-based transient stability emergency controls. Machine learning methods including decision trees and artificial neural networks have the potential to be applied to the problem. To counter the inefficiency of common machine learning methods in learning new information, an incremental learning algorithm is employed to train an artificial neural network for real-time transient stability prediction. The resulted learning framework can readily be integrated into on-line dynamic security assessment. The effectiveness of such prediction model is demonstrated by the simulation results of a practical power system.
收录类别:EI;SCOPUS
Scopus被引频次:3
资源类型:会议论文;期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-13344277949&partnerID=40&md5=19765df4f3d287721dd0bc47f2a85f71
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