Application of a novel fuzzy neural network to real-time transient stability swings prediction based on synchronized phasor measurements

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Title:	Application of a novel fuzzy neural network to real-time transient stability swings prediction based on synchronized phasor measurements
Authors:	Liu, Chih-wen;蘇木春;Su, Mu-chun;Tsay, Shuenn-shing;Wang, Yi-jen
Contributors:	淡江大學電機工程學系
Date:	1999-05
Issue Date:	2010-03-26 21:29:00 (UTC+8)
Publisher:	Piscataway: Institute of Electrical and Electronics Engineers (IEEE)
Abstract:	The ability to rapidly acquire synchronized phasor measurements from around the system opens up new possibilies for power system protection and control. In this paper we develop a novel class of fuzzy hyperrectangular composite neural networks which utilize synchronized phasor measurements to provide fast transient stability swings prediction for use with high-speed control. Primary features of the method include constructing a fuzzy neural network for all fault locations, using a short window of realistic-recision post-fault phasor measurements for the prediction, and testing robustness to variations in the operating point. From simulation tests on a sample power system, it reveals that the proposed tool can yield a highly successful prediction rate in real-time.
Relation:	IEEE Transactions on Power Systems 14(2), pp.685-692
DOI:	10.1109/59.761898
Appears in Collections:	[Graduate Institute & Department of Electrical Engineering] Journal Article

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