Neural-network-based fuzzy model and its application to transient stability prediction in power systems

doi:10.1109/5326.740677

淡江大學機構典藏 > 工學院 > 電機工程學系暨研究所 > 期刊論文 > Item 987654321/46252

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题名:	Neural-network-based fuzzy model and its application to transient stability prediction in power systems
作者:	蘇木春;Su, Mu-chun;Liu, Chih-wen;Tsay, Shuenn-shing
贡献者:	淡江大學電機工程學系
日期:	1999-02
上传时间:	2010-03-26 21:42:19 (UTC+8)
出版者:	Piscataway: Institute of Electrical and Electronics Engineers (IEEE)
摘要:	We present a general approach to deriving a new type of neural network-based fuzzy model for a complex system from numerical and/or linguistic information. To efficiently identify the structure and the parameters of the new fuzzy model, we first partition the output space instead of the input space. As a result, the input space itself induces corresponding partitions within each of which inputs would have similar outputs. Then we use a set of hyperrectangles to fit the partitions of the input space. Consequently, the premise of an implication in the new type of fuzzy rule is represented by a hyperrectangle and the consequence is represented by a fuzzy singleton. A novel two-layer fuzzy hyperrectangular composite neural network (FHRCNN) can be shown to be computationally equivalent to such a special fuzzy model. The process of presenting input data to each hidden node in a FHRCNN is equivalent to firing a fuzzy rule. An efficient learning algorithm was developed to adjust the weights of an FHRCNN. Finally, we apply FHRCNNs to provide real-time transient stability prediction for use with high-speed control in power systems. From simulation tests on the IEEE 39-bus system, it reveals that the proposed novel FHRCNN can yield a much better performance than that of conventional multilayer perceptrons (MLP's) in terms of computational burden and classification rate
關聯:	IEEE transactions on systems, man and cybernetics, Part : C 29(1), pp.149-157
DOI:	10.1109/5326.740677
显示于类别:	[電機工程學系暨研究所] 期刊論文

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