Please use this identifier to cite or link to this item:
|Title:||Hybrid model for transient stability evaluation of interconnected longitudinal power systems using neural network/pattern recognition approach||Authors:||Chang, C.S.
|Issue Date:||Feb-1994||Citation:||Chang, C.S., Srinivasan, Dipti, Liew, A.C. (1994-02). Hybrid model for transient stability evaluation of interconnected longitudinal power systems using neural network/pattern recognition approach. IEEE Transactions on Power Systems 9 (1) : 85-92. ScholarBank@NUS Repository.||Abstract:||A methodology for evaluation of transient stability of medium size interconnected longitudinal power systems has been developed using a hybrid neural network/ pattern recognition approach. Assessment of transient stability is done using a fast pattern recognition algorithm at each load level, accurately predicted by a neural network on a half-hourly basis. As opposed to the conventional approaches, this hybrid strategy can make fast decisions with less computations.||Source Title:||IEEE Transactions on Power Systems||URI:||http://scholarbank.nus.edu.sg/handle/10635/62298||ISSN:||08858950|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Feb 28, 2018
WEB OF SCIENCETM
checked on Jul 17, 2019
checked on Oct 24, 2020
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.