Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/62298
Title: Hybrid model for transient stability evaluation of interconnected longitudinal power systems using neural network/pattern recognition approach
Authors: Chang, C.S. 
Srinivasan, Dipti 
Liew, A.C. 
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

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