%0 Conference Paper
%A Fitiwi, D.Z.
%A Rao, K.S.R.
%D 2009
%F scholars:588
%K Artificial Neural Network; Auto-reclosure; Automatic reclosing; Autoreclosing; Bus systems; Extinction time; Extra high voltage transmission lines; Fault identifications; Levenberg-Marquardt; Optimizing parameters; Reclosing; Resilient backpropagation; Self-adaptive; Single machine infinite bus; Single machines; Software environments; Taguchi; Taguchi's methods; Temporary fault; Training algorithms, Backpropagation; Data flow analysis; Model structures; Optimization; Reclosing circuit breakers, Neural networks
%R 10.1109/TENCON.2009.5395874
%T Assessment of ANN-based auto-reclosing scheme developed on single machine-infinite bus model with IEEE 14-bus system model data
%U https://khub.utp.edu.my/scholars/588/
%X This paper focuses on methods to discriminate a temporary fault from a permanent one, and accurately determine fault extinction time in an extra high voltage (EHV) transmission line in a bid to develop a self-adaptive automatic reclosing scheme. Consequently, improper reclosing of the line onto a fault is avoided. The fault identification prior to reclosing is based on optimized artificial neural network associated with three different training algorithms. In addition, Taguchi's methodology is employed in optimizing parameters that significantly influence during and post-training performance of the neural network. A comparison of overall performance of the three algorithms, developed and coded in MATLABTM software environment, is also presented. To validate the work, the developed technique in a single machine infinite bus (SMIB) model has been tested by data obtained from benchmark IEEE 14-bus system model simulations. The results show the efficacy of the developed adaptive automatic reclosing method. ©2009 IEEE.
%Z cited By 5; Conference of 2009 IEEE Region 10 Conference, TENCON 2009 ; Conference Date: 23 November 2009 Through 26 November 2009; Conference Code:79857