%K Adaptive autoreclosure; Artificial Neural Network; Error back-propagation; Levenberg-Marquardt; Resilient backpropagation; Taguchi's methods, Backpropagation algorithms; Data flow analysis; Feature extraction; Reclosing circuit breakers; Standby power systems, Neural networks
%X This paper presents a novel intelligent autoreclosure technique to discriminate temporary faults from permanent faults, and accurately determine fault extinction time. A variety of fault simulations are carried out on a specified transmission line on the standard IEEE 9-bus electric power system using MATLAB/SimPowerSytems. FFT and Prony analysis methods are employed to extract data features from each simulated fault. The fault identification prior to reclosing is accomplished by an artificial neural network trained by standard Error Back-Propagation, Levenberg Marquardt and Resilient Back-Propagation algorithms which are developed using MATLAB. Some important parameters which strongly affect the entire training process are fine-tuned with Taguchi's method to their corresponding best values. The robustness of the developed ANN identifier is verified by testing it with the data patterns which consists of high impedance faults obtained from IEEE 14-bus benchmark system. Test results show the efficacy of the proposed AR scheme. © IEEE 2010.
%D 2010
%R 10.1109/ICPS.2010.5489881
%O cited By 3; Conference of 2010 IEEE Industrial and Commercial Power Systems Technical Conference, I and CPS 2010 ; Conference Date: 9 May 2010 Through 13 May 2010; Conference Code:81303
%L scholars1187
%J Conference Record - Industrial and Commercial Power Systems Technical Conference
%C Tallahassee, FL
%T A new intelligent autoreclosing scheme using Artificial neural network and Taguchi's methodology
%A F.D. Zahlay
%A K.S.R. Rao
%A T.B. Ibrahim