Logo

Autoreclosure in Extra High Voltage Lines using Taguchi's Method and Optimized Neural Networks

Desta, Zahlay F. and K.S., Rama Rao (2009) Autoreclosure in Extra High Voltage Lines using Taguchi's Method and Optimized Neural Networks. In: 2009 The IEEE International Conference on Computer Engineering and Technology, ICCET2009, Singapore, 22 Jan 2009, Singapore.

[img] PDF
Restricted to Registered users only

343Kb

Abstract

Abstract— This paper presents a method to discriminate a temporary fault from a permanent one in an extra high voltage (EHV) transmission line so that 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 standard Error Back-Propagation, Levenberg Marquardt Algorithm and Resilient Back-Propagation training algorithms together with Taguchi’s Method. The algorithms are developed using MATLAB software. A range of faults are simulated on EHV modeled transmission line using SimPowerSytems, and the spectra of the fault data are analyzed using fast Fourier transform to extract features of each type of fault. For both training and testing purposes, the neural network is fed with the normalized energies of the DC component, the fundamental and the first four harmonics of the faulted voltages. The developed algorithm is effectively trained, verified and validated with a set of training, dedicated testing and validation data respectively.

Item Type:Conference or Workshop Item (Paper)
Uncontrolled Keywords:Autoreclosure, EHV transmission line faults, artificial neural networks, Levenberg Marquardt algorithm, back-propagation algorithm, RPROP, Taguchi’s method.
Subjects:T Technology > TK Electrical engineering. Electronics Nuclear engineering
Departments / MOR / COE:Departments > Electrical & Electronic Engineering
ID Code:2640
Deposited By: Assoc Prof Dr K. S. Rama Rao
Deposited On:30 Jul 2010 07:07
Last Modified:19 Jan 2017 08:25

Repository Staff Only: item control page

Document Downloads

More statistics for this item...