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Assessment of ANN-based auto-reclosing scheme developed on single machine-infinite bus model with IEEE 14-bus system model data

Fitiwi, D. Z. and K., S. Rama Rao. (2009) Assessment of ANN-based auto-reclosing scheme developed on single machine-infinite bus model with IEEE 14-bus system model data. In: 2009 IEEE Region 10 Conference TENCON 2009, 23-26 Jan 2009, Singapore.

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Abstract

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.

Item Type:Conference or Workshop Item (Paper)
Subjects:T Technology > TK Electrical engineering. Electronics Nuclear engineering
Departments / MOR / COE:Departments > Electrical & Electronic Engineering
ID Code:4401
Deposited By: Assoc Prof Dr K. S. Rama Rao
Deposited On:18 Mar 2011 01:23
Last Modified:19 Jan 2017 08:25

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