Please use this identifier to cite or link to this item:
Title: Identification of Overcurrent Relay Faults Using Backpropagation Neural Network
Authors: Ching Li Tey 
Farah Hani Nordin 
Tuan Ab Rashid Bin Tuan Abdullah 
Issue Date: 2015
Conference: The 3rd National Graduate Conference (NatGrad2015), Universiti Tenaga Nasional, Putrajaya Campus, 8-9 April 2015 
Abstract: Overcurrent relays are considered as the backbone for any protection system and any faults in overcurrent relays may lead to power system breakdown. However, the actual fault of a relay is usually known after the faulty relay is sent for a Root Cause Analysis which is time consuming. Thus, the aim of this paper is to identify the overcurrent relay faults using Backpropogation Neural Network based on the general faults obtained by visual inspection without going through the Root Cause Analysis. A systematic system is created using neural network and Graphical User Interface (GUI) so that an inexperienced user will be able to predict the actual fault without sending the relay to the manufacturer. This will not only expedite repairing time, improve system availability but also will be able to reduce the operating cost.
Appears in Collections:COGS Scholarly Publication

Show full item record

Google ScholarTM


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.