DocumentCode :
143638
Title :
Fault detection and classification approaches in transmission lines using artificial neural networks
Author :
Ben Hessine, Moez ; Jouini, Hana ; Chebbi, Souad
Author_Institution :
Latice Lab., Univ. of Tunis, Tunis, Tunisia
fYear :
2014
fDate :
13-16 April 2014
Firstpage :
515
Lastpage :
519
Abstract :
This paper studies a new approach based on the artificial neural networks (ANN) for the fault detection and classification, in real time, in transmission lines to extra high voltage (EHV) which can be used in the production system digital protection. This approach is based on the treatment of each phase current and voltage. The outputs of the ANN indicate the fault presence and it type. The ANN detector and classifier are tested in various fault types, various locations, different fault resistances and various inception angle. All the test results show that the fault suggested detector and classifier can be used to support a new system generations of protection relay at high speed.
Keywords :
fault diagnosis; neural nets; pattern classification; power engineering computing; power generation protection; power transmission lines; relay protection; ANN detector and classifier; EHV; artificial neural networks; classification approach; extra high voltage; fault detection and classification; fault resistance; inception angle; phase current and voltage; production system digital protection; protection relay; system generation; transmission lines; Artificial neural networks; Fault detection; Power transmission lines; Radio frequency; Relays; Training; Artificial neural networks (ANN); Fault classification; Fault detection; Transmission line;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Mediterranean Electrotechnical Conference (MELECON), 2014 17th IEEE
Conference_Location :
Beirut
Type :
conf
DOI :
10.1109/MELCON.2014.6820588
Filename :
6820588
Link To Document :
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