Fault Detection and Classification Approaches in Transmission Lines Using Artificial Neural Networks

被引：0

作者：

Ben Hessine, Moez ^{[1
]}

Jouini, Houda ^{[1
]}

Chebbi, Souad ^{[1
]}

机构：

[1] Univ Tunis, ESSTT, Latice Lab, High Sch Sci & Techn, Hussien 1008, Tunisia

来源：

2014 17TH IEEE MEDITERRANEAN ELECTROTECHNICAL CONFERENCE (MELECON) | 2014年

关键词：

Fault detection; Fault classification; Transmission line; Artificial neural networks (ANN); LOCATION;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

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.

引用

页码：515 / 519

页数：5

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