Fault detection and classification in transmission lines-based on wavelet transform and ANN

被引：268

作者：

Silva, K. M. ^{[1
]}

Souza, B. A. ^{[1
]}

Brito, N. S. D. ^{[1
]}

机构：

[1] Univ Fed Campina Grande, Dept Elect Engn, BR-58109970 Campina Grande, Brazil

来源：

IEEE TRANSACTIONS ON POWER DELIVERY | 2006年 / 21卷 / 04期

关键词：

artificial neural networks (ANNs); fault classification; fault detection; transmission lines; wavelet transforms;

D O I：

10.1109/TPWRD.2006.876659

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 proposes a novel method for transmission-line fault detection and classification using oscillographic data. The fault detection and its clearing time are determined based on a set of rules obtained from the current waveform analysis in time and wavelet domains. The method is able to single out faults from other power-quality disturbances, such as voltage sags and oscillatory transients, which are common in power systems operation. An artificial neural network classifies the fault from the voltage and current waveforms pattern recognition in the time domain. The method has been used for fault detection and classification from real oscillographic data of a Brazilian utility company with excellent results.

引用

页码：2058 / 2063

页数：6

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