Arc-Fault Recognition Based on BP Neural Network

Author

Ma, Shaohua ; Guan, Lina

Author_Institution

Shenyang Univ. of Technol., Shenyang, China

Volume

1

fYear

2011

fDate

6-7 Jan. 2011

Firstpage

584

Lastpage

586

Abstract

The back-propagation algorithm has been used widely as a learning algorithm in a feed-forward multilayer neural network. In this study, fault detection was carried out using the information of the arc current. After collecting the actual data, wavelet transformation were adopted in order to obtain the sideband or detail value characteristics under healthy and various faulty operating conditions. The feature vectors consisted of the values obtained by wavelet transformation and turned into the input of network. They were trained with BP neural network system, and the fault detection algorithm was verified using the test data. The experiment shows that the method has better performance for arc-fault recognition.

Keywords

arcs (electric); backpropagation; domestic safety; electrical engineering computing; electrical faults; electrical safety; fault location; multilayer perceptrons; wavelet transforms; BP neural network; arc current; arc fault recognition; backpropagation algorithm; feedforward multilayer neural network; learning algorithm; wavelet transform; Artificial neural networks; Computational modeling; Data models; Fires; Mathematical model; Training; Wavelet transforms; BP neural network; arc-fault; wavelet transformation;

fLanguage

English

Publisher

ieee

Conference_Titel

Measuring Technology and Mechatronics Automation (ICMTMA), 2011 Third International Conference on

Conference_Location

Shangshai

Print_ISBN

978-1-4244-9010-3

Type

conf

DOI

10.1109/ICMTMA.2011.149

Filename

5720852