DocumentCode
2528544
Title
ANN based power system fault classification
Author
Upendar, J. ; Gupta, C.P. ; Singh, G.K.
Author_Institution
Dept. of Electr. Eng., Indian Inst. of Technol. Roorkee, Roorkee
fYear
2008
fDate
19-21 Nov. 2008
Firstpage
1
Lastpage
6
Abstract
This paper presents Wavelet based back propagation algorithm for classifying the power system faults, which is quite reliable, fast and computationally efficient. The proposed technique consists of a preprocessing unit based on discrete wavelet transform (DWT) in combination with an artificial neural network (ANN). The DWT acts as extractor of distinctive features in the input current signal which are collected at source end. The information is then fed into ANN for classifying faults. It can be used on-line following the operation of digital relays or off-line using the data stored in the digital recording apparatus. Extensive simulation studies carried out using MATLAB show that the proposed algorithm provides an accepted degree of accuracy in fault classification under different fault conditions.
Keywords
backpropagation; discrete wavelet transforms; fault diagnosis; neural nets; power system analysis computing; power system faults; power system protection; relays; ANN; MATLAB; artificial neural network; digital recording apparatus; digital relays; discrete wavelet transform; distinctive features; power system fault classification; wavelet based back propagation algorithm; Artificial neural networks; Continuous wavelet transforms; Data mining; Discrete wavelet transforms; Frequency; Power system faults; Power system transients; Transient analysis; Wavelet analysis; Wavelet transforms; Artificial Neural Network; Fault Classification; Wavelet Transform;
fLanguage
English
Publisher
ieee
Conference_Titel
TENCON 2008 - 2008 IEEE Region 10 Conference
Conference_Location
Hyderabad
Print_ISBN
978-1-4244-2408-5
Electronic_ISBN
978-1-4244-2409-2
Type
conf
DOI
10.1109/TENCON.2008.4766623
Filename
4766623
Link To Document