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
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;
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
DOI :
10.1109/TENCON.2008.4766623