DocumentCode :
3097108
Title :
Classification of power quality disturbances using Wavelet and Artificial Neural Networks
Author :
Rodriguez, A. ; Ruiz, J.E. ; Aguado, J. ; Lopez, J.J. ; Martin, F.I. ; Muñoz, F.
Author_Institution :
Electr. Eng. Dept., Univ. of Malaga, Malaga, Spain
fYear :
2010
fDate :
4-7 July 2010
Firstpage :
1589
Lastpage :
1594
Abstract :
An automated classification system based on Wavelet transform as a feature extraction tool in combination with Artificial Neural Network as algorithm classifier is presented. Perturbed signals generated according to mathematical models have been used to obtain experimental results in two stages, first, with a data set with simple disturbances and, later, including complex disturbances, more usual in real electrical system. In both cases noise is added to the signals from 40dB to 20dB. Two different neural networks have been used as classifier algorithm, a backpropagation and probabilistic. A data set with several disturbances, simple and complex, has been generated by simulation software based on electrical models, to test the implemented system. Evaluation results verifying the accuracy of the proposed method are presented.
Keywords :
backpropagation; feature extraction; neural nets; pattern classification; power engineering computing; power supply quality; probability; wavelet transforms; artificial neural network; automated classification system; backpropagation; feature extraction; power quality disturbance; probabilistic; wavelet transform; Artificial neural networks; Classification algorithms; Mathematical model; Power quality; Voltage fluctuations; Wavelet transforms;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Industrial Electronics (ISIE), 2010 IEEE International Symposium on
Conference_Location :
Bari
Print_ISBN :
978-1-4244-6390-9
Type :
conf
DOI :
10.1109/ISIE.2010.5636343
Filename :
5636343
Link To Document :
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