Pattern recognition of phenomena associated to power quality using neural networks

Author

Taboada, J.D. ; Cabrera, J.C. ; Ramos, G. ; Torres, M.T.

Author_Institution

Los Andes Univ., Merida, Venezuela

fYear

2004

fDate

8-11 Nov. 2004

Firstpage

1

Lastpage

5

Abstract

This paper presents an approximation to the recognition of phenomena associated to power quality in electric networks, by means of neural networks and based on the work discussed in reference [Taboada, JD, et al., (2003)], where a former recognition of the phenomena was processed using the discrete wavelet transform (DWT). A multiple layer perceptron (MLP) was used, together with the back propagation algorithm for the training process. The patterns recognized corresponded to signals of harmonic, transient, sags and swell waveforms.

Keywords

backpropagation; discrete wavelet transforms; multilayer perceptrons; pattern recognition; power supply quality; power system analysis computing; power system harmonics; DWT; back propagation algorithm; discrete wavelet transform; electric network; harmonic signal; multiple layer perceptron; neural network; pattern recognition; power quality; sag signal; swell waveform; training process; transient signal; Databases; Discrete wavelet transforms; Neural networks; Neurons; Pattern recognition; Power generation; Power quality; Signal generators; Signal processing; Signal processing algorithms;

fLanguage

English

Publisher

ieee

Conference_Titel

Transmission and Distribution Conference and Exposition: Latin America, 2004 IEEE/PES

Print_ISBN

0-7803-8775-9

Type

conf

DOI

10.1109/TDC.2004.1432341

Filename

1432341