Boosting algorithm to improve a voltage waveform classifier based on artificial neural network

Authors:
Milde M. S. Lira;Ronaldo R. B. de Aquino;Aida A. Ferreira;Manoel A. Carvalho, Jr.;Otoni Nóbrega Neto;Gabriela S. M. Santos;Carlos Alberto B. O. Lira
Affiliations:
Federal University of Pernambuco, Recife, PE, Brazil;Federal University of Pernambuco, Recife, PE, Brazil;Federal Federal Center of Technologic Education of Pernambuco, Recife, PE, Brazil;Federal University of Pernambuco, Recife, PE, Brazil;Federal University of Pernambuco, Recife, PE, Brazil;Federal University of Pernambuco, Recife, PE, Brazil;Federal University of Pernambuco, Recife, PE, Brazil
Venue:
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Year:
2007

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Abstract

An ANN-based classifier for voltage wave disturbance was developed. Voltage signals captured on the power transmission system of CHESF, Federal Power Utility, were processed in two steps: by wavelet transform and principal component analysis. The classification was carried out using a combination of six MLPs with different architectures: five representing the first to fifth-level details, and one representing the fifth-level approximation. Network combination was formed using the boosting algorithm which weights a model's contribution by its performance rather than giving equal weight to all models. Experimental results with real data indicate that boosting is clearly an effective way to improve disturbance classification accuracy when compared with the simple average and the individual models.