Resumen

GUZMAN, Ian Carlo; OSLINGER, José Luis  y  DARIO NIETO, Rubén. Wavelet denoising of partial discharge signals and their pattern classification using artificial neural networks and support vector machines. Dyna rev.fac.nac.minas [online]. 2017, vol.84, n.203, pp.240-248. ISSN 0012-7353.  https://doi.org/10.15446/dyna.v84n203.63745.

This paper presents two pattern recognition approaches using Partial Discharges fingerprints as input features to classify PD patterns. A multi-layer perceptron (MLP) backpropagation neural network and a support vector machine (SVM) were trained to recognize three types of PD patterns. Experimental results showed that the algorithms can achieve high recognition rates. Moreover, the Discrete wavelet transform (DWT) was used to denoise PD signals as a prior stage to the classification process. Different mother wavelets were tested for different levels of decomposition in order to find appropriate wavelet parameters for better signal to noise ratio (SNR) and less distortion after the denoising process.

Palabras clave : Partial Discharge (PD); Discrete Wavelet Transform (DWT); Artificial Neural Network (ANN); Support Vector Machine (SVM).

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