Publicado

2017-10-01

Wavelet denoising of partial discharge signals and their pattern classification using artificial neural networks and support vector machines

Filtrado wavelet de descargas parciales y su clasificación de patrones usando redes neuronales artificiales y máquinas de soporte vectorial

Palabras clave:

Partial Discharge (PD), Discrete Wavelet Transform (DWT), Artificial Neural Network (ANN), Support Vector Machine (SVM) (en)
Descarga parcial (DP), transformada wavelet discreta, red neuronal artificial, máquina de soporte vectorial (es)

Autores/as

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.
Este artículo presenta dos enfoques de reconocimiento de patrones usando huellas dactilares de descargas parciales como características de entrada para llevar a cabo la clasificación de patrones de DP. Un perceptrón multicapa (MLP) basado en el algoritmo de propagación hacia atrás y una máquina de soporte vectorial fueron entrenados para reconocer tres tipos de patrones de DP. Los resultados experimentales demostraron que los algoritmos pueden arrojar altas tasas de reconocimiento. De otra parte, la trasformada wavelet discreta (DWT) fue utilizada para eliminar el nivel de ruido presente en las DP como una etapa previa al proceso de clasificación. Diferentes wavelets madre fueron probadas a diferentes niveles de descomposición con el objeto de encontrar parámetros wavelet apropiados para obtener una mejor relación señal-ruido (SNR) y menos distorsión después del proceso de filtrado.

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Citas

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