Comparison between Principal Component Analysis and Wavelet Transform 'Filtering Methods for Lightning Stroke Classification on Transmission Lines

John A. Morales, E. Orduña, C. Rehtanz, R. J. Cabral, A. S. Bretas

Resultado de la investigación: Contribución a una revistaArtículorevisión exhaustiva

8 Citas (Scopus)

Resumen

© 2014 Elsevier B.V. This paper presents an assessment between Principal Component Analysis (PCA) and Wavelet Transform (WT) signal processing techniques applied for Transmission Lines (TLs) lightning stroke classification. In this work, the atmospherics discharges signals are analyzed in two steps. The first step objective is patterns extraction, which is developed through Principal Component Analysis and the Wavelet Transform. The second step objective is pattern classification, which is developed using three different techniques: Artificial Neural Network (ANN), k-Nearest Neighbors (k-NN) and Support Vector Machine (SVM). This work presents as assessment of lightning stroke classification, providing useful information, especially in extraction and selection of mother functions and the use of PCA. Both methodologies are assessed under different lightning stroke conditions. Features as extraction, speed, orthogonal functions and others are comparatively assess. Resu lts show that by using PCA, optimal mother functions can be extracted, presenting a new alternative for relaying protection.
Idioma originalInglés
Páginas (desde-hasta)37-46
Número de páginas10
PublicaciónElectric Power Systems Research
DOI
EstadoPublicada - 1 ene. 2015

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