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  • 1.
    Salles, Rafael S.
    et al.
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.
    de Oliveira, Roger Alves
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.
    Rönnberg, Sarah K.
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.
    Mariscotti, Andrea
    Department of Electrical, Electronics and Telecommunication Engineering and Naval Architecture (DITEN), University of Genoa, 16145 Genoa, Italy.
    Analytics of Waveform Distortion Variations in Railway Pantograph Measurements by Deep Learning2022In: IEEE Transactions on Instrumentation and Measurement, ISSN 0018-9456, E-ISSN 1557-9662, Vol. 71, article id 2516211Article in journal (Refereed)
    Abstract [en]

    Waveform distortion in general represent a widespread problem in electrified transports due to interference, service disruption, increased losses and ageing of components. Given the multitude of moving sources and the extremely variable operating conditions, short time records must be considered for analysis, and this increases in turn its complexity, from which the need for effective automated processing, as offered by a deep learning (DL) approach. This paper proposes an application of unsupervised DL to measurements of railway pantograph quantities to identify waveform distortion patterns. Data consists of pantograph current from a Swiss 15 kV 16.7 Hz railway system. Three DL input types are considered: waveforms, harmonic spectra, and supraharmonic spectra. The applied DL method applied is the deep autoencoder (DAE) followed by feature clustering, using techniques to define a suitable number of clusters. Short-term distortion is evaluated over sub-10 min intervals of harmonic and supraharmonic spectra down to sub-second intervals. Results are explained among others by connecting the distribution of the clusters (determined by self-supervised method) to the dynamic operating conditions of the rolling stock. Resulting DAE performance are superior in terms of accuracy and comprehensiveness of spectral components compared to a more traditional principal component analysis (PCA) that was chosen as reference for comparison.

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