<abstract xmlns="http://www.w3.org/1999/xhtml"><p>The main purpose of this work is to propose a modern one-dimensional convolutional neural network (1 D CNN) configurations for distinguishing separate PD impulses from different types of PD sources while the parameters of these sources are changed. Three PD sources were built for signal generation: corona discharge, discharge in a void, and surface discharge. The reason for using separate PD impulses for classification is to develop a universal tool with the ability to recognize an insulation defects by analysing very few events in the insulation in a short range of time. Additionally, we found the optimal sample rates for the data acquisition for these network configurations. The necessity of signal filtering was also tested. The following configurations of a neural network were proposed: configuration for classification raw PD impulses; configuration for classification of PD impulses represented by power spectral density, for both filtered and unfiltered variants.</p></abstract>

The main purpose of this work is to propose a modern one-dimensional convolutional neural network (1 D CNN) configurations for distinguishing separate PD impulses from different types of PD sources while the parameters of these sources are changed. Three PD sources were built for signal generation: corona discharge, discharge in a void, and surface discharge. The reason for using separate PD impulses for classification is to develop a universal tool with the ability to recognize an insulation defects by analysing very few events in the insulation in a short range of time. Additionally, we found the optimal sample rates for the data acquisition for these network configurations. The necessity of signal filtering was also tested. The following configurations of a neural network were proposed: configuration for classification raw PD impulses; configuration for classification of PD impulses represented by power spectral density, for both filtered and unfiltered variants.

Impulse signals classification using one dimensional convolutional neural network

Department of Electrical Power Engineering and Environmental Engineering, Faculty of Electrical Engineering

Journal of Electrical Engineering

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

The main purpose of this work is to propose a modern one-dimensional convolutional neural network (1 D CNN) configurations for distinguishing separate PD...

Impulse signals classification using one dimensional convolutional neural network

Online veröffentlicht: 24. Dez. 2020

Seitenbereich: 397 - 405

Eingereicht: 22. Okt. 2020

DOI: https://doi.org/10.2478/jee-2020-0054

Schlüsselwörter
convolutional neural networks, one-dimensional convolutional neural network, partial discharge, signal analysis

© 2020 Mikhail Olkhovskiy et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Impulse signals classification using one dimensional convolutional neural network

Online veröffentlicht: 24. Dez. 2020

Seitenbereich: 397 - 405

Eingereicht: 22. Okt. 2020

DOI: https://doi.org/10.2478/jee-2020-0054

Schlüsselwörterconvolutional neural networks, one-dimensional convolutional neural network, partial discharge, signal analysis

© 2020 Mikhail Olkhovskiy et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Schlüsselwörter
convolutional neural networks, one-dimensional convolutional neural network, partial discharge, signal analysis