Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Random Convolutional Kernel Transform with Empirical Mode Decomposition for Medium Voltage Insulator Classification based on Ultrasound Sensor

Anne Carolina Rodrigues Klaar
* ORCID logo ,
Laio Oriel Seman
* ORCID logo ,
Viviana Cocco Mariani
* ORCID logo ,
Leandro dos Santos Coelho
*
Version 1 : Received: 19 January 2024 / Approved: 21 January 2024 / Online: 22 January 2024 (09:52:24 CET)

A peer-reviewed article of this Preprint also exists.

Klaar, A.C.R.; Seman, L.O.; Mariani, V.C.; Coelho, L.S. Random Convolutional Kernel Transform with Empirical Mode Decomposition for Classification of Insulators from Power Grid. Sensors 2024, 24, 1113. Klaar, A.C.R.; Seman, L.O.; Mariani, V.C.; Coelho, L.S. Random Convolutional Kernel Transform with Empirical Mode Decomposition for Classification of Insulators from Power Grid. Sensors 2024, 24, 1113.

Abstract

The electrical energy supply relies on the satisfactory operation of insulators. The ultrasound recorded from insulators in different conditions has a time series output, which can be used to classify faulty insulators. The random convolutional kernel transform (Rocket) algorithms use convolutional filters to extract various features from time series data. This paper proposes the combination of Rocket algorithms, machine learning classifiers, and empirical mode decomposition (EMD) methods, such as complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), empirical wavelet transform (EWT), and variational mode decomposition (VMD). The results show that the EMD methods combined with MiniRocket, significantly improve the accuracy of logistic regression in insulator fault diagnosis. The proposed strategy achieves respectively an accuracy of 0.992 using CEEMDAN, 0.995 with EWT, and 0.980 with VMD. These results highlight the potential of incorporating EMD methods in insulator failure detection models to enhance the safety and dependability of power systems.

Keywords

Electric Power System; Empirical Mode Decomposition; Rocket Algorithm; Time Series Classification.

Subject

Engineering, Electrical and Electronic Engineering

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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