Identification of corona discharges based on wavelet scalogram images with deep convolutional neural networks

Halil Ibrahim Uckol, Suat Ilhan*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

Abstract

This paper presents a new wavelet-based approach to automate the identification of positive and negative DC corona discharge current pulses. Two electrode systems with variable gap spacings formed corona discharges, and two commercial sensors (a high-frequency current transformer (HFCT) and a shunt resistor) captured transient corona discharge currents. The proposed method employs a continuous wavelet transform to generate time-frequency representations of corona discharge pulse currents, called scalogram images. The effects of sampling interval, data acquisition time, data shifting, and external noise components in the signals on the scalogram images were examined. The well-known pre-trained convolutional neural network models, AlexNet, MobileNet, and ShuffleNet, were tailored, and their ensemble structure was generated to discriminate scalogram images of discharge pulses. A framework was constructed to increase the generalization ability of the study. The results demonstrate that the scalogram images are robust candidates for corona discharge identification.

Original languageEnglish
Article number109712
JournalElectric Power Systems Research
Volume224
DOIs
Publication statusPublished - Nov 2023

Bibliographical note

Publisher Copyright:
© 2023 Elsevier B.V.

Funding

This work was supported by the Research Fund of the Istanbul Technical University Research Support Program (ITU-BAP) for Ph.D. candidates. (Project Number: MDK-2022-44154). We would like to express our special thanks to Electrical Eng. Harun Gökhan from Bilim Electrical Company for the preparation of the electrodes. This work was supported by the Research Fund of the Istanbul Technical University Research Support Program (ITU-BAP) for Ph.D. candidates. (Project Number: MDK-2022-44154 ). We would like to express our special thanks to Electrical Eng. Harun Gökhan from Bilim Electrical Company for the preparation of the electrodes.

FundersFunder number
Bilim Electrical Company
Istanbul Teknik Üniversitesi
Bilimsel Araştırma Projeleri Birimi, İstanbul Teknik ÜniversitesiMDK-2022-44154

    Keywords

    • Corona discharge
    • Deep learning
    • HFCT
    • Scalogram
    • Shunt resistor
    • Wavelet transform

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